Patent Publication Number: US-2022232816-A1

Title: Predictive weed map and material application machine control

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation-in-part of and claims priority of U.S. patent application Ser. No. 17/067,383, filed Oct. 9, 2020, which is a continuation-in-part of and claims priority of U.S. patent application Ser. No. 16/783,475, filed Feb. 6, 2020, and Ser. No. 16/783,511, filed Feb. 6, 2020, the present application is a continuation-in-part of and claims priority of U.S. patent application Ser. No. 17/066,444, filed Oct. 8, 2020, which is a continuation-in part of and claims priority of U.S. patent application Ser. No. 16/783,475, filed Feb. 6, 2020, and Ser. No. 16/783,511, filed Feb. 6, 2020. The contents of all of the above applications are hereby incorporated by reference in their entirety. 
    
    
     FIELD OF THE DESCRIPTION 
     The present description relates to agriculture. More specifically, the present description relates to agricultural machines and operations which deliver material to a worksite. 
     BACKGROUND 
     There are a wide variety of different types of agricultural machines. Some agricultural machines apply material, such as fluid or solid material, to a field. For instance, some machines, such as sprayers or dry spreaders, can deliver fluid or solid material, such as fertilizer, herbicide, pesticide, as well as variety of other materials to a field. Some machines, such as agricultural planting machines, can deliver material such as seeds, as well as other material, such as liquid or solid material, for instance, fertilizer. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A predictive map is obtained by an agricultural material application system. The predictive map maps predictive weed values at different geographic locations in a field. A geographic position sensor detects a geographic locations of an agricultural material application machine at the field. A control system generates a control signal to control the agricultural material application machine based on the geographic locations of the agricultural material application machine and the predictive map. 
     Example 1 is an agricultural material application system comprising: 
     a geographic position sensor that detects a geographic location of a mobile material application machine at a field; 
     a control system that: 
     receives a predictive map that maps predictive weed values to different geographic locations in the field; and 
     generates a control signal to control a controllable subsystem of the mobile material application machine based on the geographic location of the mobile material application machine and the predictive map. 
     Example 2 is the agricultural material application system of any or all previous examples and further comprising: 
     an in-situ sensor that detects a weed value corresponding to a geographic location; 
     a predictive model generator that: 
     receives an information map that maps values of a characteristic corresponding to different geographic locations in the field; and 
     generates a predictive model indicative of a relationship between values of the characteristic and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map corresponding to the geographic location; and 
     a predictive map generator that generates, as the predictive map, a functional predictive map of the field that maps predictive weed values to the different geographic locations in the field based on the values of the characteristic in the information map and based on the predictive model. 
     Example 3 is the agricultural material application system of any or all previous examples wherein the weed value is indicative of one or more of weed presence, weed type, weed size, and weed intensity. 
     Example 4 is the agricultural material application system of any or all previous examples, wherein the information map comprises a vegetative index map that maps vegetative index values to the different geographic locations in the field; 
     wherein the predictive model generator generates, as the predictive model, a predictive weed model that models a relationship between vegetative index values and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a vegetative index value in the vegetative index map at the geographic location to which the detected weed values corresponds, the predictive weed model being configured to receive a vegetative index value as a model input and generate a predictive weed value as a model output; and 
     wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the vegetative index values in the information map and based on the predictive weed model. 
     Example 5 is the agricultural material application system of any or all previous examples, wherein the information map comprises an optical map that maps optical characteristic values to the different geographic locations in the field; 
     wherein the predictive model generator generates, as the predictive model, a predictive weed model that models a relationship between optical characteristic values and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and an optical characteristic value in the optical map at the geographic location to which the detected weed values corresponds, the predictive weed model being configured to receive an optical characteristic value as a model input and generate a predictive weed value as a model output; and 
     wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the optical characteristic values in the information map and based on the predictive weed model. 
     Example 6 is the agricultural material application system of any or all previous examples, wherein the information map comprises a weed map that maps weed values to the different geographic locations in the field; 
     wherein the predictive model generator generates, as the predictive model, a predictive weed model that models a relationship between weed values and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a weed value in the weed map at the geographic location to which the detected weed values corresponds, the predictive weed model being configured to receive a weed value as a model input and generate a predictive weed value as a model output; and 
     wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the weed values in the information map and based on the predictive weed model. 
     Example 7 is the agricultural material application system of any or all previous examples, wherein the information map comprises two or more information maps, each of the two or more information maps mapping values of a respective characteristic to the different geographic locations in the field; 
     wherein the predictive model generator generates, as the predictive model, a predictive weed model indicative of a relationship between values of the two or more respective characteristics and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and values of the two or more respective characteristics in the two or more information maps corresponding to the geographic location, the predictive weed model being configured to receives a value of each of the two or more respective characteristics as model inputs and generate a predictive weed value as a model output; and 
     wherein the predictive map generator generates, as the functional predictive map, a functional predictive weed map that maps predictive weed values to the different geographic locations in the field based on the values of the two more respective characteristics in the two or more information maps and the predictive weed model. 
     Example 8 is the agricultural material application system of any or all previous examples, wherein the controllable subsystem comprises a material application actuator and wherein the control signal controls the material application actuator to increase an amount of material applied by the material application machine based on the functional predictive weed map. 
     Example 9 is the agricultural material application system of any or all previous examples, wherein the controllable subsystem comprises a material application actuator and wherein the control signal controls the material application actuator to decrease an amount of material applied by the material application machine based on the functional predictive weed map. 
     Example 10 is the agricultural material application system of any or all previous examples, wherein the controllable subsystem comprises a material application actuator and wherein the control signal controls the material application actuator to deactivate or activate a component of the material application machine based on the functional predictive weed map. 
     Example 11 is a method of controlling a mobile agricultural material application machine comprising: 
     receiving a predictive map of a field that maps predictive weed values corresponding to different geographic locations in the field; 
     detecting a geographic location of the mobile agricultural material application machine at the field; 
     controlling a controllable subsystem of the mobile agricultural material application machine based on the geographic location of the mobile agricultural material application machine and the predictive map. 
     Example 12 is the method of any or all previous examples and further comprising: 
     receiving an information map that maps values of a characteristic to different geographic locations in a field; 
     obtaining in-situ sensor data indicative of a weed value corresponding to a geographic location at the field; 
     generating a predictive weed model indicative of a relationship between values of the characteristic and weed values; and 
     generating, as the predictive map, a functional predictive weed map of the field, that maps predictive weed values to the different geographic locations in the field based on the values of the characteristic in the information map and the predictive model. 
     Example 13 is the method of any or all previous examples, wherein controlling a controllable subsystem comprises controlling a material application actuator of the mobile agricultural material application machine based on the geographic location of the mobile agricultural material application machine and the functional predictive weed map. 
     Example 14 is the method of any or all previous examples, wherein controlling the material application actuator comprises controlling the material application actuator of the mobile agricultural material application machine to adjust a rate at which material is applied to the field based on the geographic location of the mobile agricultural material application machine and the functional predictive weed map. 
     Example 15 is the method of any or all previous examples, wherein controlling the material application actuator comprises controlling the material application actuator of the mobile agricultural material application machine to activate or deactivate a component of the mobile agricultural material application machine based on the geographic location of the mobile agricultural material application machine and the functional predictive weed map. 
     Example 16 is a mobile agricultural material application machine, comprising: 
     a geographic position sensor that detects a geographic location of the mobile agricultural material application machine at a field; and 
     a control system that: 
     receives a predictive map that maps predictive weed values to different geographic locations in the field; and 
     generates a control signal based on the geographic location of the mobile agricultural material application machine at the field and the predictive map. 
     Example 17 is the mobile agricultural material application machine of any or all previous examples and further comprising: 
     a communication system that receives an information map that maps values of a characteristic to different geographic locations in the field; 
     an in-situ sensor that detects a weed value corresponding to the geographic location; 
     a predictive model generator that generates a predictive weed model indicative of a relationship between values of the characteristic and weed values based on the weed value detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map at the geographic location; and 
     a predictive map generator that generates, as the predictive map, a functional predictive weed map of the field that maps predictive weed values to the different geographic locations in the field based on the values of the characteristic in the information map at those different geographic locations and based on the predictive weed model. 
     Example 18 is the mobile agricultural machine of any or all previous examples, wherein the control system generates the control signal to control a controllable subsystem of the mobile agricultural material application machine. 
     Example 19 is the mobile agricultural material application machine of any or all previous examples, wherein the control system generates the control signal to control an actuator that is controllably actuatable to adjust a rate at which material is applied to the field. 
     Example 20 is the mobile agricultural material application machine of any or all previous examples, wherein the control system generates the control signal to control an actuator to activate or deactivate a component of the mobile agricultural material application machine. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a partial pictorial, partial block diagram showing one example of a mobile agricultural material application machine as a mobile agricultural planting machine. 
         FIG. 2  is a side view showing one example of a row unit of the mobile agricultural planting machine shown in  FIG. 1   
         FIG. 3  is a side view showing another example of a row unit of the mobile agricultural planting machine shown in  FIG. 1 . 
         FIG. 4  shows an example of a seed metering system. 
         FIG. 5  shows an example of a seed delivery system that can be used with a seed metering system. 
         FIG. 6  shows another example of a seed delivery system that can be used with a seed metering system. 
         FIG. 7  is a partial pictorial, partial block diagram showing one example of a mobile agricultural material application machine as a mobile agricultural sprayer. 
         FIG. 8  is a partial pictorial, partial block diagram showing one example of a mobile agricultural material application machine as a mobile agricultural sprayer. 
         FIG. 9  shows an example of a material delivery machine. 
         FIG. 10  is a block diagram showing some portions of an agricultural material application system, including a mobile agricultural material application machine, in more detail, according to some examples of the present disclosure. 
         FIG. 11  is a block diagram showing one example of a predictive model generator and predictive map generator. 
         FIG. 12  is a block diagram showing one example of a predictive model generator and predictive map generator. 
         FIG. 13  is a block diagram showing one example of a predictive model generator and a predictive map generator. 
         FIG. 14  is a block diagram showing one example of a predictive model generator and a predictive map generator. 
         FIGS. 15A-15B  (collectively referred to herein as  FIG. 15 ) show a flow diagram illustrating one example of operation of an agricultural material application system in generating a map. 
         FIG. 16  is a block diagram showing one example of a logistics system in more detail. 
         FIG. 17  is a flow diagram illustrating one example of operation of an agricultural material application system in controlling a material application operation. 
         FIG. 18  is a block diagram showing one example of a mobile material application machine in communication with a remote server environment. 
         FIGS. 19-21  show examples of mobile devices that can be used in an agricultural material application system. 
         FIG. 22  is a block diagram showing one example of a computing environment that can be used in an agricultural material application system. 
     
    
    
     DETAILED DESCRIPTION 
     For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure. 
     In some examples, the present description relates to using in-situ data taken concurrently with an operation, such as an agricultural material application operation, in combination with prior or predicted data, such as prior or predicted data represented in a map, to generate a predictive model and a predictive map. In some examples, the predictive map can be used to control a mobile machine, such as a mobile agricultural material application machine or a material delivery machine, or both. 
     During an agricultural material application operation material, such as seed, fertilizer, herbicide, pesticide, etc., is delivered to the field. The application of material can be controlled, such as by an operator or user, or by an automated control system, or both. It may be desirable to controllably (e.g., variably) apply material, based on the characteristics of the field. For example, it may be desirable to vary the amount of material applied at a given locations, based on the nutrient levels at those locations. For instance, some locations of the field may have adequate or near adequate nutrient levels, such that no fertilizer or relatively less fertilizer need be applied. In other examples, some locations of the field may have nutrient levels that require the application of more material than expected. In other examples, it may be desirable to vary the amount of material applied at given locations, based on the weed characteristics at those locations. For instance, herbicide may not be required at given location due to lack of weeds at those location, or additional herbicide may be needed where the weeds are particularly intense. 
     Applying material as needed based on the field conditions at the time of the operation, as opposed to a blanket application or a prescribed application determined ahead of the operation in the field, may save cost, may reduce environmental impact, as well as result in more effective material use, which may result in higher yields. 
     Some current systems may include sensors that detect characteristics indicative of nutrient levels of the field which can be used in the control of material application. However, such systems often include latency, such as due to the sensor feedback delay or due to the machine control delay, which may result in suboptimal material application. 
     The present description thus relates to a system that can predict characteristic values, such as nutrient values or weed values, or both, at different locations across the worksite, such that a mobile agricultural material application machine can be proactively controlled. 
     In some examples, it may be desirable to know when a material application machine will run out of material. As the operator or user or control system may vary the application throughout the operation it can be difficult to know, a priori, where the machine will run out of material. 
     Knowing when and where the machine will run out of material can be useful in planning logistics of the material application operation, such as scheduling or meeting a material delivery vehicle. Efficient scheduling can reduce downtime, as well as provide various other benefits. 
     The present description thus relates to a system that can predict material consumption values at different locations across the worksite, such that the material application operation can be proactively controlled. 
     In one example, the present description relates to obtaining an information map, such as a soil property map. A soil property map illustratively maps soil property values (which may be indicative of soil type, soil moisture, soil structure, soil salinity, soil pH, soil organic matter, soil contaminant concentration, soil nutrient levels, as well as various other soil properties) across different geographic locations in a field of interest. The soil property maps thus provide geo-referenced soil properties across a field of interest. Soil type can refer to taxonomic units in soil science, wherein each soil type includes defined sets of shared properties. Soil types can include, for example, sandy soil, clay soil, silt soil, peat soil, chalk soil, loam soil, and various other soil types. Soil moisture can refer to the amount of water that is held or otherwise contained in the soil. Soil moisture can also be referred to as soil wetness. Soil structure can refer to the arrangement of solid parts of the soil and the pore space located between the solid parts of the soil. Soil structure can include the way in which individual particles, such as individual particles of sand, silt, and clay, are assembled. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and form (types of aggregates), as well as a variety of other descriptions. Soil salinity refers to the amount (e.g., concentration) of salt in the soil. Soil nutrient levels refers to the amounts (e.g., concentrations) of various nutrients of the soil, such as nitrogen. These are merely examples. Various other characteristics and properties of the soil can be mapped as soil property values on a soil property map. The soil property map can be derived in a variety of ways, such as from sensor readings during previous operations at the field of interest, from surveys of the field, such as soil sampling surveys, as well as surveys by aerial machines (e.g., satellites, drones, etc.) that includes sensors that capture sensor information of the field. The soil property map can be generated based on data from remote sources, such as third-party service providers or government agencies, for instance, the USDA Natural Resources Conservation Service (NRCS), the United States Geological Survey (USGS), as well as from various other remote sources. These are merely some examples. The soil property map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a yield map. A yield map illustratively maps yield values across different geographic locations in a field of interest. The yield map may be based on sensor readings taken during an aerial survey of the field of interest or during a previous operation on the field of interest, or derived from other values, such as vegetative index values. In some examples, the yield map may be a historical yield map that includes historical yield values from a previous harvesting operation, such as the harvesting operation from a prior year or a prior season. These are merely some examples. The yield map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a residue map. A residue map illustratively maps residue values (which may be indicative of residue amount and residue distribution) across different geographic locations in a field of interest. Residue illustratively refers to vegetation residue, such as remaining vegetation material at the field of interest, such as remaining crop material, as well as material of other plants, such as weeds. The residue map may be derived from sensor readings during a previous operation at the field. For example, the machine performing the previous operation may be outfitted with sensors that detect residue values at different geographic locations in the field. The residue map may be derived from sensor readings from sensors on aerial machine (e.g., satellites, drones, etc.) that survey the field of interest. The sensors may read one or more bands of electromagnetic radiation reflected from the residue material at the field. These are merely some examples. The residue map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a constituents map. A constituents map illustratively maps constituent values (which may be indicative of constituent levels (e.g., concentrations) of constituents, such as, sugar, starch, fiber, water/moisture, etc., of crop plants) across different geographic locations in a field of interest. The constituent map may be derived from sensor readings during a previous operation at the field. The constituent map may be derived from sensor readings from sensors on aerial machine (e.g., satellites, drones, etc.) that survey the field of interest. The sensors may read one or more bands of electromagnetic radiation reflected from the residue material at the field. These are merely some examples. The constituent map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a topographic map. A topographic map illustratively maps topographic characteristic values across different geographic locations in a field of interest, such as elevations of the ground across different geographic locations in a field of interest. Since ground slope is indicative of a change in elevation, having two or more elevation values allows for calculation of slope across the areas having known elevation values. Greater granularity of slope can be accomplished by having more areas with known elevation values. As an agricultural machine travels across the terrain in known directions, the pitch and roll of the agricultural machine can be determined based on the slope of the ground (i.e., areas of changing elevation). Topographic characteristics, when referred to below, can include, but are not limited to, the elevation, slope (e.g., including the machine orientation relative to the slope), and ground profile (e.g., roughness). The topographic map can be derived from sensor readings taken during a previous operation on the field of interest or from an aerial survey of the field (such as a plane, drone, or satellite equipped with lidar or other distance measuring devices). In some examples, the topographic map can be obtained from third parties. These are merely some examples. The topographic map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a seeding map. A seeding map illustratively maps values of seeding characteristics (e.g., seed location, seed spacing, seed population, seed genotype, etc.) across different geographic locations in a field of interest. The seeding map may be derived from control signals used by a planting machine when planting seeds or from sensors on the planting machine that confirm that a seed was metered or planted. The seeding map can be generated based on a prescriptive seeding map that was used in the control of a planting operation. These are merely some examples. The seeding map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map such as a vegetative index map. A vegetative index map illustratively maps georeferenced vegetative index values (which may be indicative of vegetative growth or plant health) across different geographic locations in a field of interest. One example of a vegetive index includes a normalized difference vegetation index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetive index map be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. 
     Without limitations, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum. A vegetative index map can be used to identify the presence and location of vegetation. In some examples, these maps enable vegetation to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, including crop or other weeds. The sensor readings can be taken at various times, such as during satellite observation of the field of interest, a fly over operation (e.g., manned or unmanned aerial vehicles), sensor readings during a prior operation) at the field of interest, as well as during a human scouting operation. These are merely some examples. The vegetative index map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining a map, such as an optical map. An optical map illustratively maps electromagnetic radiation values (or optical characteristic values) across different geographic locations in a field of interest. Electromagnetic radiation values can be from across the electromagnetic spectrum. This disclosure uses electromagnetic radiation values from infrared, visible light and ultraviolet portions of the electromagnetic spectrum as examples only and other portions of the spectrum are also envisioned. An optical map may map datapoints by wavelength (e.g., a vegetative index). In other examples, an optical map identifies textures, patterns, color, shape, or other relations of data points. Textures, patterns, or other relations of data points can be indicative of presence or identification of vegetation on the field (e.g., crops, weeds, plant matter, such as residue, etc.). Additionally, or alternatively, an optical map may identify the presence of standing water or wet spots on the field. The optical map can be derived using satellite images, optical sensors on flying vehicles such as UAVS, or optical sensors on a ground-based system, such as another machine operating in the field prior to the current operation. In some examples, optical maps may map three-dimensional values as well such as vegetation height when a stereo camera or lidar system is used to generate the map. These are merely some examples. The optical map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a weed map. A weed map illustratively maps weed values (which may be indicative of weed location, weed presence, weed type, and weed intensity (e.g., density)) across different geographic locations in a field of interest. The weed map may be derived from sensor readings during a previous operation at the field. The weed map may be derived from sensor readings from sensors on aerial machine (e.g., satellites, drones, etc.) that survey the field of interest. The sensors may read one or more bands of electromagnetic radiation reflected from the weed material at the field. The weed map may be derived from various other data, such as optical characteristic data or vegetative index data of the field of interest. These are merely some examples. The weed map can be generated in a variety of other ways. 
     In one example, the present description relates to obtaining an information map, such as a contamination map. A contamination map illustratively maps contamination values (which may be indicative of pest presence, pest type, pest intensity (e.g., population), disease presence, disease type, and disease intensity (e.g., prevalence)) across different geographic locations in a field of interest. The contamination map may be derived from sensor readings during a previous operation at the field. The contamination map may be derived from sensor readings from sensors on aerial machine (e.g., satellites, drones, etc.) that survey the field of interest. The sensors may read one or more bands of electromagnetic radiation reflected from the vegetation material (or from the contaminants) at the field. The contamination map may be derived from various other data, such as optical characteristic data or vegetative index data of the field of interest. These are merely some examples. The contamination map can be generated in a variety of other ways. 
     In other examples, one or more other types of information maps can be obtained. The various other types of information maps illustratively map values of various other characteristics across different geographic locations in a field of interest. 
     The present discussion proceeds, in some examples, with respect to systems that obtain one or more information maps of a worksite (e.g., field) and also use an in-situ sensor to detect a characteristic. The systems generate a model that models a relationship between the values on the one or more obtained maps and the output values from the in-situ sensor. The model is used to generate a predictive map that predicts, for example, values of the characteristic detected by the in-situ sensor to different geographic locations in the worksite. The predictive map, generated during an operation, can be presented to an operator or other user or can be used in automatically controlling a mobile machine, such as a mobile agricultural material application machine or a material delivery machine, or both, during a material application operation. 
       FIG. 1  shows one example of a mobile agricultural material application machine  100  as a mobile agricultural planting machine  100 - 1  that includes a towing vehicle  10  and a planting implement  101 .  FIG. 1  also illustrates that mobile agricultural planting machine  100 - 1  can include one or more in-situ sensors  308 , some of which are shown in  FIG. 1  as well as below. For example,  FIG. 1  shows that planting machine  100 - 1  can include one or more fill level sensors  107  that detect a fill level of material in tanks  107 . Fill level sensors can include float gauges, weight sensors that detect a weight of material in tanks  107 , emitter sensors that detect a level to which the material is filled, as well as various other types of sensors. Various components of agricultural planting machine  100 - 1  can be on individual parts of planting implement  101 , towing vehicle  10 , or can be distributed in various ways across both the planting implement  101  and towing vehicle  10 .  FIG. 1 , also illustrates that towing vehicle can include, among other things, operator interface mechanisms  318  which can be used by an operator to manipulate and control agricultural planting machine  100 - 1 . 
     As shown, planting implement  101  is a row crop planter. In other examples, other types of planting machines can be used, such as air seeders. Planting implement  101  illustratively includes a toolbar  102  that is part of a frame  104 .  FIG. 1  also shows that a plurality of planting row units  106  are mounted to the toolbar  102 . Planting implement  101  can be towed behind towing vehicle  10 , such as a tractor.  FIG. 1  shows that material, such as seed, fertilizer, etc. can be stored in a tank  107  and pumped, using one or more pumps  115 , through supply lines to the row units  106 . The seed, fertilizer, etc., can also be stored on the row units  106  themselves. As shown in the illustrated example of  FIG. 1 , each row unit can include a respective controller(s)  135  which can be used to control operating parameters of each row unit  106 . In other examples, centralized controllers can control the row units  106 . 
       FIG. 2  is a side view showing one example of a row unit  106 . In the example shown in  FIG. 2 , row unit  106  illustratively includes a chemical tank  110  and a seed storage tank  112 . It also illustratively includes a furrow opener  114  (e.g., opening disks) that opens a furrow in field  107 , a set of gauge wheels  116 , and a furrow closer  118  (e.g., closing wheels) that close furrow  162 . Seeds from tank  112  are fed by gravity into a seed meter  124 . The seed meter  124  controls the rate which seeds are dropped into a seed tube  120  or other seed delivery system, such as a brush belt or flighted brush belt (both shown below) from seed storage tank  112 . The seeds can be sensed by a seed sensor  122 . An actuator, such as motor, can be used to control the speed of seed meter  124  to control the rate at which seeds are delivered to the furrow  162 . 
     Some parts of the row unit  106  will now be discussed in more detail. First, it will be noted that there are different types of seed meters  124 , and the one that is shown is shown for the sake of example only and is described in greater detail below. For instance, in one example, each row unit  106  need not have its own seed meter. Instead, metering or other singulation or seed dividing techniques can be performed at a central location, for groups of row units  106 . The metering systems can include rotatable disks, rotatable concave or bowl-shaped devices, among others. The seed delivery system can be a gravity drop system (such as seed tube  120  shown in  FIG. 2 ) in which seeds are dropped through the seed tube  120  and fall (via gravitational force) through the seed tube and out the outlet end  121  into the furrow (or seed trench)  162 . Other types of seed delivery systems are assistive systems, in that they do not simply rely on gravity to move the seed from the metering system into the ground. Instead, such systems actively capture the seeds from the seed meter and physically move the seeds from the meter to a lower opening where the exit into the ground or trench. Some examples of these assistive systems are described in greater detail below. 
       FIG. 2  also shows an actuator  109  in a plurality of possible locations ( 109 A,  109 B,  109 C,  109 D, and  109 E). Actuator  109  (e.g., pump) pumps material (such as fertilizer) from tank  107  through supply line  111  so the material can be dispensed in or near the furrows. In such an example, a controller can generate a control signal to control the actuation of pump  109 . In other examples, actuators  109  are controllable valves and one or more pumps  115  pump the material from tank(s)  107  to actuators  109  through supply line  111 . In other examples, actuators control the delivery of material from other tanks, such as tank  110 . In such an example, a controller controls the actuator by generating valve or actuator control signals. The control signal for each valve or actuator  109  can, in one example, be a pulse width modulated control signal. The flow rate through the corresponding actuator  109  can be based on the duty cycle of the control signal (which controls the amount of time the valve is open and closed). It can be based on multiple duty cycles of multiple valves or based on other criteria. Further, the material can be applied in varying rates on a per-seed or per-plant basis. For example, material may be applied at one rate when it is being applied at a location spaced from a seed location and at a second, higher, rate when it is being applied closer to the seed location. In other examples, the material may be applied based on various characteristics of the field, such as the nutrient levels, weed characteristics, as well as various other characteristics. These are examples only. 
     Additionally,  FIG. 2  shows a flow rate sensor  199  in a plurality of possible locations ( 199 A,  199 B,  199 C,  199 D, and  199 E). Flow rate sensor  199  can detect a volumetric flow rate of material flowing through supply line  111 . 
     Additionally,  FIG. 2  shows that row unit  106  can include one or more fill level sensors, such as a fill level sensor  177  and a fill level sensor  178 . Fill level sensor  177  illustratively detects a fill level of tank  110 . Fill level sensor  178  illustratively detects a fill level of tank  112 . Fill level sensors  177  and  178  can include float gauges, weight sensors that detect a weight of material in tanks  110  and  112 , emitter sensors that detect a level to which the material is filled, as well as various other types of sensors. 
     In the example of shown in  FIG. 2 , material is passed, e.g., pumped or otherwise forced, through supply line  111  to an inlet end of actuator  109 . Actuator  109  is controlled by a controller (e.g.,  135 ) to allow the liquid to pass from the inlet end of actuator  109  to an outlet end. As material passes through actuator  109 , it travels through an application assembly  169  from a proximal end (which is attached to an outlet end of actuator  109 ) to a distal tip (or application tip)  119 , where the liquid is discharged into a trench, or proximate a trench or furrow  162  (e.g., on the surface of field  107  next to trench or furrow  162  but not in trench or furrow  162 ), opened by disc opener  114 . 
     A downforce generator or actuator  126  is mounted on a coupling assembly  128  that couples row unit  106  to toolbar  102 . Downforce actuator  126  can be a hydraulic actuator, a pneumatic actuator, a spring-based mechanical actuator or a wide variety of other actuators. In the example shown in  FIG. 2 , a rod  130  is coupled to a parallel linkage  132  and is used to exert an additional downforce (in the direction indicated by arrow  134 ) on row unit  106 . The total downforce (which includes the force indicated by arrow  134  exerted by actuator  126 , plus the force due to gravity acting on the row unit  106 , and indicated by arrow  136 ) is offset by upwardly directed forces acting on closing wheels  118  (from ground  138  and indicated by arrow  140 ) and double disk opener  114  (again from ground  138  and indicated by arrow  142 ). The remaining force (the sum of the force vectors indicated by arrows  134  and  136 , minus the force indicated by arrows  140  and  142 ) and the force on any other ground engaging component on the row unit (not shown), is the differential force indicated by arrow  146 . The differential force may also be referred to herein as downforce margin. The force indicated by arrow  146  acts on the gauge wheels  116 . This load can be sensed by a gauge wheel load sensor  159  which may located anywhere on row unit  106  where it can sense that load. It can also be placed where may not sense the load directly, but a characteristic indicative of that load. For example, it can be disposed near a set of gauge wheel control arms (or gauge wheel arm)  148  that movably mount gauge wheels to shank  152  and control an offset between gauge wheels  116  and the disks in double disk opener  114  to control planting depth. Percent ground contact is a measure of a percentage of time that the load (downforce margin) on the gauge wheels  116  is zero (indicating that the gauge wheels are out of contact with the ground). The percent ground contact is calculated on the basis of sensor data provided by the gauge wheel load sensor  159 . 
     In addition, there may be other separate and controllable downforce actuators, such as one or more of a closing wheel downforce actuator  153  that controls the downforce exerted on closing wheels  118 . Closing wheel downforce actuator  153  can be a hydraulic actuator, a pneumatic actuator, a spring-based mechanical actuator or a wide variety of other actuators. The downforce exerted by closing wheel downforce actuator  153  is represented by arrow  137 . It will be understood that each row unit  106  can include the various components described with reference to  FIGS. 2-6 . 
     In the illustrated example, arms (or gauge wheel arms)  148  illustratively abut a mechanical stop (or arm contact member or wedge)  150 . The position of mechanical stop  150  relative to shank  152  can be set by a planting depth actuator assembly  154 . Control arms  148  illustratively pivot around pivot point  156  so that, as planting depth actuator assembly  154  actuates to change the position of mechanical stop  150 , the relative position of gauge wheels  116 , relative to the double disk opener  114 , changes, to change the depth at which seeds are planted. 
     In operation, row unit  106  travels generally in the direction indicated by arrow  160 . The double disk opener  114  opens the furrow  162  in the soil  138 , and the depth of the furrow  162  is set by planting depth actuator assembly  154 , which, itself, controls the offset between the lowest parts of gauge wheels  116  and disk opener  114 . Seeds are dropped through seed tube  120  into the furrow  162  and closing wheels  118  close the soil. 
     As the seeds are dropped through seed tube  120 , they can be sensed by seed sensor  122 . Some examples of seed sensor  122  are an optical sensor or a reflective sensor, and can include a radiation transmitter and a receiver. The transmitter emits electromagnetic radiation and the receiver the detects the radiation and generates a signal indicative of the presences or absences of a seed adjacent to the sensor. These are just some examples of seed sensors. Row unit controller  335  may control the actuators  109  and/or pumps  115  based on the seed sensor signal to controllably apply material relative to the seed locations in the furrow  162 . 
     Also, as shown in  FIG. 2 , row unit  106  can include, as in-situ sensors  308 , one or more observation sensor systems  151 . Observation sensor systems  151  may include one or more sensors that detect one or more characteristics such as soil nutrient levels, weed characteristics, as well as various other characteristics. In one example, an observation sensor system  151 , such as the observation sensor system  151  disposed between opener  114  and closer  118  can detect characteristic of the furrow as well as of the field proximate the furrow. Observation sensor systems  151  may include one or more of an imaging system (e.g., stereo or mono camera), optical sensors, radar, lidar, ultrasonic sensors, infrared or thermal sensors, as well as a variety of other sensors. In some examples, an observation sensor system  151  may detects seeds in furrow  162 . Planting implement  101  can also include an observation sensor system  151  disposed to observe in front of opener  114 , such as the observation sensor system  151  shown mounted to toolbar  102 . In other examples, observation sensor systems  151  can be mounted to various other locations of agricultural planting machine  100 - 1 , such as various other locations on planting implement  101  or towing vehicle  10 , or both. 
       FIG. 3  is similar to  FIG. 2 , and similar items are similarly numbered. However, instead of the seed delivery system being a seed tube  120  which relies on gravity to move the seed to the furrow  162 , the seed delivery system shown in  FIG. 4  is an assistive seed delivery system  166 . Assistive seed delivery system  166  also illustratively has a seed sensor  122  disposed therein. Assistive seed delivery system  166  captures the seeds as they leave seed meter  124  and moves them in a direction indicated by arrow  168  toward furrow  162 . System  166  has an outlet end  170  where the seeds exit system  166  into furrow  162  where they again reach their final seed position. System  166  may driven at variable speeds by an actuator, such as a variable motor, which can be controlled by a controller (e.g.,  135 ). The controller may control the speed of system  166  based on various characteristics, such as nutrient levels, weed characteristics, etc. The controller may control the actuator  109  to dispense material based on the seed sensor signal from seed sensor  122  as well as the speed at which system  166  is driven. The controller may control the actuator  109  based on various other characteristics, such as nutrient levels, weed characteristics, etc. 
       FIG. 3  also shows that row unit  106  can include a sensor  170  that interacts with the soil to detect various characteristics, such as nutrient levels of the soil. For instance, sensor  172  can be in the form of a probe that detects nutrient levels of the soil (such as the amount, or concentration, of various nutrients such as nitrogen, phosphorus, potassium, organic matter, etc.). In another example, sensor  172  can be in the form of an electromagnetic sensor that detects the capability of the soil to conduct or accumulate electrical charge, such as a capacitive sensor. 
       FIG. 4  shows one example of a rotatable mechanism that can be used as part of the seed metering system (or seed meter)  124 . The rotatable mechanism includes a rotatable disc, or concave element,  179 . Concave element  179  has a cover (not shown) and is rotatably mounted relative to the frame of row unit  106 . Rotatable concave element  179  is driven by an actuator, such as a motor (not shown in  FIG. 4 ), and has a plurality of projections or tabs  182  that are closely proximate corresponding apertures  184 . A seed pool  186  is disposed generally in a lower portions of an enclosure formed by rotating concave element  179  and its corresponding cover. Rotatable concave element  179  is rotatably driven by its motor (such as an electric motor, a pneumatic motor, a hydraulic motor, etc.) for rotation generally in the direction indicated by arrow  188 , about a hub. A pressure differential is introduced into the interior of the metering mechanism so that the pressure differential influences seeds from seed pool  186  to be drawn to apertures  184 . For instance, a vacuum can be applied to draw the seeds from seed pool  186  so that they come to rest in apertures  184 , where the vacuum holds them in place. Alternatively, a positive pressure can be introduced into the interior of the metering mechanism to create a pressure differential across apertures  184  to perform the same function. 
     Once a seed comes to rest in (or proximate) an aperture  184 , the vacuum or positive pressure differential acts to hold the seed within the aperture  184  such that the seed is carried upwardly generally in the direction indicated by arrow  188 , from seed pool  186 , to a seed discharge area  190 . It may happen that multiple seeds are residing in an individual seed cell. In that case, a set of brushes or other members  194  that are located closely adjacent the rotating seed cells tend to remove the multiple seeds so that only a single seed is carried by each individual cell. Additionally, a seed sensor  193  can also illustratively be mounted adjacent to rotating element  181 . Seed sensor  193  detects and generates a signal indicative of seed presence. 
     Once the seeds reach the seed discharge area  190 , the vacuum or other pressure differential is illustratively removed, and a positive seed removal wheel or knock-out wheel  191 , can act to remove the seed from the seed cell. Wheel  191  illustratively has a set of projections  195  that protrude at least partially into apertures  184  to actively dislodge the seed from those apertures. When the seed is dislodged (such as seed  171 ), it is illustratively moved by the seed tube  120 , seed delivery system  166  (some examples of which are shown above and below) to the furrow  162  in the ground. 
       FIG. 5  shows an example where the rotating element  181  is positioned so that its seed discharge area  190  is above, and closely proximate, assistive seed delivery system  166 . In the example shown in  FIG. 5 , assistive seed delivery system  166  includes a transport mechanism such as a belt  200  with a brush that is formed of distally extending bristles  202  attached to belt  200  that act as a receiver for the seeds. Belt  200  is mounted about pulleys  204  and  206 . One of pulleys  204  and  206  is illustratively a drive pulley while the other is illustratively an idler pulley. The drive pulley is illustratively rotatably driven by an actuator, such a conveyance motor (not shown in  FIG. 5 ), which can be an electric motor, a pneumatic motor, a hydraulic motor, etc. Belt  200  is driven generally in the direction indicated by arrow  208   
     Therefore, when seeds are moved by rotating element  181  to the seed discharge area  190 , where they are discharged from the seed cells in rotating element  181 , they are illustratively positioned within the bristles  202  by the projections  182  that push the seed into the bristles. Assistive seed delivery system  166  illustratively includes walls that form an enclosure around the bristles, so that, as the bristles move in the direction indicated by arrow  208 , the seeds are carried along with them from the seed discharge area  190  of the metering mechanism, to a discharge area  210  either at ground level, or below ground level within a trench or furrow  162  that is generated by the furrow opener  114  on the row unit  106 . 
     Additionally, a seed sensor  203  is also illustratively coupled to assistive seed delivery system  166 . As the seeds are moved in bristles  202  past sensor  203 , sensor  203  can detect the presence or absence of a seed. Some examples of seed sensor  203  includes an optical sensor or reflective sensor. 
       FIG. 6  is similar to  FIG. 5 , except that seed delivery system  166  is not formed by a belt with distally extending bristles. Instead, it is formed by a flighted belt (transport mechanism) in which a set of paddles  214  form individual chambers (or receivers), into which the seeds are dropped, from the seed discharge area  190  of the metering mechanism. The flighted belt moves the seeds from the seed discharge area  190  to the exit end  210  of the flighted belt, within the trench or furrow  162 . 
     There are a wide variety of other types of seed delivery systems as well, that include a transport mechanism and a receiver that receives a seed. For instance, they include dual belt delivery systems in which opposing belts receive, hold and move seeds to the furrow, a rotatable wheel that has sprockets which catch seeds from the metering system and move them to the furrow, multiple transport wheels that operate to transport the seed to the furrow, an auger, among others. 
       FIG. 7  shows one example of a mobile agricultural material application machine  100  as a mobile agricultural sprayer  100 - 2  that includes a towing vehicle  240  and a towed spraying implement  224 . Though, in other examples, such as the example shown in  FIG. 8 , sprayer  100 - 2  can be self-propelled. Additionally, other types of material application machines are contemplated, such as dry material spreaders.  FIG. 7  also shows that mobile agricultural sprayer  100 - 2  can include one or more in-situ sensors  308 , some of which are shown in  FIG. 7  as well as below. For example,  FIG. 7  shows that sprayer  100 - 2  can include one or more fill level sensors  271  that detect a fill level of material in tanks  234 . Fill level sensors can include float gauges, weight sensors that detect a weight of material in tanks  234 , emitter sensors that detect a level to which the material is filled, as well as various other types of sensors. 
     Sprayer  100 - 2  includes a spraying system having one or more tanks  234  containing one or more materials, such as a liquid materials (e.g., fertilizer, herbicide, pesticide, etc.), that is to be applied to field  207 . Tanks  234  are fluidically coupled to spray nozzles  230  by a delivery system comprising a set of conduits. One or more pumps are configured to pump the product from the tanks  234  through the conduits and through nozzles  230  to apply the product to the field  207 . In some examples, the fluid pumps are actuated by operation of one or more motors, such as electric motors, pneumatic motors, or hydraulic motors, that drive the pumps. 
     Spray nozzles  230  are coupled to, and spaced apart along, boom  220 . Boom  220  includes arms  221  and  222  which can articulate or pivot relative to a center frame  226 . Thus, arms  221  and  222  are movable between a storage or transport position and an extended or deployed position (shown in  FIG. 7 ). The boom  220 , including each arm  221  and  222 , can include multiple discrete and controllable sections which are supplied product from tanks  234  by the fluid pumps through a respective conduit of each section. 
     Each section can include a respective set of one or more spray nozzles  230 . Each section can be activated or deactivated through the actuation of a corresponding controllable actuator, such as a valve, for instance, a section can be deactivated, that is the section or the nozzles of the section, or both, are prevented from receiving fluid, by actuation of a controllable actuator that is upstream of the section or the nozzles, or both. In some examples, the nozzles of the section may each have an associated controllable actuator which can be actuated to activate or deactivate the nozzles. The application rate of product is the rate (volumetric rate) at which product is applied to the field over which sprayer  100 - 2  travels. The application rate corresponds to a volumetric flow rate of the product from the tanks  234  through the spray nozzles  230 . The volumetric flow rate is controlled by operation of actuators (such as fluid pumps or valves), such as by varying the speed of actuation of the pump with an associated motor or by controllably opening and closing a vale. In some examples, a controllable valve that corresponds to each section or to each nozzle, can be operable to reciprocate (e.g., pulse) between a closed state and an open state at variable frequency (e.g., pulse width modulation control) to control the rate at which the product is discharged from the set of spray nozzles  230  of the respective section or from the respective individual spray nozzle  230 . 
     In the example illustrated in  FIG. 7 , agricultural sprayer  100 - 2  comprises a towed implement  224  that carries the spraying system, and a towing or support machine  240  (illustratively a tractor, which may be similar to towing vehicle  10 ) that tows the towed spraying implement  224 . Towed implement  224  includes a set of ground engaging elements  243 , such as wheels or tracks. Towing machine  240  includes a set of ground engaging elements  244 , such as wheels or tracks. In the example illustrated, towing machine  240  includes an operator compartment or cab  228 , which can include a variety of different operator interface mechanisms (e.g.,  318  shown in  FIG. 10 ) for controlling sprayer  110 - 2 . 
     As will be shown in  FIG. 8 , an agricultural sprayer can be self-propelled. That is, rather than being towed by a towing machine, the machine that carries the spraying system also includes propulsion and steering systems. 
       FIG. 7  also illustrates that agricultural sprayer  100 - 2  can include one or more observation sensor systems  251 . Observation sensors systems  251  can be located at various locations on sprayer  100 - 2 , such as on towing vehicle  240  or implement  224 , or both. As illustrated, sprayer  100 - 2  includes an observation sensor system  251  on towing vehicle  240  as well as a plurality of observation sensor systems disposed on each of arm  221  and arm  222  of boom  220 . Observation sensor systems  251  can detect a variety of characteristic at the field, for example, soil nutrients, weed characteristics, as well as a variety of other characteristics. Observation sensor systems  251  may include one or more of an imaging system (e.g., stereo or mono camera), optical sensors, radar, lidar, ultrasonic sensors, infrared or thermal sensors, as well as a variety of other sensors. 
       FIG. 8  illustrates one example of an agricultural sprayer  100 - 3  that is self-propelled as an example mobile material application machine  100 . In  FIG. 8 , sprayer  100 - 3  has an on-board spraying system, including, among other things, one or more tanks  255  containing one or more materials (e.g., fertilizer, herbicide, pesticide, etc.) a boom  254 , that is carried on a machine frame  256  having an operator compartment  259 , and a set of ground engaging elements  260 , such as wheels or tracks. Operator compartment  259  can include a variety of different operator interface mechanisms (e.g.,  318  shown in  FIG. 10 ) for controlling agricultural sprayer  100 - 3 . Tank(s)  255  are fluidically coupled to spray nozzles  258  by a delivery system comprising a set of conduits. One or more fluid pumps are configured to pump the material from tank(s)  255  through the conduits and through nozzles  258  to apply the material to the field over which agricultural sprayer  100 - 3  travels. In some examples, the fluid pumps are actuated by operation of one or more motors, such as electric motors, pneumatic motors, or hydraulic motors, that drive the pumps. 
     Spray nozzles  258  are coupled to, and spaced apart along, boom  254 . Boom  254  includes arms  262  and  267  which can articulate or pivot relative to a center frame  266 . Thus, arms  262  and  264  are movable between a storage or transport position and an extended or deployed position (shown in  FIG. 8 ). The boom  254 , including each arm  262  and  267 , can include multiple discrete and controllable sections which are supplied product from tank(s)  255  by the fluid pump(s) through a respective conduit of each section. 
     Each section can include a respective set of one or more spray nozzles  258 . Each section can be activated or deactivated through the actuation of a corresponding controllable actuator, such as a valve, for instance, a section can be deactivated, that is the section or the nozzles of the section, or both, are prevented from receiving fluid, by actuation of a controllable actuator that is upstream of the section or the nozzles, or both. In some examples, the nozzles of the section may each have an associated controllable actuator which can be actuated to activate or deactivate the nozzles. The application rate of product is the rate (volumetric rate) at which product is applied to the field over which sprayer  100 - 3  travels. The application rate corresponds to a volumetric flow rate of the product from the tanks  255  through the spray nozzles  258 . The volumetric flow rate is controlled by operation of actuators (such as fluid pumps or valves), such as by varying the speed of actuation of the pump with an associated motor or by controllably opening and closing a valve. In some examples, a controllable valve that corresponds to each section or to each nozzle, can be operable to reciprocate (e.g., pulse) between a closed state and an open state at variable frequency (e.g., pulse width modulation control) to control the rate at which the product is discharged from the set of spray nozzles  258  of the respective section or from the respective individual spray nozzle  258 . 
       FIG. 8  also shows that agricultural sprayer  100 - 3  can include one or more in-situ sensors  308 , some of which are shown in  FIG. 8  as well as below. For example,  FIG. 8  shows that sprayer  100 - 3  can include one or more fill level sensors  271  that detect a fill level of material in tanks  255 . Fill level sensors can include float gauges, weight sensors that detect a weight of material in tanks  255 , emitter sensors that detect a level to which the material is filled, as well as various other types of sensors. 
     Additionally,  FIG. 8  shows that sprayer  100 - 3  can include one or more observation sensor systems  251 . Observation sensors systems  251  can be located at various locations on sprayer  100 - 3 . As illustrated, sprayer  100 - 3  includes an observation sensor system  251  coupled to the roof (or frame) of operator compartment  259  as well as a plurality of observation sensor systems  251  disposed on each of arm  262  and arm  267  of boom  254 . Observation sensor systems  251  can detect a variety of characteristic at the field, for example, soil nutrients, weed characteristics, as well as a variety of other characteristics. Observation sensor systems  251  may include one or more of an imaging system (e.g., stereo or mono camera), optical sensors, radar, lidar, ultrasonic sensors, infrared or thermal sensors, as well as a variety of other sensors. 
     It will be noted that while various examples of in-situ sensors  308  are shown in  FIGS. 1-8 , a mobile agricultural material application machine  100  (e.g.,  100 - 1 ,  100 - 2 ,  100 - 3 ) can include various other types of sensors, some of which will be discussed in  FIG. 10 . 
       FIG. 9  shows one example of a material delivery machine  379 .  FIG. 9  shows that a material delivery machine  379  can include a towing vehicle and towed implement, such as a truck (e.g., semi-truck)  280  and trailer (e.g., semi-trailer)  282 . Various other forms of material delivery machines are contemplated herein. For instance, in other examples, the material delivery machine  379  may not include a towed implement, instead, the material container may be integrated on the frame (e.g., chassis) of the vehicle. 
     Truck  280 , as illustrated, includes a power plant  283  (e.g., internal combustion engine, battery and electric motors, etc.), ground engaging elements  285  (e.g., wheels or tracks), and an operator compartment  287 . The operator compartment can include a variety of operator interface mechanisms, which can be similar to operator interface mechanisms  318  shown in  FIG. 10 . In some examples, truck  280  may be autonomous or semi-autonomous. Trailer  282  is coupled to track by way of a connection assembly (e.g., one or more of a hitch, electrical coupling, hydraulic coupling, pneumatic coupling, etc.) and, as illustrated, includes ground engaging elements  290 , such as wheels or tracks, and a material container  292  which includes a volume to store or hold one or more materials (dry or liquid), such as seed, fertilizer, herbicide, pesticide, etc. 
     In some examples, material delivery machine  379  can also include a material transfer subsystem (not shown) which can include a conduit (e.g., a chute, a hose, a line, a pipe, etc.) through which material can be conveyed by an actuator such as an auger, a pump, a motor, etc. In other examples, the material transfer subsystem may comprise an actuatable door disposed on the bottom side of the material container  292  that is actuatable between an open position and a closed position. These are merely some examples. 
       FIG. 10  is a block diagram showing some portions of an agricultural material application system architecture  300 .  FIG. 3  shows that agricultural material application system architecture  300  includes mobile agricultural material application machine  100  (e.g.,  100 - 1 ,  100 - 2 ,  100 - 3 , etc.). Agricultural material application system  300  also includes one or more remote computing systems  368 , one or more remote user interfaces  364 , network  359 , delivery vehicle(s)  379 , delivery service system(s)  377 , and one or more information maps  358 . Mobile agricultural material application machine  100 , itself, illustratively includes one or more processors or servers  301 , data store  302 , communication system  306 , one or more in-situ sensors  308  that sense one or more characteristics at a field concurrent with an operation, and a processing system  338  that processes the sensor data (e.g., sensor signals, images, etc.) generated by in-situ sensors  308  to generate processed sensor data. The in-situ sensors  308  generate values corresponding to the sensed characteristics. Mobile machine  100  also includes a predictive model or relationship generator (collectively referred to hereinafter as “predictive model generator  310 ”), predictive model or relationship (collectively referred to hereinafter as “predictive model  311 ”), predictive map generator  312 , control zone generator  313 , control system  314 , one or more controllable subsystems  316 , and an operator interface mechanism  318 . The mobile machine can also include a wide variety of other machine functionality  320 . 
     The in-situ sensors  308  can be on-board mobile machine  100 , remote from mobile machine  100 , such as deployed at fixed locations on the worksite or on another machine operating in concert with mobile machine  100 , such as an aerial vehicle, and other types of sensors, or a combination thereof. In-situ sensors  308  sense characteristics at the worksite during the course of an operation. In-situ sensors  308  illustratively include one or more weed sensors  372 , one or more nutrient sensors  374 , one or more material consumption sensors  376 , geographic position sensors  304 , heading/speed sensors  325 , and can include various other sensors  328 , such as the various other sensors described above. 
     Weed sensors  372  illustratively detect values of weed characteristics which can be indicative of weed presence, weed location, weed type, weed intensity, as well as various other weed characteristics. Weed sensors  372  can be located at various locations on material application machine  100  and can be configured to detect weed characteristics at the field ahead of material application machine  100  or ahead of a given component of material application machine  100 , or both. Weed sensors  372  may include one or more of an imaging system (e.g., stereo or mono camera), optical sensors, radar, lidar, ultrasonic sensors, infrared or thermal sensors, as well as a variety of other sensors. In some examples, weed sensors  372  can be similar to observation sensors systems  151  or  251 . These are merely some examples. Weed sensors  372  can be any of a variety of different types of sensors. 
     Nutrient sensors  374  illustratively detect nutrient values. Nutrient values can be indicative of an amount (e.g., concentration) of one or more nutrients in the soil of the field, such as the amount of nitrogen, the amount of potassium, the amount of phosphate, the amount of organic matter, the amount of one or more micronutrients, etc. Thus, in some examples, nutrient values are or include soil nutrient values. Alternatively, or additionally, nutrient values can be indicative of an amount (e.g., concentration) of one or more nutrients in the plants at the field. Nutrients of the plant can include various types of nutrients, such as boron, sulphur, manganese, zinc, magnesium, phosphorus, calcium, iron, copper, molybdenum, potassium, nitrogen, etc., as well as constituents of the crop such as protein, sugar, starch, ligan, etc. Thus, in some examples, nutrient values are or include plant nutrient values. As can be seen, nutrient values may be soil nutrient values or plant nutrient values, or both. In some examples, the nutrient values may be binary in that they indicate sufficient or deficient levels (e.g., relative to a threshold) rather than a valued amount. Nutrient sensors  374  can be located at various locations on material application machine  100  and can be configured to detect nutrient characteristics (e.g., soil nutrient characteristics or plant nutrient characteristics, or both) at the field ahead of material application machine  100  or ahead of a given component of material application machine  100 , or both. In some examples, nutrient sensors  374  detect the soil or a characteristic of the soil to detect nutrient values. For instance, nutrient sensors  374  may detect the color of the soil, the thermal characteristics of the soil, the emissivity or absorption of electromagnetic radiation, the capability of the soil to conduct or accumulate electrical charge, as well as various other characteristics. In some examples, nutrient sensors  374  detect the vegetation (e.g., plants) or a characteristic of the vegetation (e.g., plants) at the field to detect nutrient values. For instance, nutrient sensors  374  may detect the plant size, the plant health, the plant coloration, constituents of the plant (e.g., protein, starch, sugar, lignan, etc.), the emissivity or absorption of electromagnetic radiation, characteristics of the components of the plant (e.g., characteristics of the leaves, characteristics of the leaf buds, characteristics of the stalk, etc.), as well as various other characteristics. Nutrient sensors  374  may include one or more of an imaging system (e.g., stereo or mono camera), optical sensors, radar, lidar, ultrasonic sensors, infrared or thermal sensors, soil probes, electromagnetic sensors that detect the capability of the soil to conduct or accumulate electrical charge, as well as a variety of other sensors. In some examples, nutrient sensors  374  can be similar to observation sensors systems  151  or  251 . In some examples, nutrient sensors  374  can be similar to sensor  170 . These are merely some examples. Nutrient sensors  374  can be any of a variety of different types of sensors. 
     Material consumption sensors  376  illustratively detect material consumption values which can be indicative of the amount of material (e.g., seed, dry or liquid material, fertilizer, herbicide, pesticide, etc.) consumed (e.g., used) by material application machine  100  at the field. Material consumption sensors  374  can be located at various locations on material application machine. Material consumption sensors  374  can include fill level sensors (e.g.,  117 ,  177 ,  178 ,  271 , etc.) that detect a fill level of a material container, such as tanks  107 , tanks  110 , tanks  112 , tanks  234 , and tanks  255 . In some examples, material consumption sensors  374  can detect a flow rate of material, such as flow sensors (e.g., flow meters) that detect a volumetric flow of material through a delivery line (e.g.,  111 , or conduits of boom  220 , or conduits of boom  254 , etc.). In some examples, material consumption sensors  374  can provide a count of the material consumed, for example, seed sensors, such as seed sensors  122 ,  193 , or  203 . In some examples, observation sensor systems  151  may detect the material consumed, such as an observation sensor system disposed to observe the trench or furrow  162 . In some examples, material consumption sensors  376  can include sensors that detect the operating parameters of one or more actuators, such as the speed (or rate) at which the actuators actuate to control the rate of material. These are merely some examples. Material consumption sensors  376  can be any of a variety of different types of sensors. 
     Geographic position sensors  304  illustratively sense or detect the geographic position or location of mobile machine  100 . Geographic position sensors  304  can include, but are not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensors  304  can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensors  304  can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors. 
     Heading/speed sensors  325  detect a heading and speed at which mobile machine  100  is traversing the worksite during the operation. This can include sensors that sense the movement of ground-engaging elements (e.g., wheels or tracks), or can utilize signals received from other sources, such as geographic position sensor  304 , thus, while heading/speed sensors  325  as described herein are shown as separate from geographic position sensor  304 , in some examples, machine heading/speed is derived from signals received from geographic positions sensor  304  and subsequent processing. In other examples, heading/speed sensors  325  are separate sensors and do not utilize signals received from other sources. 
     Other in-situ sensors  328  may be any of a wide variety of other sensors, including the other sensors described above with respect to  FIGS. 1-8 . Other in-situ sensors  328  can be on-board mobile machine  100  or can be remote from mobile machine  100 , such as other in-situ sensors  328  on-board another mobile machine that capture in-situ data of characteristics at the field or sensors at fixed locations throughout the field. The remote data from remote sensors can be obtained by mobile machine  100  via communication system  306  over network  359 . 
     In-situ data includes data taken from a sensor on-board the mobile machine  100  or taken by any sensor where the data are detected during the operation of mobile machine  100  at a field. 
     Processing system  338  processes the sensor data (e.g., signals, images, etc.) generated by in-situ sensors  308  to generate processed sensor data indicative of one or more characteristics. For example, processing system generates processed sensor data indicative of characteristic values based on the sensor data generated by in-situ sensors  308 , such as weed values based on sensor data generated by weed sensors  372 , nutrient values based on sensor data generated by nutrient sensors  374 , and material consumption values based on sensor data generated by material consumption sensors  376 . Processing system  338  also processes sensor data generated by other in-situ sensors  308  to generate processed sensor data indicative of other characteristic values, such as machine speed characteristic (travel speed, acceleration, deceleration, etc.) values based on sensor data generated by heading/speed sensors  325 , machine heading values based on sensor data generated by heading/speed sensors  325 , geographic position (or location) values based on sensor data generated by geographic position sensors  304 , as well as various other values based on sensors signals generated by various other in-situ sensors  328 . 
     It will be understood that processing system  338  can be implemented by one or more processers or servers, such as processors or servers  301 . Additionally, processing system  338  can utilize various sensor signal filtering functionalities, noise filtering functionalities, sensor signal categorization, aggregation, normalization, as well as various other processing functionalities. Similarly, processing system  338  can utilize various image processing functionalities such as, sequential image comparison, RGB, edge detection, black/white analysis, machine learning, neural networks, pixel testing, pixel clustering, shape detection, as well any number of other suitable processing and data extraction functionalities. 
       FIG. 10  also shows that an operator  360  may operate mobile machine  100 . The operator  360  interacts with operator interface mechanisms  318 . In some examples, operator interface mechanisms  318  may include joysticks, levers, a steering wheel, linkages, pedals, buttons, dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, operator  360  may interact with operator interface mechanisms  318  using touch gestures. In some examples, at least some operator interface mechanisms  318  may be disposed in an operator compartment of mobile machine  100 . In some examples, at least some operator interface mechanisms  318  may be remote (or separable) from mobile machine  100  but are in communication therewith. Thus, the operator  360  may be local or remote. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types of operator interface mechanisms  318  may be used and are within the scope of the present disclosure. 
       FIG. 10  also shows remote users  366  interacting with mobile machine  100  or remote computing systems  368 , or both, through user interfaces mechanisms  364  over network  359 . In some examples, user interface mechanisms  364  may include joysticks, levers, a steering wheel, linkages, pedals, buttons, dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, user  366  may interact with user interface mechanisms  364  using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types of user interface mechanisms  364  may be used and are within the scope of the present disclosure. 
     Remote computing systems  368  can be a wide variety of different types of systems, or combinations thereof. For example, remote computing systems  368  can be in a remote server environment. Further, remote computing systems  368  can be remote computing systems, such as mobile devices, a remote network, a farm manager system, a vendor system, or a wide variety of other remote systems. In one example, mobile machine  100  can be controlled remotely by remote computing systems  368  or by remote users  366 , or both. As will be described below, in some examples, one or more of the components shown being disposed on mobile machine  100  in  FIG. 10  can be located elsewhere, such as at remote computing systems  368 . 
       FIG. 10  also shows that one or more delivery vehicles  379  can interact with other items in agricultural material application system  300  over network  359 . For instance, communication system  306  of mobile agricultural material application machine  100  may communicate with one or more delivery vehicles  379  to provide information such as material delivery locations and material delivery times to schedule material delivery. In another example, material one or more delivery vehicles  379  may be controlled, such as by control system  314 , to travel to a material delivery location. 
       FIG. 10  also shows that one or more delivery service systems  380  can interact with other items in agricultural material application system  300  over network  359 . For instance, communication system  306  of mobile agricultural material application machine  100  may communicate with material delivery service systems  380  to provide information such as material delivery locations and material delivery times to schedule material delivery. Material delivery service systems  380  can be a wide variety of different types of systems, or combinations thereof. For example, material delivery service systems  380  can be in a remote server environment. Further, material delivery service systems  380  can be remote computing systems, such as mobile devices, a remote network, a vendor system, or a wide variety of other remote systems. 
       FIG. 10  also shows that mobile machine  100  can obtain one or more information maps  358 . As described herein, the information maps  358  include, for example, a soil property map, a yield map, a residue map, a constituents map, a seeding map, a topographic map, a vegetative index (VI) map, an optical map, a weed map, a contamination map, as well as various other maps. However, information maps  358  may also encompass other types of data, such as other types of data that were obtained prior to a current operation or a map from a prior operation. In other examples, information maps  358  can be generated during a current operation, such a map generated by predictive map generator  312  based on a predictive model  311  generated by predictive model generator  310 . 
     Information maps  358  may be downloaded onto mobile material application machine  100  over network  359  and stored in data store  302 , using communication system  306  or in other ways. In some examples, communication system  306  may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations of networks. Network  359  illustratively represents any or a combination of any of the variety of networks. Communication system  306  may also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both. 
     As described above, the present description relates to the use of models to predict one or more characteristics at the field at which mobile material application machine  100  is operating. The models  311  can be generated by predictive model generator  310 , during the current operation. 
     In one example, predictive model generator  310  generates a predictive model  311  that is indicative of a relationship between the values sensed by the in-situ sensors  308  and values mapped to the field by the information maps  358 . For example, if the information map  358  maps values of a characteristic to different locations in the worksite, and the in-situ sensor  308  are sensing values indicative of a characteristic (e.g., weed values, nutrient values, material consumption values, or speed characteristic values), then model generator  310  generates a predictive model that models the relationship between the values of the mapped characteristic and the values of the sensed characteristic. 
     In some examples, the predictive map generator  312  uses the predictive models generated by predictive model generator  310  to generate functional predictive maps that predict the value of a characteristic, sensed by the in-situ sensors  308 , at different locations in the field based upon one or more of the information maps  358 . 
     For example, where the predictive model  311  is a predictive nutrient model that models a relationship between nutrient values sensed by in-situ sensors  308  and one or more of soil property values from a soil property map, yield values from a yield map, residue values from a residue map, constituent values from a constituents map, seeding characteristic values from a seeding map, topographic characteristic values from a topographic map, vegetative index values from a vegetative index map, and other characteristic values from another information map  358 , then predictive map generator  312  generates a functional predictive nutrient map that predicts nutrient values at different locations at the worksite based on one or more of the mapped values at those locations and the predictive nutrient model. 
     In another example, where the predictive model  311  is a predictive weed model that models a relationship between weed values sensed by in-situ sensors  308  and one or more of vegetative index values from a vegetative index map, optical characteristic values from an optical map, weed values from a weed map, and other characteristic values from another information map  358 , then predictive map generator  312  generates a functional predictive weed map that predicts weed values at different locations at the worksite based on one or more of the mapped values at those locations and the predictive weed model. 
     In another example, where the predictive model  311  is a predictive material consumption model that models a relationship between material consumption values sensed by in-situ sensors  308  and one or more of soil property values from a soil property map, weed values from a weed map, contamination values from a contamination map, vegetative index values from a vegetative index map, topographic characteristic values from a topographic map, and other characteristic values from another information map  358 , then predictive map generator  312  generates a functional predictive material consumption map that predicts material consumption values at different locations at the worksite based on one or more of the mapped values at those locations and the predictive material consumption model. 
     In another example, where the predictive model  311  is a predictive speed model that models a relationship between speed characteristic values sensed by in-situ sensors  308  and values of one or more characteristics from one or more information maps  358 , then predictive map generator  312  generates a functional predictive speed map that maps predictive speed characteristic values at different locations at the worksite based on or more of the mapped values at those locations and the predictive speed model. 
     In some examples, the type of values in the functional predictive map  263  may be the same as the in-situ data type sensed by the in-situ sensors  308 . In some instances, the type of values in the functional predictive map  263  may have different units from the data sensed by the in-situ sensors  308 . In some examples, the type of values in the functional predictive map  263  may be different from the data type sensed by the in-situ sensors  308  but have a relationship to the type of data type sensed by the in-situ sensors  308 . For example, in some examples, the data type sensed by the in-situ sensors  308  may be indicative of the type of values in the functional predictive map  363 . In some examples, the type of data in the functional predictive map  363  may be different than the data type in the information maps  358 . In some instances, the type of data in the functional predictive map  263  may have different units from the data in the information maps  358 . In some examples, the type of data in the functional predictive map  263  may be different from the data type in the information map  358  but has a relationship to the data type in the information map  358 . For example, in some examples, the data type in the information maps  358  may be indicative of the type of data in the functional predictive map  263 . In some examples, the type of data in the functional predictive map  263  is different than one of, or both of, the in-situ data type sensed by the in-situ sensors  308  and the data type in the information maps  358 . In some examples, the type of data in the functional predictive map  263  is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors  308  and the data type in information maps  358 . In some examples, the type of data in the functional predictive map  263  is the same as one of the in-situ data type sensed by the in-situ sensors  308  or the data type in the information maps  358 , and different than the other. 
     As shown in  FIG. 10 , predictive map  264  predicts the value of a sensed characteristic (sensed by in-situ sensors  308 ), or a characteristic related to the sensed characteristic, at various locations across the worksite based upon one or more information values in one or more information maps  358  at those locations and using a predictive model  311 . For example, if predictive model generator  310  has generated a predictive model indicative of a relationship between soil property values and values of a characteristic sensed by in-situ sensors  308  then, given the soil property value at different locations across the worksite, predictive map generator  312  generates a predictive map  264  that predicts values of the sensed characteristic at different locations across the worksite. The soil property value, obtained from the soil property map, at those locations and the relationship between soil property values and the values of the sensed characteristic, obtained from a predictive model  311 , are used to generate the predictive map  264 . This is merely one example. 
     Some variations in the data types that are mapped in the information maps  358 , the data types sensed by in-situ sensors  308 , and the data types predicted on the predictive map  264  will now be described. 
     In some examples, the data type in one or more information maps  358  is different from the data type sensed by in-situ sensors  308 , yet the data type in the predictive map  264  is the same as the data type sensed by the in-situ sensors  308 . For instance, the information map  358  may be a seeding map, and the variable sensed by the in-situ sensors  308  may be a nutrient value. The predictive map  264  may then be a predictive nutrient map that maps predictive nutrient values to different geographic locations in the in the worksite. In another example, the information map  358  may be a vegetative index map, and the variable sensed by the in-situ sensors  308  may be a weed value. The predictive map  264  may then be a predictive weed map that maps predictive weed values to different geographic locations in the in the worksite. In another example, the information map  358  may be a contamination map, and the variable sensed by the in-situ sensors  308  may be a material consumption value. The predictive map  264  may then be a predictive material consumption map that maps predictive material consumption values to different geographic locations in the in the worksite. In another example, the information map  358  may be a soil property map, and the variable sensed by the in-situ sensors  308  may be a speed characteristic value. The predictive map  264  may then be a predictive speed map that maps predictive speed characteristic values to different geographic locations in the in the worksite. 
     Also, in some examples, the data type in the information map  358  is different from the data type sensed by in-situ sensors  308 , and the data type in the predictive map  264  is different from both the data type in the information map  358  and the data type sensed by the in-situ sensors  308 . For example, the information map may be a residue map, and the variable sensed by the in-situ sensors  308  may be a color of the plants. The predictive map may then be a predictive nutrient map that maps predictive nutrient values to the different geographic locations in the worksite. 
     In some examples, the information map  358  is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors  308 , yet the data type in the predictive map  264  is the same as the data type sensed by the in-situ sensors  308 . For instance, the information map  358  may be a seeding map generated during a previous planting operation on the field, and the variable sensed by the in-situ sensors  308  may be a nutrient value. The predictive map  264  may then be a predictive nutrient map that maps predictive nutrient values to different geographic locations in the field. In another example, the information map  358  may be a vegetative index map generated during a previous operation on the field, and the variable sensed by the in-situ sensors  308  may be a weed value. The predictive map  264  may then be a predictive weed map that maps predictive weed values to different geographic locations in the field. In another example, the information map  358  may be a topographic map generated during a previous operation on the field, and the variable sensed by the in-situ sensors  308  may be a material consumption value. The predictive map  264  may then be a predictive material consumption map that maps predictive material consumption values to different geographic locations in the field. In another example, the information map  358  may be a soil property map generated during a previous operation on the field, and the variable sensed by the in-situ sensors  308  may be a speed characteristic value. The predictive map  264  may then be a predictive speed map that maps predictive speed characteristic values to different geographic locations in the field. 
     In some examples, the information map  358  is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors  308 , and the data type in the predictive map  264  is also the same as the data type sensed by the in-situ sensors  308 . For instance, the information map  358  may be a weed map generated during a previous year or earlier in the same season, and the variable sensed by the in-situ sensors  308  may be a weed value. The predictive map  264  may then be a predictive weed map that maps predictive weed values to different geographic locations in the field. In such an example, the relative weed value differences in the georeferenced information map  358  from the prior year or earlier in the same season can be used by predictive model generator  310  to generate a predictive model that models a relationship between the relative weed value differences on the information map  358  and the weed values sensed by in-situ sensors  308  during the current operation. The predictive model is then used by predictive map generator  310  to generate a predictive weed map. In another example, the information map  358  may be a nutrient map generated during a previous year or earlier in the same season, and the variable sensed by the in-situ sensors  308  may be a nutrient value. The predictive map  264  may then be a predictive nutrient map that maps predictive nutrient values to different geographic locations in the field. In such an example, the relative nutrient value differences in the georeferenced information map  358  from the prior year or earlier in the same season can be used by predictive model generator  310  to generate a predictive model that models a relationship between the relative nutrient value differences on the information map  358  and the nutrient values sensed by in-situ sensors  308  during the current operation. The predictive model is then used by predictive map generator  310  to generate a predictive nutrient map. In another example, the information map  358  may be a material consumption map generated during a previous year or earlier in the same season, and the variable sensed by the in-situ sensors  308  may be a material consumption value. The predictive map  264  may then be a predictive material consumption map that maps predictive material consumption values to different geographic locations in the field. In such an example, the relative material consumption value differences in the georeferenced information map  358  from the prior year or earlier in the same season can be used by predictive model generator  310  to generate a predictive model that models a relationship between the relative material consumption value differences on the information map  358  and the material consumption values sensed by in-situ sensors  308  during the current operation. The predictive model is then used by predictive map generator  310  to generate a predictive material consumption map. In another example, the information map  358  may be a speed map generated during a previous year or earlier in the same season, and the variable sensed by the in-situ sensors  308  may be a speed characteristic value. The predictive map  264  may then be a predictive speed map that maps predictive speed characteristic values to different geographic locations in the field. In such an example, the relative speed characteristic value differences in the georeferenced information map  358  from the prior year or earlier in the same season can be used by predictive model generator  310  to generate a predictive model that models a relationship between the relative speed characteristic value differences on the information map  358  and the speed characteristic values sensed by in-situ sensors  308  during the current operation. The predictive model is then used by predictive map generator  310  to generate a predictive speed map. 
     In another example, the information map  358  may be a topographic map generated during a prior operation in the same year and may map topographic characteristic values to different geographic locations in the field. The variable sensed by the in-situ sensors  308  during the current operation may be a nutrient value, a weed value, a material consumption value, or a speed characteristic value. The predictive map  264  may then map predictive characteristic values (e.g., nutrient values, weed values, material consumption values, or speed characteristic values) to different geographic locations in the field. In such an example, the topographic characteristic values at time of the prior operation are geo-referenced, recorded, and provided to mobile machine  100  as an information map  358  of topographic characteristic values. In-situ sensors  308  during a current operation can detect characteristic values (e.g., nutrient values, weed values, material consumption values, or speed characteristic values) at geographic locations in the field and predictive model generator  310  may then build a predictive model that models a relationship between characteristic values (e.g., nutrient values, weed values, material consumption values, or speed characteristic values) at the time of the current operation and topographic characteristic values at the time of the prior operation. This is because the topographic characteristic values at the time of the prior operation are likely to be the same as at the time of the current operation or may be more accurate or otherwise may be more reliable (or fresher) than topographic characteristic values obtained in other ways. For instance, a machine that operated on the field previously may provide topographic characteristic values that are fresher (closer in time) or more accurate than topographic characteristic values detected in other ways, such as satellite or other aerial-based sensing. For instance, vegetation on the field, meteorological conditions, as well as other obscurants, may obstruct or otherwise create noise that makes topographic characteristic values unavailable or unreliable. Thus, the topographic map generated during the prior operation may be more preferable. This is merely one example. 
     In some examples, predictive map  264  can be provided to the control zone generator  313 . Control zone generator  313  groups adjacent portions of an area into one or more control zones based on data values of predictive map  264  that are associated with those adjacent portions. A control zone may include two or more contiguous portions of a worksite, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting of controllable subsystems  316  may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map  264 . In that case, control zone generator  313  parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems  316 . In another example, control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem  316  or for groups of controllable subsystems  316 . The control zones may be added to the predictive map  264  to obtain predictive control zone map  265 . Predictive control zone map  265  can thus be similar to predictive map  264  except that predictive control zone map  265  includes control zone information defining the control zones. Thus, a functional predictive map  263 , as described herein, may or may not include control zones. Both predictive map  264  and predictive control zone map  265  are functional predictive maps  263 . In one example, a functional predictive map  263  does not include control zones, such as predictive map  264 . In another example, a functional predictive map  263  does include control zones, such as predictive control zone map  265 . 
     It will also be appreciated that control zone generator  313  can cluster values to generate control zones and the control zones can be added to predictive control zone map  265 , or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibrating mobile machine  100  or both. In other examples, the control zones may be presented to the operator  360  and used to control or calibrate mobile machine  100 , and, in other examples, the control zones may be presented to the operator  360  or another user, such as a remote user  366 , or stored for later use. 
     Predictive map  264  or predictive control zone map  265  or both are provided to control system  314 , which generates control signals based upon the predictive map  264  or predictive control zone map  265  or both. In some examples, communication system controller  329  controls communication system  306  to communicate the predictive map  264  or predictive control zone map  265  or control signals based on the predictive map  264  or predictive control zone map  265  to other mobile machines (e.g., delivery vehicles  379 , other mobile material application machines) that are operating at the same worksite or in the same operation. In some examples, communication system controller  329  controls the communication system  306  to send the predictive map  264 , predictive control zone map  265 , or both to other remote systems, such as remote computing systems  368  or delivery service systems  377 , or both. 
     Control system  314  includes communication system controller  329 , interface controller  330 , one or more material application controllers  331 , a propulsion controller  334 , a path planning controller  335 , one or more zone controllers  336 , logistics module  315 , and control system  314  can include other items  339 . Controllable subsystems  316  include material application actuators  340 , propulsion subsystem  350 , steering subsystem  352 , and can include a wide variety of other controllable subsystems  356 . 
     Control system  314  can control various items of agricultural system  300  based on sensor data detected by sensors  308 , models  311 , predictive map  264  or predictive map with control zones  265 , operator or user inputs, as well as various other bases. 
     Interface controllers  330  are operable to generate control signals to control interface mechanisms, such as operator interface mechanisms  318  or user interface mechanisms  364 , or both. While operator interface mechanisms  318  are shown as separate from controllable subsystems  316 , it will be understood that operator interface mechanisms  318  are controllable subsystems. The interface controllers  330  are also operable to present the predictive map  264  or predictive control zone map  265  or other information derived from or based on the predictive map  264 , predictive control zone map  265 , or both, to operator  360  or a remote user  366 , or both. Operator  360  may be a local operator or a remote operator. As an example, interface controller  330  generates control signals to control a display mechanism to display one or both of predictive map  264  and predictive control zone map  265  for the operator  360  or a remote user  366 , or both. Interface controller  330  may generate operator or user actuatable mechanisms that are displayed and can be actuated by the operator or user to interact with the displayed map. The operator or user can edit the map by, for example, correcting a value displayed on the map, based on the operator&#39;s or the user&#39;s observation. 
     Propulsion controller  334  illustratively generates control signals to control propulsion subsystem  350  to control a speed setting, such as one or more of a travel speed, acceleration, deceleration, and direction (e.g., forward and reverse), such as based on one or more of the predictive map  264  and the predictive control zone map  265  as well as based on outputs from logistics module  315 . Propulsion subsystem  350  includes one or more powertrain components, such as a powerplant (e.g., internal combustion engine, electric motors and batteries, etc.), a transmission or gear box, as well as various other items. 
     Path planning controller  335  illustratively generates control signals to control steering subsystem  352  to steer mobile machine  100  according to a desired path or according to desired parameters, such as desired steering angles, based on one or more of the predictive map  264  and the predictive control zone map  265  or logistics outputs from logistics module  315 . Path planning controller  333  can control a path planning system to generate a route for mobile machine  100  and can control propulsion subsystem  350  and steering subsystem  352  to steer agricultural mobile machine  100  along that route. Steering subsystem  352  can include various items, such as one or more actuators to control an angle (steering angle) of one or more ground engaging elements (e.g., wheels or tracks) of mobile machine  100 . 
     Material application controllers  331  illustratively generate control signals, to control one or material application actuators  340  of mobile material application machine  100  to control the application of material to the field, that is the amount of material applied, the rate at which material is applied, whether or not material is applied, etc. Material application controllers  331  can generate control signals based on a predictive map  264  or predictive control zone map  265 , or both. Material application controllers  331  can generate control signals based on logistics outputs from logistics module  315 . 
     Material application actuators  340  can include a variety of different types of actuators such as hydraulic, pneumatic, electromechanical actuators, motors, pumps, valves, as well as various other types of actuators. Some examples of actuators  340  are discussed above in  FIGS. 1-8 . For example, actuators  340  can include actuators that drive the speed of rotation of a seed meter, such as seed meter  124  or the speed of rotation of an assistive seed delivery, such as assistive seed delivery system  166 , or both. Actuators  340  can include actuators that control a flow of material from one or more material containers through a delivery line and to an outlet. For example, when material application machine  100  is in the form of a planting machine, such as planting machine  100 - 1 , actuators  340  can include one or more actuators, such as pumps or valves, or both, (e.g.,  109  or  115 ) that control the flow of material from a material container (e.g.,  107 ,  110 , or  112 ) through a delivery line (e.g.,  111 ) and out of an outlet (e.g.,  119 ). In another example, when material application machine  100  is a spraying machine, such as spraying machine  100 - 2  or  100 - 3 , actuators can include one or more pumps or one or more valves, or both, that control the flow of material from a material container (e.g.,  234  or  255 ) through a conduit and out of an outlet (e.g.,  230  or  258 ). In some examples, actuators  340  may be controlled to activate or deactivate one or more components of material application machine  100 . For example, actuators  340  may be controlled to activate or deactivate one or more nozzles or one or more sections of a spraying machine. 
     In some examples, the mobile agricultural material application machine  100  may have multiple material containers. Each container may contain a different variety of the material to be applied, for example, a different seed variety (e.g., genotype), a different fertilizer material variety, a different herbicide variety, a different pesticide variety, etc. In some examples, material application controllers  331  may control material application actuators  340  to control which variety is applied to the field, based on a predictive map  264  or a predictive control zone map  265 , or both, as well as other inputs. For instance, it may be desirable to plant weed resistant seed varieties in areas of the field where the map ( 264  or  265 , or both) indicate high levels of weeds. In another example, it may be desirable to plant low nutrient requirement seed varieties in areas of the field where the map ( 264  or  265 , or both) indicate low levels of nutrient(s). These are merely some examples. In another example, it may be desirable to apply a different variety of herbicide in areas of the field where the map ( 264  or  265 , or both) indicate weed of a given type, which may be resistant to the currently activated herbicide. This is merely one example. 
     Zone controller  336  illustratively generates control signals to control one or more controllable subsystems  316  to control operation of the one or more controllable subsystems  316  based on the predictive control zone map  265 . 
     Logistics module  315  illustratively generates logistics control outputs. Logistics module  315  will be discussed in more detail in  FIG. 16 . 
     Other controllers  339  included on the mobile machine  100 , or at other locations in agricultural system  300 , can control other subsystems  356 . 
     While the illustrated example of  FIG. 10  shows that various components of agricultural system architecture  300  are located on mobile material application machine  100 , it will be understood that in other examples one or more of the components illustrated on mobile material application machine  100  in  FIG. 10  can be located at other locations, such as one or more remote computing systems  368 . For instance, one or more of data stores  302 , map selector  309 , predictive model generator  310 , predictive model  311 , predictive map generator  312 , functional predictive maps  263  (e.g.,  264  and  265 ), control zone generator  313 , and control system  314  (or components thereof) can be located remotely from mobile machine  100  but can communicate with (or be communicated to) mobile machine  100  via communication system  306  and network  359 . Thus, predictive models  311  and functional predictive maps  263  may be generated and/or located at remote locations away from mobile machine  100  and can be communicated to mobile machine  100  over network  359 , for instance, communication system  306  can download the predictive models  311  and functional predictive maps  263  from the remote locations and store them in data store  302 . In other examples, mobile machine  100  may access the predictive models  311  and functional predictive maps  263  at the remote locations without downloading the predictive models  311  and functional predictive maps  263 . The information used in the generation of the predictive models  311  and functional predictive maps  263  may be provided to the predictive model generator  310  and the predictive map generator  312  at those remote locations over network  359 , for example in-situ sensor data generated by in-situ sensors  308  can be provided over network  359  to the remote locations. Similarly, information maps  358  can be provided to the remote locations. 
     In some examples, control system  314  may remain local to mobile machine  100 , and a remote system (e.g.,  368  or  364 ) may be provided with functionality (e.g., such as a control signal generator) that communicates control commands to mobile machine  100  that are used by control system  314  for the control of mobile machine  100 . 
     Similarly, where various components are located remotely from mobile machine  100 , those components can receive data from components of mobile machine  100  over network  359 . For example, where predictive model generator  310  and predictive map generator  312  are located remotely from mobile machine  100 , such as at remote computing systems  368 , data generated by in-situ sensors  308  and geographic position sensors  304 , for instance, can be communicated to the remote computing systems  368  over network  359 . Additionally, information maps  358  can be obtained by remote computing systems  368  over network  359  or over another network. 
       FIG. 11  is a block diagram of a portion of the agricultural material application system architecture  300  shown in  FIG. 10 . Particularly,  FIG. 11  shows, among other things, examples of the predictive model generator  310  and the predictive map generator  312  in more detail.  FIG. 11  also illustrates information flow among the various components shown. The predictive model generator  310  receives one or more information map(s)  358 . In the example illustrated in  FIG. 11 , information maps  358  include one or more of a soil property map  410 , a yield map  411 , a residue map  412 , a constituents map  413 , a seeding map  414 , a topographic map  415 , a vegetative index map  416 , or any of a wide variety of other information maps  429 . Predictive model generator  310  also receives geographic location data  434 , such as an indication of a geographic location, from geographic position sensor  304 . Geographic location data  434  illustratively represents the geographic locations to which values detected by in-situ sensors  308  correspond. In some examples, the geographic position of the mobile machine  100 , as detected by geographic position sensors  304 , will not be the same as the geographic position on the field to which a value detected by in-situ sensors  308  corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor  304 , along with timing, machine speed and heading, machine dimensions, machine processing delays, sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., sensor field of view), as well as various other data, can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor  308  corresponds. 
     In-situ sensors  308  illustratively include nutrient sensors  374 , as well as processing system  338 . In some examples, processing system  338  is separate from in-situ sensors  308  (such as the example shown in  FIG. 10 ). In some instances, nutrient sensors  374  may be located on-board mobile material application machine  100 . The processing system  338  processes sensor data generated from nutrient sensors  374  to generate processed sensor data  430  indicative of nutrient values (e.g., soil nutrient values or plant nutrient values, or both). 
     As shown in  FIG. 11 , the example predictive model generator  310  includes a nutrient-to-soil property model generator  440 , a nutrient-to-yield model generator  441 , a nutrient-to-residue model generator  442 , a nutrient-to-constituents model generator  443 , a nutrient-to-seeding characteristic model generator  444 , a nutrient-to-topographic characteristic model generator  445 , a nutrient-to-vegetative index model generator  446 , and a nutrient-to-other characteristic model generator  448 . In other examples, the predictive model generator  310  may include additional, fewer, or different components than those shown in the example of  FIG. 11 . Consequently, in some examples, the predictive model generator  310  may include other items  449  as well, which may include other types of predictive model generators to generate other types of nutrient models. 
     Nutrient-to-soil property model generator  440  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and soil property value(s) from the soil property map  410  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-soil property model generator  440 , nutrient-to-soil property model generator  440  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced soil property values contained in the soil property map  410  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the soil property value, from the soil property map  415 , corresponding to that given location. 
     Nutrient-to-yield model generator  441  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and yield value(s) from the yield map  411  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-yield model generator  441 , nutrient-to-yield model generator  441  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced yield values contained in the yield map  411  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the yield value, from the yield map  411 , corresponding to that given location. 
     Nutrient-to-residue model generator  442  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and residue value(s) from the residue map  412  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-residue model generator  442 , nutrient-to-residue model generator  442  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced residue values contained in the residue map  412  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the residue value, from the residue map  412 , corresponding to that given location. 
     Nutrient-to-constituents model generator  443  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and constituent value(s) from the constituents map  413  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-constituents model generator  443 , nutrient-to-constituents model generator  443  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced constituent values contained in the constituents map  413  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the constituent value, from the constituents map  413 , corresponding to that given location. 
     Nutrient-to-seeding characteristic model generator  444  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and seeding characteristic value(s) from the seeding map  414  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-seeding characteristic model generator  444 , nutrient-to-seeding characteristic model generator  444  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced seeding characteristic values contained in the seeding map  414  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the seeding characteristic value, from the seeding map  414 , corresponding to that given location. 
     Nutrient-to-topographic characteristic model generator  445  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and topographic characteristic value(s) from the topographic map  415  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-topographic characteristic model generator  445 , nutrient-to-topographic characteristic model generator  445  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced topographic characteristic values contained in the topographic map  415  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the topographic characteristic value, from the topographic map  415 , corresponding to that given location. 
     Nutrient-to-vegetative index model generator  446  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and vegetative index value(s) from the vegetative index map  416  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-vegetative index model generator  446 , nutrient-to-vegetative index model generator  446  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced vegetative index values contained in the vegetative index map  416  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the vegetative index value, from the vegetative index map  416 , corresponding to that given location. 
     Nutrient-to-other characteristic model generator  448  identifies a relationship between nutrient value(s) detected in in-situ sensor data  430 , at geographic location(s) to which the nutrient value(s), detected in the in-situ sensor data  430 , correspond, and other characteristic value(s) from an other map  429  corresponding to the same geographic location(s) to which the detected nutrient value(s) correspond. Based on this relationship established by nutrient-to-other characteristic model generator  448 , nutrient-to-other characteristic model generator  448  generates a predictive nutrient model. The predictive nutrient model is used by predictive nutrient map generator  452  to predict a nutrient value at different locations in the field based upon the georeferenced other characteristic values contained in the other map  429  corresponding to the same locations in the field. Thus, for a given location in the field, a nutrient value can be predicted at the given location based on the predictive nutrient model and the other characteristic value, from the other map  415 , corresponding to that given location. 
     In light of the above, the predictive model generator  310  is operable to produce a plurality of predictive nutrient models, such as one or more of the predictive nutrient models generated by model generators  440 ,  441 ,  442 ,  443 ,  444 ,  445 ,  446 ,  448 , and  449 . In another example, two or more of the predictive models described above may be combined into a single predictive nutrient model, such as a predictive nutrient model that predicts a nutrient value based upon two or more of the soil property values, the yield values, the residue values, the constituent values, the seeding characteristic values, the topographic characteristic values, the vegetative index values, and the other characteristic values at different locations in the field. Any of these nutrient models, or combinations thereof, are represented collectively by predictive nutrient model  450  in  FIG. 11 . 
     The predictive nutrient model  450  is provided to predictive map generator  312 . In the example of  FIG. 11 , predictive map generator  312  includes a predictive nutrient map generator  452 . In other examples, predictive map generator  312  may include additional or different map generators. Thus, in some examples, predictive map generator  312  may include other items  456  which may include other types of map generators to generate other types of maps. 
     Predictive nutrient map generator  452  receives one or more of the soil property map  410 , the yield map  411 , the residue map  412 , the constituents map  413 , the seeding map  414 , the topographic map  415 , and an other map  429 , along with the predictive nutrient model  450  which predicts a nutrient value based upon one or more of a soil property value, a yield value, a residue value, a constituent value, a seeding characteristic value, a topographic characteristic value, a vegetative index value, and an other characteristic value, and generates a predictive map that predicts a nutrient value at different locations in the field, such as functional predictive nutrient map  460 . 
     Predictive map generator  312  thus outputs a functional predictive nutrient map  460  that is predictive of a nutrient value. The functional predictive nutrient map  460  is a predictive map  264 . The functional predictive nutrient map  460  predicts a nutrient value at different locations in a field. The functional predictive nutrient map  460  may be provided to control zone generator  313 , control system  314 , or both. Control zone generator  313  generates control zones and incorporates those control zones into the functional predictive nutrient map  460  to produce a predictive control zone map  265 , that is a functional predictive nutrient control zone map  461 . One or both of functional predictive nutrient map  460  and functional predictive nutrient control zone map  461  may be provided to control system  314 , which generates control signals to control one or more of the controllable subsystems  316  based upon the functional predictive nutrient map  460 , the functional predictive nutrient control zone map  461 , or both. 
       FIG. 12  is a block diagram of a portion of the agricultural material application system architecture  300  shown in  FIG. 10 . Particularly,  FIG. 12  shows, among other things, examples of the predictive model generator  310  and the predictive map generator  312  in more detail.  FIG. 12  also illustrates information flow among the various components shown. The predictive model generator  310  receives one or more information map(s)  358 . In the example illustrated in  FIG. 12 , information maps  358  include one or more of a vegetative index map  416 , an optical map  417 , a weed map  418 , or any of a wide variety of other information maps  429 . Predictive model generator  310  also receives geographic location data  1434 , such as an indication of a geographic location, from geographic position sensor  304 . Geographic location data  1434  illustratively represents the geographic locations to which values detected by in-situ sensors  308  correspond. In some examples, the geographic position of the mobile machine  100 , as detected by geographic position sensors  304 , will not be the same as the geographic position on the field to which a value detected by in-situ sensors  308  corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor  304 , along with timing, machine speed and heading, machine dimensions, machine processing delays, sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., sensor field of view), as well as various other data, can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor  308  corresponds. 
     In-situ sensors  308  illustratively include weed sensors  372 , as well as processing system  338 . In some examples, processing system  338  is separate from in-situ sensors  308  (such as the example shown in  FIG. 10 ). In some instances, weed sensors  372  may be located on-board mobile material application machine  100 . The processing system  338  processes sensor data generated from weed sensors  372  to generate processed sensor data  1430  indicative of weed values. Weed sensors  372  and processing system  338  may sense one or more visual properties of weeds including color, size, and shape. Weed sensors  372  and processing system  338  may sense plant locations relative to known crop seed or plant locations or to known weed seed or plant locations. 
     As shown in  FIG. 12 , the example predictive model generator  310  includes a weed-to-vegetative index model generator  1440 , a weed-to-optical characteristic model generator  1441 , a weed-to-weed model generator  1442 , and a weed-to-other characteristic model generator  1445 . In other examples, the predictive model generator  310  may include additional, fewer, or different components than those shown in the example of  FIG. 12 . Consequently, in some examples, the predictive model generator  310  may include other items  1449  as well, which may include other types of predictive model generators to generate other types of weed models. 
     Weed-to-vegetative index model generator  1440  identifies a relationship between weed value(s) detected in in-situ sensor data  1430 , at geographic location(s) to which the weed value(s), detected in the in-situ sensor data  1430 , correspond, and vegetative index value(s) from the vegetative index map  416  corresponding to the same geographic location(s) to which the detected weed value(s) correspond. Based on this relationship established by weed-to-vegetative index model generator  1440 , weed-to-vegetative index model generator  1440  generates a predictive weed model. The predictive weed model is used by predictive weed map generator  1452  to predict a weed value at different locations in the field based upon the georeferenced vegetative index values contained in the vegetative index map  416  corresponding to the same locations in the field. Thus, for a given location in the field, a weed value can be predicted at the given location based on the predictive weed model and the vegetative index value, from the vegetative index map  416 , corresponding to that given location. 
     Weed-to-optical characteristic model generator  1441  identifies a relationship between weed value(s) detected in in-situ sensor data  1430 , at geographic location(s) to which the weed value(s), detected in the in-situ sensor data  1430 , correspond, and optical characteristic value(s) from the optical map  417  corresponding to the same geographic location(s) to which the detected weed value(s) correspond. Based on this relationship established by weed-to-optical characteristic model generator  1441 , weed-to-optical characteristic model generator  1441  generates a predictive weed model. The predictive weed model is used by predictive weed map generator  1452  to predict a weed value at different locations in the field based upon the georeferenced optical characteristic values contained in the optical map  417  corresponding to the same locations in the field. Thus, for a given location in the field, a weed value can be predicted at the given location based on the predictive weed model and the optical characteristic value, from the optical map  417 , corresponding to that given location. 
     Weed-to-weed model generator  1442  identifies a relationship between weed value(s) detected in in-situ sensor data  1430 , at geographic location(s) to which the weed value(s), detected in the in-situ sensor data  1430 , correspond, and weed value(s) from the weed map  418  corresponding to the same geographic location(s) to which the detected weed value(s) correspond. Based on this relationship established by weed-to-weed model generator  1442 , weed-to-weed model generator  1442  generates a predictive weed model. The predictive weed model is used by predictive weed map generator  1452  to predict a weed value at different locations in the field based upon the georeferenced weed values contained in the weed map  418  corresponding to the same locations in the field. Thus, for a given location in the field, a weed value can be predicted at the given location based on the predictive weed model and the weed value, from the weed map  418 , corresponding to that given location. 
     Weed-to-other characteristic model generator  1445  identifies a relationship between weed value(s) detected in in-situ sensor data  1430 , at geographic location(s) to which the weed value(s), detected in the in-situ sensor data  1430 , correspond, and other characteristic value(s) from an other map  429  corresponding to the same geographic location(s) to which the detected weed value(s) correspond. Based on this relationship established by weed-to-other characteristic model generator  1445 , weed-to-other characteristic model generator  1445  generates a predictive weed model. The predictive weed model is used by predictive weed map generator  1452  to predict a weed value at different locations in the field based upon the georeferenced other characteristic values contained in the other map  429  corresponding to the same locations in the field. Thus, for a given location in the field, a weed value can be predicted at the given location based on the predictive weed model and the other characteristic value, from the other map  429 , corresponding to that given location. 
     In light of the above, the predictive model generator  310  is operable to produce a plurality of predictive weed models, such as one or more of the predictive weed models generated by model generators  1440 ,  1441 ,  1442 ,  1445 , and  1449 . In another example, two or more of the predictive models described above may be combined into a single predictive weed model, such as a predictive weed model that predicts a weed value based upon two or more of the vegetative index values, the optical characteristic values, the weed values, and the other characteristic values at different locations in the field. Any of these weed models, or combinations thereof, are represented collectively by predictive weed model  1450  in  FIG. 12 . 
     The predictive weed model  1450  is provided to predictive map generator  312 . In the example of  FIG. 12 , predictive map generator  312  includes a predictive weed map generator  1452 . In other examples, predictive map generator  312  may include additional or different map generators. Thus, in some examples, predictive map generator  312  may include other items  1456  which may include other types of map generators to generate other types of maps. 
     Predictive weed map generator  1452  receives one or more of the vegetative index map  416 , the optical map  417 , the weed map  418 , and an other map  429 , along with the predictive weed model  1450  which predicts a weed value based upon one or more of a vegetative index value, an optical characteristic value, a weed value, and an other characteristic value, and generates a predictive map that predicts a weed value at different locations in the field, such as functional predictive weed map  1460 . 
     Predictive map generator  312  thus outputs a functional predictive weed map  1460  that is predictive of a weed value. The functional predictive weed map  1460  is a predictive map  264 . The functional predictive weed map  1460  predicts a weed value at different locations in a field. The functional predictive weed map  1460  may be provided to control zone generator  313 , control system  314 , or both. Control zone generator  313  generates control zones and incorporates those control zones into the functional predictive weed map  1460  to produce a predictive control zone map  265 , that is a functional predictive weed control zone map  1461 . One or both of functional predictive weed map  1460  and functional predictive weed control zone map  1461  may be provided to control system  314 , which generates control signals to control one or more of the controllable subsystems  316  based upon the functional predictive weed map  1460 , the functional predictive weed control zone map  1461 , or both. 
       FIG. 13  is a block diagram of a portion of the agricultural material application system architecture  300  shown in  FIG. 10 . Particularly,  FIG. 13  shows, among other things, examples of the predictive model generator  310  and the predictive map generator  312  in more detail.  FIG. 13  also illustrates information flow among the various components shown. The predictive model generator  310  receives one or more information map(s)  358 . In the example illustrated in  FIG. 13 , information maps  358  include one or more of a soil property map  410 , a topographic map  415 , vegetative index map  416 , a weed map  418 , a contamination map  419 , or any of a wide variety of other information maps  429 . Predictive model generator  310  also receives geographic location data  2434 , such as an indication of a geographic location, from geographic position sensor  304 . Geographic location data  2434  illustratively represents the geographic locations to which values detected by in-situ sensors  308  correspond. In some examples, the geographic position of the mobile machine  100 , as detected by geographic position sensors  304 , will not be the same as the geographic position on the field to which a value detected by in-situ sensors  308  corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor  304 , along with timing, machine speed and heading, machine dimensions, machine processing delays, sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., sensor field of view), as well as various other data, can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor  308  corresponds. 
     In-situ sensors  308  illustratively include material consumption sensors  376 , as well as processing system  338 . In some examples, processing system  338  is separate from in-situ sensors  308  (such as the example shown in  FIG. 10 ). In some instances, material consumption sensors  376  may be located on-board mobile material application machine  100 . The processing system  338  processes sensor data generated from material consumption sensors  376  to generate processed sensor data  2430  indicative of material consumption values. 
     As shown in  FIG. 13 , the example predictive model generator  310  includes a material consumption-to-soil property model generator  2440 , a material consumption-to-topographic characteristic model generator  2441 , a material consumption-to-vegetative index model generator  2442 , a material consumption-to-weed model generator  2443 , a material consumption-to-contamination model generator  2444 , and a material consumption-to-other characteristic model generator  2445 . In other examples, the predictive model generator  310  may include additional, fewer, or different components than those shown in the example of  FIG. 13 . Consequently, in some examples, the predictive model generator  310  may include other items  2449  as well, which may include other types of predictive model generators to generate other types of material consumption models. 
     Material consumption-to-soil property model generator  2440  identifies a relationship between material consumption value(s) detected in in-situ sensor data  2430 , at geographic location(s) to which the material consumption value(s), detected in the in-situ sensor data  2430 , correspond, and soil property value(s) from the soil property map  410  corresponding to the same geographic location(s) to which the detected material consumption value(s) correspond. Based on this relationship established by material consumption-to-soil property model generator  2440 , material consumption-to-soil property model generator  2440  generates a predictive material consumption model. The predictive material consumption model is used by predictive material consumption map generator  2452  to predict a material consumption value at different locations in the field based upon the georeferenced soil property values contained in the soil property map  410  corresponding to the same locations in the field. Thus, for a given location in the field, a material consumption value can be predicted at the given location based on the predictive material consumption model and the soil property value, from the soil property map  410 , corresponding to that given location. 
     Material consumption-to-topographic characteristic model generator  2441  identifies a relationship between material consumption value(s) detected in in-situ sensor data  2430 , at geographic location(s) to which the material consumption value(s), detected in the in-situ sensor data  2430 , correspond, and topographic characteristic value(s) from the topographic map  415  corresponding to the same geographic location(s) to which the detected material consumption value(s) correspond. Based on this relationship established by material consumption-to-topographic characteristic model generator  2441 , material consumption-to-topographic characteristic model generator  2441  generates a predictive material consumption model. The predictive material consumption model is used by predictive material consumption map generator  2452  to predict a material consumption value at different locations in the field based upon the georeferenced topographic characteristic values contained in the topographic map  415  corresponding to the same locations in the field. Thus, for a given location in the field, a material consumption value can be predicted at the given location based on the predictive material consumption model and the topographic characteristic value, from the topographic map  415 , corresponding to that given location. 
     Material consumption-to-vegetative index model generator  2442  identifies a relationship between material consumption value(s) detected in in-situ sensor data  2430 , at geographic location(s) to which the material consumption value(s), detected in the in-situ sensor data  2430 , correspond, and vegetative index value(s) from the vegetative index map  416  corresponding to the same geographic location(s) to which the detected material consumption value(s) correspond. Based on this relationship established by material consumption-to-vegetative index model generator  2442 , material consumption-to-vegetative index model generator  2442  generates a predictive material consumption model. The predictive material consumption model is used by predictive material consumption map generator  2452  to predict a material consumption value at different locations in the field based upon the georeferenced vegetative index values contained in the vegetative index map  416  corresponding to the same locations in the field. Thus, for a given location in the field, a material consumption value can be predicted at the given location based on the predictive material consumption model and the vegetative index value, from the vegetative index map  416 , corresponding to that given location. 
     Material consumption-to-weed model generator  2443  identifies a relationship between material consumption value(s) detected in in-situ sensor data  2430 , at geographic location(s) to which the material consumption value(s), detected in the in-situ sensor data  2430 , correspond, and weed value(s) from the weed map  418  corresponding to the same geographic location(s) to which the detected material consumption value(s) correspond. Based on this relationship established by material consumption-to-weed model generator  2443 , material consumption-to-weed model generator  2442  generates a predictive material consumption model. The predictive material consumption model is used by predictive material consumption map generator  2452  to predict a material consumption value at different locations in the field based upon the georeferenced weed values contained in the weed map  418  corresponding to the same locations in the field. Thus, for a given location in the field, a material consumption value can be predicted at the given location based on the predictive material consumption model and the weed value, from the weed map  418 , corresponding to that given location. 
     Material consumption-to-contamination model generator  2444  identifies a relationship between material consumption value(s) detected in in-situ sensor data  2430 , at geographic location(s) to which the material consumption value(s), detected in the in-situ sensor data  2430 , correspond, and contamination value(s) from the contamination map  419  corresponding to the same geographic location(s) to which the detected material consumption value(s) correspond. Based on this relationship established by material consumption-to-contamination model generator  2444 , material consumption-to-contamination model generator  2444  generates a predictive material consumption model. The predictive material consumption model is used by predictive material consumption map generator  2452  to predict a material consumption value at different locations in the field based upon the georeferenced contamination values contained in the contamination map  419  corresponding to the same locations in the field. Thus, for a given location in the field, a material consumption value can be predicted at the given location based on the predictive material consumption model and the contamination value, from the contamination map  419 , corresponding to that given location. 
     Material consumption-to-other characteristic model generator  2445  identifies a relationship between material consumption value(s) detected in in-situ sensor data  2430 , at geographic location(s) to which the material consumption value(s), detected in the in-situ sensor data  2430 , correspond, and other characteristic value(s) from an other map  429  corresponding to the same geographic location(s) to which the detected material consumption value(s) correspond. Based on this relationship established by material consumption-to-other characteristic model generator  2445 , material consumption-to-other characteristic model generator  2445  generates a predictive material consumption model. The predictive material consumption model is used by predictive material consumption map generator  2452  to predict a material consumption value at different locations in the field based upon the georeferenced other characteristic values contained in the other map  429  corresponding to the same locations in the field. Thus, for a given location in the field, a material consumption value can be predicted at the given location based on the predictive material consumption model and the other characteristic value, from the other map  429 , corresponding to that given location. 
     In light of the above, the predictive model generator  310  is operable to produce a plurality of predictive material consumption models, such as one or more of the predictive material consumption models generated by model generators  2440 ,  2441 ,  2442 ,  2443 ,  2444 ,  2445 , and  2449 . In another example, two or more of the predictive models described above may be combined into a single predictive material consumption model, such as a predictive material consumption model that predicts a material consumption value based upon two or more of the soil property values, the topographic characteristic values, the vegetative index values, the weed values, the contamination values, and the other characteristic values at different locations in the field. Any of these material consumption models, or combinations thereof, are represented collectively by predictive material consumption model  2450  in  FIG. 13 . 
     The predictive material consumption model  2450  is provided to predictive map generator  312 . In the example of  FIG. 13 , predictive map generator  312  includes a predictive material consumption map generator  2452 . In other examples, predictive map generator  312  may include additional or different map generators. Thus, in some examples, predictive map generator  312  may include other items  2456  which may include other types of map generators to generate other types of maps. 
     Predictive material consumption map generator  2452  receives one or more of the soil property map  410 , the topographic map  415 , the vegetative index map  416 , the weed map  418 , the contamination map  419 , and an other map  429 , along with the predictive material consumption model  2450  which predicts a material consumption value based upon one or more of a soil property value, a topographic characteristic value, a vegetative index value, a weed value, a contamination value, and an other characteristic value, and generates a predictive map that predicts a material consumption value at different locations in the field, such as functional predictive material consumption map  2460 . 
     Predictive map generator  312  thus outputs a functional predictive material consumption map  2460  that is predictive of a material consumption value. The functional predictive material consumption map  2460  is a predictive map  264 . The functional predictive material consumption map  2460  predicts a material consumption value at different locations in a field. The functional predictive material consumption map  2460  may be provided to control zone generator  313 , control system  314 , or both. Control zone generator  313  generates control zones and incorporates those control zones into the functional predictive material consumption map  2460  to produce a predictive control zone map  265 , that is a functional predictive material consumption control zone map  2461 . One or both of functional predictive material consumption map  2460  and functional predictive material consumption control zone map  2461  may be provided to control system  314 , which generates control signals to control one or more of the controllable subsystems  316  based upon the functional predictive material consumption map  2460 , the functional predictive material consumption control zone map  2461 , or both. 
       FIG. 14  is a block diagram of a portion of the agricultural material application system architecture  300  shown in  FIG. 10 . Particularly,  FIG. 14  shows, among other things, examples of the predictive model generator  310  and the predictive map generator  312  in more detail.  FIG. 14  also illustrates information flow among the various components shown. The predictive model generator  310  receives one or more information map(s)  358 . In the example illustrated in  FIG. 14 , information maps  358  can include one or more of the information maps  358  discussed herein, as well as various other types of information maps. Predictive model generator  310  also receives geographic location data  3434 , such as an indication of a geographic location, from geographic position sensor  304 . Geographic location data  3434  illustratively represents the geographic locations to which values detected by in-situ sensors  308  correspond. In some examples, the geographic position of the mobile machine  100 , as detected by geographic position sensors  304 , will not be the same as the geographic position on the field to which a value detected by in-situ sensors  308  corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor  304 , along with timing, machine speed and heading, machine dimensions, machine processing delays, sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., sensor field of view), as well as various other data, can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor  308  corresponds. 
     In-situ sensors  308  illustratively include heading/speed sensors  325 , as well as processing system  338 . In some examples, processing system  338  is separate from in-situ sensors  308  (such as the example shown in  FIG. 10 ). In some instances, heading/speed sensors  325  may be located on-board mobile material application machine  100 . The processing system  338  processes sensor data generated from heading/speed sensors  325  to generate processed sensor data  3430  indicative of speed characteristic values. 
     As shown in  FIG. 14 , the example predictive model generator  310  includes a speed characteristic-to-mapped characteristic(s) model generator  2440 . In other examples, the predictive model generator  310  may include additional, fewer, or different components than those shown in the example of  FIG. 14 . Consequently, in some examples, the predictive model generator  310  may include other items  3449  as well, which may include other types of predictive model generators to generate other types of speed models. 
     Speed characteristic-to-mapped characteristic(s) model generator  3440  identifies a relationship between speed characteristic value(s) detected in in-situ sensor data  3430 , at geographic location(s) to which the speed characteristic value(s), detected in the in-situ sensor data  3430 , correspond, and value(s) of one or more characteristics from the one or more information maps  358  corresponding to the same geographic location(s) to which the detected speed characteristic value(s) correspond. Based on this relationship established by speed characteristic-to-mapped characteristic(s) model generator  3440 , speed characteristic-to-mapped characteristic(s) model generator  3440  generates a predictive speed model. The predictive speed model is used by predictive speed map generator  3452  to predict a speed characteristic value at different locations in the field based upon the georeferenced values of the one or more characteristics contained in the one or more information maps  358  corresponding to the same locations in the field. Thus, for a given location in the field, a speed characteristic value can be predicted at the given location based on the predictive speed model and the value of one or more characteristics, from the one or more information maps  358 , corresponding to that given location. 
     In light of the above, the predictive model generator  310  is operable to produce a plurality of predictive speed models, such as one or more of the predictive speed models generated by model generators  3440  and  3449 . In another example, two or more of the predictive models described above may be combined into a single predictive speed model, such as a predictive speed model that predicts a speed characteristic value based upon values of two or more characteristics at different locations in the field. Any of these speed models, or combinations thereof, are represented collectively by predictive speed model  3450  in  FIG. 14 . 
     The predictive speed model  3450  is provided to predictive map generator  312 . In the example of  FIG. 14 , predictive map generator  312  includes a predictive speed map generator  3452 . In other examples, predictive map generator  312  may include additional or different map generators. Thus, in some examples, predictive map generator  312  may include other items  3456  which may include other types of map generators to generate other types of maps. 
     Predictive speed map generator  3452  receives one or more of the information maps  358 , along with the predictive speed model  3450  which predicts a speed characteristic value based upon a value of one or more characteristics, and generates a predictive map that predicts a speed characteristic value at different locations in the field, such as functional predictive speed map  3460 . 
     Predictive map generator  312  thus outputs a functional predictive speed map  3460  that is predictive of a speed characteristic value. The functional predictive speed map  3460  is a predictive map  264 . The functional predictive speed map  3460  predicts a speed characteristic value at different locations in a field. The functional predictive speed map  3460  may be provided to control zone generator  313 , control system  314 , or both. Control zone generator  313  generates control zones and incorporates those control zones into the functional predictive speed map  3460  to produce a predictive control zone map  265 , that is a functional predictive speed control zone map  3461 . One or both of functional predictive speed map  3460  and functional predictive speed control zone map  3461  may be provided to control system  314 , which generates control signals to control one or more of the controllable subsystems  316  based upon the functional predictive speed map  3460 , the functional predictive speed control zone map  3461 , or both. 
     In some cases, where the mobile machine  100  is to be controlled based on a functional predictive map or a functional predictive control zone map, or both, multiple target settings for the same actuator may be possible at a given location. In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the actuators. For example, where the actuator is an actuator in propulsion subsystem  350  that is being controlled in order to control the speed of mobile machine  100 , there may be multiple target speed settings. In such a case, control zone generator  313  may select one of the competing target settings to control the mobile machine. Thus, in generating the functional predictive control zone map that is eventually provided to the control system, operator, or user, for control of the mobile machine, control zone generator  313  may first resolve competing target settings of competing control zones. Control zone generator  313  may select the competing settings based on a number of criteria, for example, various performance metrics such as time to complete, job quality, fuel cost, labor cost, etc. may be used. There may be a hierarchy of these criteria which can be selectively adjusted, such as based on operator or user input, or based on default rankings. As an example, time to complete may be input as the highest priority in the hierarchy, and thus the target setting corresponding to time to complete will be selected. This is merely one example. In other examples, characteristics of the information maps  358  used in the generation of the functional predictive map and functional predictive control zone map may have a priority or hierarchy. For example, target settings based on yield values from a yield map may have a higher priority than target settings based on topographic values from a topographic map, and thus, the value corresponding to the yield value will be selected over the values corresponding to the topographic value. This is merely one example. In either case, it will be understood that control zone generator  313  can resolve competing target settings such that the control zone map that is generated and provided for control does not contain competing target settings. 
       FIGS. 15A-15B  (collectively referred to herein as  FIG. 15 ) show a flow diagram illustrating one example of the operation of agricultural material application architecture  300  in generating a predictive model and a predictive map. 
     At block  502 , agricultural material application system  300  receives one or more information maps  358 . Examples of information maps  358  or receiving information maps  358  are discussed with respect to blocks  504 ,  505 ,  507 , and  508 . As discussed above, information maps  358  map values of a variable, corresponding to a characteristic, to different locations in the worksite, as indicated at block  505 . As indicated at block  504 , receiving the information maps  358  may involve selecting one or more of a plurality of possible information maps  358  that are available. For instance, one information map  358  may be a soil property map, such as soil property map  410 . Another information map  358  may be a yield map, such as yield map  411 . Another information map  358  may be a residue map, such as residue map  412 . Another information map  358  may be a constituents map, such as constituents map  413 . Another information map  358  may be a seeding map, such as seeding map  414 . Another information map  358  may be a topographic map, such as topographic map  415 . Another information map  358  may be a vegetative index map, such as vegetative index map  416 . Another information map  358  may be an optical map, such as optical map  417 . Another information map  358  may be a weed map, such as weed map  418 . Another information map  358  may be a contamination map, such as contamination map  419 . Information maps  358  may include various other types of information maps that map various other characteristics, such as other maps  429 . 
     The process by which one or more information maps  358  are selected can be manual, semi-automated, or automated. The information maps  358  can be based on data collected prior to a current operation, as indicated by block  506 . For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current season, or at other times. The data may be based on data detected in ways other than using aerial images. For instance, the data may be collected during a previous operation on the worksite, such an operation during a previous year, or a previous operation earlier in the current season, or at other times. The machines performing those previous operations may be outfitted with one or more sensors that generate sensor data indicative of one or more characteristics. In other examples, and as described above, the information maps  358  may be predictive maps having predictive values. The predictive information map  358  can be generated during a current operation by predictive map generator  312  based on a model generated by predictive model generator  310 , as indicated by block  506 . The predictive information map  358  can be predicted in other ways (before or during the current operation), such as based on other measured values. The data for the information maps  358  can be obtained by predictive model generator  310  and predictive map generator  312  using communication system  306  and stored in data store  302 . The data for the information maps  358  can be obtained by material application system  300  using a communication system in other ways as well, and this is indicated by block  507  in the flow diagram of  FIG. 15 . 
     As material application machine  100  is operating, in-situ sensors  308  generate sensor data indicative of one or more in-situ data values indicative of one or more characteristics, as indicated by block  508 . For example, nutrient sensors  374  generate sensor data indicative of one or more in-situ nutrient values as indicated by block  509 . Weed sensors  372  generate sensor data indicative of one or more in-situ weed values as indicated by block  510 . Material consumption sensors  376  generate sensor data indicative of one or more in-situ material consumption values as indicated by block  511 . Heading/speed sensors  325  generate sensor data indicative of one or more in-situ speed characteristic values as indicated by block  512 . In some examples, data from in-situ sensors  308  is georeferenced using position data from geographic position sensor  304  as well as, in some examples, one or more of heading data, travel speed data, machine latency data, sensor position and parameter data, as well as various other data. 
     At block  513 , predictive model generator  310  controls one or more model generators to generate one or more models that model the relationship between mapped values and values sensed by in-situ sensors  308 . 
     For instance, in one example, predictive model generator  310  controls one or more of the model generators  441 ,  442 ,  443 ,  444 ,  445 ,  446 ,  448 , and  449  to generate a predictive nutrient model that models the relationship between the mapped values, such as one or more of the soil property values, the yield values, the residue values, the constituent values, the seeding characteristic values, the topographic characteristic values, the vegetative index values, and the other characteristic values contained in the respective information map and the in-situ nutrient values sensed by in-situ sensors  308 . Predictive model generator  310  thus generates a predictive nutrient model, such as predictive nutrient model  450 , as indicated by block  514 . 
     In another example, predictive model generator controls one or more of the model generators  1440 ,  1441 ,  1442 ,  1445 , and  1449  to generate a predictive weed model that models the relationship between the mapped values, such as one or more of the vegetative index values, the optical characteristic values, the weed values, and the other characteristic values contained in the respective information map and the in-situ weed values sensed by in-situ sensors  308 . Predictive model generator  310  thus generates a predictive weed model, such as predictive weed model  1450 , as indicated by block  515 . 
     In another example, predictive model generator controls one or more of the model generators  2440 ,  2441 ,  2442 ,  2443 ,  2444 ,  2445 , and  2449  to generate a predictive material consumption model that models the relationship between the mapped values, such as one or more of the soil property values, the topographic characteristic values, the vegetative index values, the weed values, the contamination values, and the other characteristic values contained in the respective information map and the in-situ material consumption values sensed by the in-situ sensors  308 . Predictive model generator  310  thus generates a predictive material consumption model, such as predictive material consumption model  2450 , as indicated by block  516 . 
     In another example, predictive model generator controls one or more of the model generators  3440  and  3449  to generate a predictive speed model that models the relationship between mapped values, such as values of one or more characteristics in one or more information maps  358 , and the in-situ speed characteristic values sensed by in-situ sensors  308 . Predictive model generator  310  thus generates a predictive speed model, such as predictive speed model  3450 , as indicated by block  517 . 
     The relationship(s) or model(s) generated by predictive model generator  310  are provided to predictive map generator  312 . 
     Predictive map generator  312 , at block  518 , controls one or more predictive map generators to generate one or more functional predictive maps based on the relationship(s) or model(s) generated by predictive model generator  310  and one or more of the information maps  358 . 
     For instance, in one example, predictive map generator  312  controls predictive nutrient map generator  452  to generate a predictive nutrient map, such as functional predictive nutrient map  460 , that predicts nutrient values (or sensor value(s) indictive of nutrient values) at different geographic locations in a worksite at which material application machine  100  is operating using the predictive nutrient model  450  and one or more of the information maps  358 , such as one or more of soil property map  410 , yield map  411 , residue map  412 , constituents map  413 , seeding map  414 , topographic map  415 , vegetative index map  416 , and an other map  429 . Generating a predictive nutrient map, such as functional predictive nutrient map  460  is indicated by block  519 . 
     It should be noted that, in some examples, the functional predictive nutrient map  460  may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive nutrient map  460  that provides two or more of a map layer that provides predictive nutrient values based on soil property values from soil property map  410 , a map layer that provides predictive nutrient values based on yield values from yield map  411 , a map layer that provides predictive nutrient values based on residue values from residue map  412 , a map layer that provides predictive nutrient values based on constituent values from constituents map  413 , a map layer that provides predictive nutrient values based on seeding characteristic value from seeding map  414 , a map layer that provides predictive nutrient values based on topographic characteristic values from topographic map  415 , a map layer that provides predictive nutrient values based on vegetative index values from vegetative index map, and a map layer that provides predictive nutrient values based on other characteristic values from an other map  429 . In some examples, the functional predictive nutrient map  460  may include a map layer that provides predictive nutrient values based on two or more of soil property values from soil property map  410 , yield values from yield map  411 , residue values from residue map  412 , constituent values from constituents map  413 , seeding characteristic values from seeding map  414 , topographic characteristic values from topographic map  415 , vegetative index values from vegetative index map  416 , and other characteristic values from an other map  429 . 
     In one example, predictive map generator  312  controls predictive weed map generator  1452  to generate a predictive weed map, such as functional predictive weed map  1460 , that predicts weed values (or sensor value(s) indictive of weed values) at different geographic locations in a worksite at which material application machine  100  is operating using the predictive weed model  1450  and one or more of the information maps  358 , such as one or more of vegetative index map  416 , optical map  417 , weed map  418 , and an other map  429 . Generating a predictive weed map, such as functional predictive weed map  1460  is indicated by block  520 . 
     It should be noted that, in some examples, the functional predictive weed map  1460  may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive weed map  1460  that provides two or more of a map layer that provides predictive weed values based on vegetative index values from vegetative index map  416 , a map layer that provides predictive weed values based on optical characteristic values from optical map  417 , a map layer that provides predictive weed values based on weed values from weed map  418 , and a map layer that provides predictive weed values based on other characteristic values from an other map  429 . In some examples, the functional predictive weed map  1460  may include a map layer that provides predictive weed values based on two or more of vegetative index values from vegetative index map  416 , optical characteristic values from optical map  417 , weed values from weed map  418 , and other characteristic values from an other map  429 . 
     In one example, predictive map generator  312  controls predictive material consumption map generator  2452  to generate a predictive material consumption map, such as functional predictive material consumption map  2460 , that predicts material consumption values (or sensor value(s) indictive of material consumption values) at different geographic locations in a worksite at which material application machine  100  is operating using the predictive material consumption model  2450  and one or more of the information maps  358 , such as one or more of soil property map  410 , topographic map  415 , vegetative index map  416 , weed map  418 , contamination map  419 , and an other map  429 . Generating a predictive material consumption map, such as functional predictive material consumption map  2460  is indicated by block  521 . 
     It should be noted that, in some examples, the functional predictive material consumption map  2460  may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive material consumption map  2460  that provides two or more of a map layer that provides predictive material consumption values based on soil property values from soil property map  410 , a map layer that provides predictive material consumption values based on topographic characteristic values from topographic map  415 , a map layer that provides predictive material consumption values based on vegetative index values from vegetative index map  416 , a map layer that provides predictive material consumption values based on weed values from weed map  418 , a map layer that provides predictive material consumption values based on contamination values from contamination map  419 , and a map layer that provides predictive material consumption values based on other characteristic values from an other map  429 . In some examples, the functional predictive material consumption map  2460  may include a map layer that provides predictive material consumption values based on two or more of soil property values from soil property map  410 , topographic characteristic values from a topographic map  415 , vegetative index values from vegetative index map  416 , weed values from weed map  418 , contamination values from contamination map  419 , and other characteristic values from an other map  429 . 
     In one example, predictive map generator  312  controls predictive speed map generator  3452  to generate a predictive speed map, such as functional predictive speed map  3460 , that predicts speed characteristic values (or sensor value(s) indictive of speed characteristic values) at different geographic locations in a worksite at which material application machine  100  is operating using the predictive speed model  3450  and one or more of the information maps  358 . Generating a predictive speed map, such as functional predictive speed map  3460  is indicated by block  522 . 
     It should be noted that, in some examples, the functional predictive speed map  2460  may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive material consumption map  2460  that provides two or more map layers, each map layer providing predictive speed characteristic values based on a respective characteristic, for instance a map layer that provides predictive speed characteristic values based on values of a first characteristic from a first information map  358  and a map layer that provides predictive speed characteristic values based on values of a second characteristic from a second information map  358 . In some examples, the functional predictive speed map  3460  may include a map layer that provides predictive speed characteristic values based on values of two or more characteristics, such as a map layer that provides predictive speed characteristic values based on values of a first characteristic from a first information map  358  and values of a second characteristic from a second information map  358 . 
     At block  523 , predictive map generator  312  configures the functional predictive map(s) (e.g., one or more of  460 ,  1460 ,  2460 , and  3460 ) so that the functional predictive map(s) are actionable (or consumable) by control system  314 . Predictive map generator  312  can provide the functional predictive map(s) to the control system  314  or to control zone generator  313 , or both. Some examples of the different ways in which the functional predictive map(s) (e.g., one or more of  460 ,  1460 ,  2460 , and  3460 ) can be configured or output are described with respect to blocks  523 ,  524 ,  525 , and  526 . For instance, predictive map generator  312  configures one or more of the functional predictive maps so that the one or more functional predictive maps include values that can be read by control system  314 , and used as the basis for generating control signals for one or more of the different controllable subsystems  316 , as indicated by block  523 . 
     At block  524 , control zone generator  313  can divide the functional predictive maps into control zones based on the values on the functional predictive maps to generated functional predictive maps with control zones. In one example, control zone generator  313  can divide the functional predictive nutrient map  460  into control zones based on the values on the functional predictive nutrient map  460  to generate functional predictive nutrient control zone map  461 . In another example, control zone generator  313  can divide the functional predictive weed map  1460  into control zones based on the values on the functional predictive weed map  1460  to generate functional predictive weed control zone map  1461 . In another example, control zone generator  313  can divide the functional predictive material consumption map  2460  into control zones based on the values on the functional predictive material consumption map  2460  to generate functional predictive material consumption control zone map  2461 . In another example, control zone generator  313  can divide the functional predictive speed map  3460  into control zones based on the values on the functional predictive seed map  3460  to generate functional predictive speed control zone map  3461 . 
     Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system, the controllable subsystems, based on wear considerations, or on other criteria. 
     At block  525 , predictive map generator  312  configures one or more of the functional predictive maps (e.g., one or more of  460 ,  1460 ,  2460 , and  3460 ) or one or more of the functional predictive control zone maps (e.g., one or more of  461 ,  1461 ,  2461 , and  3461 ), or both, for presentation to an operator or other user. When presented to an operator or other user, the presentation of the one or more functional predictive maps or of the one or more functional predictive control zone maps, or both, may contain one or more of the predictive values on the one or more functional predictive maps correlated to geographic location, the control zones of the one or more functional predictive control zone maps correlated to geographic location, and settings values or control parameters that are used based on the predicted values on the one or more functional predictive maps or control zones on the one or more functional predictive control zone maps. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on the one or more functional predictive maps or the control zones on the one or more predictive control zone maps, or both, conform to measured values that may be measured by sensors on material application machine  100  as material application machine  100  operates at the worksite. Further where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display elements are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of material application machine  100  may be unable to see the information corresponding to the one or more functional predictive maps or the one or more functional predictive control zone maps, or both, or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the one or more functional predictive maps or the one or more functional predictive control zone maps, or both, on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on the one or more functional predictive maps or the one or more functional predictive control zone maps, or both, and also be able to change the one or more functional predictive maps or the one or more functional predictive control zone maps, or both. In some instances, the one or more functional predictive maps or the one or more functional predictive control zone maps, or both, accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. The one or more functional predictive maps or the one or more functional predictive control zone maps, or both, can be configured in other ways as well, as indicated by block  526 . 
     At block  527 , input from geographic position sensor  304  and other in-situ sensors  308  are received by the control system  314 . Particularly, at block  528 , control system  314  detects an input from the geographic position sensor  304  identifying a geographic location of material application machine  100 . Block  529  represents receipt by the control system  314  of sensor inputs indicative of trajectory or heading of material application machine  100 , and block  530  represents receipt by the control system  314  of a speed of material application machine  100 . Block  531  represents receipt by the control system  314  of other information from various in-situ sensors  308 . 
     At block  532 , control system  314  generates control signals to control the controllable subsystems  316  (or to other items) based on the one or more functional predictive maps (e.g., one or more of  460 ,  1460 ,  2460 , and  3460 ) or the one or more functional predictive control zone maps (e.g., one or more of  461 ,  1461 ,  2461 , and  3461 ), or both, and the input from the geographic position sensor  304  and any other in-situ sensors  308 . At block  534 , control system  314  applies the control signals to the controllable subsystems  316  (or to other items). It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems  316  (or other items) that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems  316  (or other items) that are controlled may be based on the type(s) of the functional predictive map(s) or the functional predictive control zone map(s), or both, that is being used. Similarly, the control signals that are generated and the controllable subsystems  316  (or other items) that are controlled and the timing of the control signals can be based on various latencies of material application machine  100  and the responsiveness of the controllable subsystems  316  (or other items). 
     As an example, communication system controller  329  can provide control signals to control communication system  306  based on the functional predictive map(s) or the functional predictive control zone map(s), or both. For instance, communication system controller can provide control signals to control communication system  306  to communicate the functional predictive map(s) or functional predictive control zone map(s), or both, or data based thereon to other items of material application system  300 . 
     As another example, interface controller  330  can generate control signals to control an interface mechanism, such as an operator interface mechanism  318  or a user interface mechanisms  364 , or both, based on or indicative of the functional predictive map(s) or functional predictive control zone map(s), such as to display the functional predictive map(s) or the functional predictive control zone map(s), or both. 
     As another example, material application controller  331  can generate control signals to control one or more material application actuators  340  to control application of material (e.g., the amount of material that is applied, whether or not material is applied, etc.) based on the functional predictive map(s) or the functional predictive control zone map(s), or both. 
     As another example, propulsion controller  334  can generate control signals to control propulsion subsystem  350  to control a speed characteristic (e.g., a travel speed, an acceleration, a deceleration, etc.) of material application machine  100  based on the functional predictive map(s) or the functional predictive control zone map(s), or both. 
     As another example, path planning controller  335  can generate control signals to control steering subsystem  352  to control a heading of material application machine  100  based on the functional predictive map(s) or the functional predictive control zone map(s), or both. 
     As another example, logistics module  315  can generate logistics control signals to control one or more controllable subsystems  316  or various other items of material application system  300  based on the functional predictive map(s) or the functional predictive control zone map(s), or both. Logistics module  315  will be discussed in more detail in  FIGS. 16-17 . 
     These are merely some examples. Control system  314  can generate any of a number of control signals to control any of a number of items of material application system  300 . 
     At block  536 , a determination is made as to whether the operation has been completed. If the operation is not completed, the processing advances to block  538  where in-situ sensor data from geographic position sensor  304  and in-situ sensors  308  (and perhaps other sensors) continue to be read and further generation and application of control signals can be performed based on the inputs at block  538  and functional predictive map(s) or the functional predictive control zone map(s), or both. 
     In some examples, at block  540 , material application system  300  can also detect learning trigger criteria to perform machine learning on one or more of the one or more functional predictive maps (e.g., one or more of  460 ,  1460 ,  2460 , and  3460 ), the one or more functional predictive control zone maps (e.g., one or more of  461 ,  1461 ,  2461 , and  3461 ), the one or more predictive models (e.g., one or more of  450 ,  1450 ,  2450 , and  3450 ), the one or more zones generated by control zone generator  313 , the one or more control algorithms implemented by the controllers in the control system  314 , and other triggered learning. 
     The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks  542 ,  544 ,  546 ,  548 , and  549 . For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors  308 . In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors  308  that exceeds a threshold trigger or causes the predictive model generator  310  to generate a new predictive model that is used by predictive map generator  312 . Thus, as material application machine  100  continues an operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors  308  triggers the creation of a new relationship represented by one or more new predictive models generated by predictive model generator  310 . Further, one or more new functional predictive maps, one or more new functional predictive control zone maps, or both, can be generated using the respective one or more new predictive models. Block  542  represents detecting a threshold amount of in-situ sensor data used to trigger creation of one or more new predictive models. 
     In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors  308  are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in the one or more information maps  358 ) are within a selected range or is less than a defined amount, or below a threshold value, then one or more new predictive models are not generated by the predictive model generator  310 . As a result, the predictive map generator  312  does not generate one or more new functional predictive maps, one or more new functional predictive control zone maps, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator  310  generates one or more new predictive models using all or a portion of the newly received in-situ sensor data that the predictive map generator  312  uses to generate one or more new functional predictive maps which can be provided to control zone generator  313  for the creation of one or more new functional predictive control zone maps. At block  544 , variations in the in-situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation of the relationship between the in-situ sensor data and the information in the one or more information maps, can be used as a trigger to cause generation of one or more of one or more new predictive models, one or more new functional predictive maps, and one or more new functional predictive control zone maps. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways. 
     Other learning trigger criteria can also be used. For instance, if predictive model generator  310  switches to a different information map (different from the originally selected information map), then switching to the different information map may trigger re-learning by predictive model generator  310 , predictive map generator  312 , control zone generator  313 , control system  314 , or other items. In another example, transitioning of material application machine  100  to a different topography, a different control zone, a different region of the worksite, a different area with different grouped characteristics (such as a different crop genotype area) may be used as learning trigger criteria as well. 
     In some instances, an operator  360  or user  366  can also edit the functional predictive map(s) or functional predictive control zone map(s), or both. The edits can change a value on the functional predictive map(s), change a size, shape, position, or existence of a control zone on functional predictive control zone map(s), or both. Block  546  shows that edited information can be used as learning trigger criteria. 
     In some instances, it may also be that an operator  360  or user  366  observes that automated control of a controllable subsystem, is not what the operator or user desires. In such instances, the operator  360  or user  366  may provide a manual adjustment to the controllable subsystem reflecting that the operator  360  or user  366  desires the controllable subsystem to operate in a different way than is being commanded by control system  314 . Thus, manual alteration of a setting by the operator or user can cause one or more of predictive model generator  310  to relearn one or more predictive models, predictive map generator  312  to generate one or more new functional predictive maps, control zone generator  313  to generate one or more new control zones on one or more functional predictive maps, and a control system to relearn a control algorithm or to perform machine learning on one or more of the controllers in the control system based upon the adjustment by the operator or user, as shown in block  548 . Block  549  represents the use of other triggered learning criteria. 
     In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated by block  550 . 
     If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block  550 , then one or more of the predictive model generator  310 , predictive map generator  312 , control zone generator  313 , control system  314  performs machine learning to generate new predictive model(s), new functional predictive map(s), new control zone(s), and new control algorithm(s), respectively, based upon the learning trigger criteria. The new predictive model(s), the new functional predictive map(s), the new control zone(s), and the new control algorithm(s) are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block  552 . 
     If the operation has been completed, operation moves from block  552  to block  554  where one or more of the functional predictive map(s), the functional predictive control zone map(s), the predictive model(s), the control zone(s), and the control algorithm(s) are stored. The functional predictive map(s), the functional predictive control zone map(s), the predictive model(s), the control zone(s), and the control algorithm(s) may be stored locally on a data store of a machine or stored remotely for later use. 
     If the operation has not been completed, operation returns to block  523  such that the new functional predictive map(s), the new functional predictive control zone map(s), the new control zone(s), and/or the new control algorithm(s) can be used to control the material application machine  100  or other items of material application system  300 , or both. 
       FIG. 16  is a block diagram of a portion of agricultural material application system  300  shown in  FIG. 10 , in more detail. Particularly,  FIG. 16  shows examples of the logistics module  315  in more detail.  FIG. 16  also illustrates information flow among the various components shown. 
     As illustrated in  FIG. 16 , logistics module  315  receives one or more material consumption maps  602 , one or more speed maps  604 , one or more functional predictive maps  263 , one or more information maps  358 , sensor data  606 , material application machine dimensional data  608 , route data  610 , material delivery vehicle data  612 , threshold data  614 , and various other data  616 , such as, but not limited to, other operator or user inputs. 
     Material consumption maps  602  can include functional predictive material consumption map  2460 , functional predictive material consumption control zone map  2461 , as well as other material consumption maps  603 , such as other types of predictive material consumption maps or prescriptive material consumption maps. 
     Speed maps  604  can include functional predictive speed map  3460 , functional predictive speed control zone map  3461 , as well as other speed maps  605 , such as other types of predictive speed maps or prescriptive speed maps. 
     Functional predictive maps  263 , in the example illustrated in  FIG. 16 , can include predictive maps  264 , such as functional predictive nutrient map  460  or functional predictive weed map  1460 , and predictive maps with control zones  265 , such as functional predictive nutrient control zone map  461  or functional predictive weed control zone map  1461 . 
     Information maps  358  can include any of the information maps  358  discussed herein as well as various other information maps that map values of various other characteristics. For example, information maps  358  can also include prescriptive material application maps. A prescriptive material application map can be used in the control of machine  100  at the field. A prescriptive material application map various machine settings values, such as material application settings (e.g., material application rate settings) across different geographic locations in a field of interest. 
     Sensor data  606  includes data generated by or derived from in-situ sensors  308 . 
     Material application machine dimensional data  608  can include data indicative of the volumetric capacity or weight capacity of the material containers of material application machine  100  (e.g., tanks  107 , tanks  110 , tanks  112 , tanks  234 , tanks  255 , etc.). Material application machine dimensional data  608  may also include the width of a towed implement, a spray boom, a spray nozzle coverage pattern, a spreader throw zone, or any other width associated with material application perpendicular to the direction of travel of the material application machine  100 . 
     Route data  610  can include data indicative of a planned or prescribed route of material application machine  100  at the field, including data indicative of the route already travelled. Route data  610  can also include data indicative of a route from a location at which a material delivery vehicle  379  is located to the field or to a particular location on the field. In some examples, the route data  610  may be input or provided by an operator or user. In some examples, route data  610  may be output by control system  314 , such as by path planning controller  335 . In some examples, the route data  610  may be in the form of an information map  358 , such as a route map that maps a planned or prescribed route of material application machine  100  at the field. 
     Material delivery vehicle data  612  can include data indicative of a location of a material delivery vehicle  379 . a heading or speed, or both of material delivery vehicle  363 , as well as various other data. Material delivery vehicle  379  may have on-board sensors that provide such data which can be provided to logistics module  315  over network  359 . 
     Threshold data  614  includes data indicative of various thresholds, such as threshold material empty levels, as well as various other thresholds. Such threshold data can be provided by an operator or user or otherwise generated by control system  314 . 
     Preferred material delivery location data  615  includes data indicative of preferred or commanded material delivery location(s). That is, location(s) at the worksite where the material delivery vehicle  363  and the material application machine  100  are to be located to perform a material delivery operation. Such locations could include, for example, headlands, ends of rows, outside of the area of the field where material is to be applied, near a field entrance, as well as various other locations. 
     Other data  616  can include any of a wide variety of other data, including, but not limited to, various other data provided by operator or user input. 
     It will be noted that the various data can be stored in a data store, such as data store  302 , or a data store at a different location. 
     As illustrated in  FIG. 16 , logistics module  315  includes data capture logic  622 , material delivery location identifier logic  652 , distance logic  653 , arrival time logic  654 , material empty logic  655 , application rate logic  656 , speed logic  657 , route planning logic  658 , display element integration component  659 , map generator  660 , and can include other items  630  as well. Data capture logic  622 , itself, includes sensor accessing logic  662 , data store accessing logic  664 , and can include other items  666  as well. Data capture logic  622  captures or obtains data that can be used by other items of logistics module  315 . Sensor accessing logic  662  can be used by logistics module  315  to obtain or otherwise access sensor data (or values indicative of the sensed variables/characteristics) provided from in-situ sensors  308 . Additionally, data store accessing logic  664  can be used by logistics module  315  to obtain or access data stored on data stores, such as data store  302  or other data stores. Upon obtaining the various data, logistics module  315  generates logistics outputs  668  which can be used in the control of material application machine  100  or other items of material application system  300 . 
     Material empty logic  655  illustratively identifies geographic locations at the field, along the route of material application machine  100 , at which one or more material containers of material application machine  100  will be empty or will be empty to a threshold level. Material empty logic  655  can determine the location at which one or more material containers will be empty or empty to a threshold level based on a material consumption map  602 , as well as various other data, such as route data  610  and sensor data  606 . For instance, material empty logic  655  can identify a current fill level of one or more containers based on sensor data from in-situ sensors (e.g., fill level sensors  117 ,  177 ,  178 ,  271 , etc.), a current location of material application machine  100  (e.g., as indicated by geographic position sensor  304 ), a current heading of material application machine  100  (e.g., as indicated by heading/speed sensor  325 ) and can aggregate the material consumption values (e.g., predictive material consumption values), as indicated by a material consumption map  602 , along the route of material application machine  100  (as indicated by route data  610 ) to identify the location at which one or more material containers of material application machine  100  will be empty or empty to a threshold level. For instance, it may be that it is desirable to only allow the material application machine to become empty to a threshold level, rather than completely empty to reduce the risk of operating over ground without applying material. 
     In other examples, instead of using a material consumption map  602 , material empty logic  655  may utilize other types of maps. For instance, logistics module  315  may receive a functional predictive nutrient control zone map  461  or a functional predictive weed control zone map  1461 . Map  461  or map  1461  may include various machine settings values, such as material application settings (e.g., material application rate settings), along the route of the material application machine  100  which can be used (aggregated) by material empty logic  655  to identify a material empty location. In another example, material empty logic  655  may utilize an information map  358  in the form of a prescriptive material application map which may include various machine settings values, such as material application settings (e.g., material application rate settings), along the route of the material application machine  100  which can be used (aggregated) by material empty logic  655  to identify a material empty location. 
     Material delivery location identifier logic  652  identifies geographic locations at the field at which material is to be delivered to material application machine  100 . In some examples, the material delivery locations may be predetermined locations at the field (e.g., as indicated by preferred material delivery location data  615 ), such as in headlands or at an area of the field that is not used for agricultural, or an area of the field that is near a field entrance. For instance, it may be desirable to limit compaction (or other deterioration) at the field and thus it may be preferable to have the material application machine  100  travel to a location away from the operating portion of the field to receive material. In other examples, a material delivery vehicle  373  may travel onto the operating portion of the field to deliver material and thus the material delivery location may be a geographic location on the field along the route of the material application machine  100 . In some examples, the material delivery location is the same as the material empty location identified by material empty logic  655 . 
     Distance logic  653  illustratively determines a distance of machine(s) away from a material delivery location. For instance, distance logic  653  can determine the distance of material application machine  100  away from a material delivery location based on the current position of material application machine  100  (e.g., as indicated by geographic position sensor  304 ) as well as the route of material application machine  100  (e.g., as indicated by route data  610 ). Distance logic  653  can determine the distance of a material delivery vehicle  379  away from a material delivery location based on the current position of the material delivery vehicle  379  (e.g., as indicated by material delivery vehicle data  612 ) as well as the route of the material delivery vehicle  379  (e.g., as indicated by route data  610 ). 
     Arrival time logic  654  illustratively identifies a time at which machine(s) will (or can) arrive at a material delivery location. For instance, arrival time logic  654  can determine the time at which the material application machine  100  will (or can) arrive at a material delivery location based on distance between the material application machine  100  and the material delivery location as identified by distance logic  653  as well as speed data of the material application machine  100 , for instance speed characteristic values (e.g., predictive speed characteristic values) from a speed map  604 , or based on current speed characteristic values of material application machine  100  (e.g., as indicated by heading/speed sensors  325 ). Arrival time logic  654  can also identify a time at which a material delivery vehicle  379  will (or can) arrive at a material delivery location based on the distance between the material delivery vehicle  379  and the material delivery location as identified by distance logic  653  as well as speed data of the material delivery vehicle  379 , such as speed limits along the route of material delivery machine  379  (e.g., as indicated by route data  610 ), historical speed of material delivery machine  379  (e.g., as indicated by material delivery vehicle data  612 ), duration of intermediate stops along the route of the material delivery machine  379  (e.g., stops due to on-road traffic control, stops due to replenishing at other locations, etc.), or current speed of material delivery machine  379  (e.g., as indicated by material delivery vehicle data  612 ). 
     Based on the arrival times, logistics module  315  may generate a logistics output  668  to control a material delivery machine  379  to begin traveling to the material delivery location to arrive at the same time or within a threshold amount of time as the time that the material application machine  100  will arrive. In other examples, logistics module  315  may communicate with a material delivery service system  380  to schedule a material delivery based on the arrival time. 
     In some instances, it may be preferable to change the application rate of material application machine  100  such that material application machine will become empty (at least to a threshold level) at the end of a pass, rather than, somewhere else along the route (e.g., in the middle of the field or in the middle of a pass). In this way, material application machine  100  will still apply material over the pass and will not have to travel back over it. For instance, where material empty logic  655  identifies a material empty location that is not at the end of a pass, application rate logic  656  will identify an application rate setting to adjust the application rate of material application machine  100  such that material application machine will become empty at the end of a pass. It will be important to note that it may be that application rate logic  656  adjusts the operation rate in a pass that is prior to the pass that material empty logic  655  identifies as the pass in which the material application machine  100  will become empty. 
     In some examples, if the material application machine  100  is projected to be within a threshold level of empty at the end of a pass or of the field, such as 5% (which may be indicated by threshold data  614 ), application rate logic  656  may automatically identify an application rate setting to adjust the application rate of material application machine  100  such that material application machine  100  will become empty at the end of a pass or at the end of the field, regardless of the material empty location identified by material empty logic  655 . 
     Where the material delivery vehicle  379  and material application machine  100  will not (or cannot) arrive at a material delivery location at the same time, or within a threshold amount of time of each other, based on current conditions (e.g., machine settings), speed logic  657  may identify a speed setting to adjust a speed of material application machine  100  or of material delivery vehicle  379 , or both, such that the material application machine  100  and material delivery vehicle  379  arrive at the material delivery location at the same time or within a threshold amount of time of each other. Reducing the speed of a machine  100  may reduce wear, save on fuel costs, as well as provide various other benefits. 
     Where the material delivery vehicle  379  and material application machine  100  will not (or cannot) arrive at a material delivery location at the same time, or within a threshold amount of time of each other, based on current conditions (e.g., machine settings), route planning logic  658  may identify a new route for material application machine  100  or for material delivery vehicle  379 , or both, such that the material application machine  100  and material delivery vehicle  379  arrive at the material delivery location at the same time or within a threshold amount of time of each other. In this way, downtime can be reduced. 
     Map generator  660  illustratively generates one or more logistics maps  661 . Logistics maps  661  illustratively map the field in which the material application operation is being performed and perhaps surrounding areas of the field. Logistics maps  661  may include a variety of display elements (discussed below) and can be used in the control of a material application machine  100  or a material delivery vehicle  379 , or both. In some examples, a logistics map  661  may be one of the other maps discussed herein, such as one of the functional predictive maps  263  with logistics display elements integrated into the map. 
     Display element integration component  659  illustratively generates one or more display elements, such as material delivery location display elements, material empty location display elements, route display elements, material application machine display elements, material delivery machine display elements, distance display elements, arrival time display elements, as well as various other display elements. Display element integration component  659  can integrate the one or more display elements into one or more maps, such as one or more of functional predictive maps  263  or a separate logistics map  661  generated by map generator  661 . 
     It will be noted that as the one or more functional predictive maps  263  are updated or otherwise made new (as described above in  FIG. 15 ), the logistics outputs  668  generated by logistics module  315  can also be updated or otherwise made new according to the updated (or new) functional predictive maps  263 . For example, logistics module  315  may, based on the updated or new functional predictive maps  263 , may generate updated (or new) material empty locations, material delivery locations, distances, arrival times, speed outputs, route outputs, display elements, logistics maps, etc. 
     The logistic outputs  668  can be used to control material application machine  100  or material delivery vehicle  379 , or both. The logistics outputs  668  can be displayed (or provided) on an interface mechanism, such as operator interface mechanism  318  or user interface mechanism  364 . The logistics outputs  668  can be provided to other items of material application system  300 , such as to remote computing systems  368 , delivery vehicles  379 , and/or delivery services systems  380 . 
       FIG. 17  is a flow diagram showing one example operation of agricultural material application system  300  in controlling a material application operation, such as by controlling a material application machine  100  or by controlling other items of material application system  300 . 
     At block  702  logistics module  315  obtains one or more maps. Logistics module  315  can obtain one or more material consumption maps  602  as indicated by block  704 . Logistics module  315  can obtain one or more speed maps  604 , as indicated by block  706 . Logistics module  315  can obtain one or more other maps, such as other functional predictive maps  263  or information map(s)  358 , or both, as indicated by block  709 . 
     At block  710  various other data are obtained by logistics module  315 . For example, logistics module  315  can obtain one or more of the data items illustrated in  FIG. 16 . As indicated by block  712 , logistics module  315  can obtain sensor data  606 . As indicated by block  713 , logistics module  315  can obtain material application machine dimensional data  608 . As indicated by block  715 , logistics module  315  can obtain receiving route data  610 . As indicated by block  716 , logistics module  315  can obtain material delivery vehicle data  612 . As indicated by block  717 , logistics module  315  can obtain threshold data  614 . As indicated by block  718 , logistics module  315  can obtain preferred material delivery location data  615 . As indicated by block  719 , logistics module  315  can obtain various other data  616 . 
     At block  720  logistics module  315  generates one or more logistics outputs  668  based on the data obtained at blocks  702  and block  710 . As indicated by block  722 , material empty logic  655  can generate, as a logistics output  668 , a material empty location. As indicated by block  724 , material delivery location identifier logic  652  can generate, as a logistics output  668 , a material delivery location. As indicated by block  726 , distance logic  653  can generate, as a logistics output  668 , one or more distances, such as distance between the material application machine  100  and a material delivery location and a distance between a material delivery vehicle  379  and the material delivery location. As indicated by block  728 , arrival time logic  654  can generate, as a logistics output  668 , one or more arrival times, such as time at which a material application machine  100  will (or can) arrive at material delivery location and a time at which a material delivery vehicle  379  will (or can) arrive at a material delivery location. As indicated by block  730 , application rate logic  656  can generate, as a logistics output  668 , one or more application rate settings which can be used to control one or more material application actuators  340  to control an application rate of material. As indicated by block  732 , speed logic  657  can generate, as a logistics output  668 , one or more speed outputs which can be used to control propulsion subsystem  350  or to control a propulsion subsystem of a material delivery vehicle  379 , or both. As indicated by block  734 , route planning logic  658  can generate, as a logistics output  668 , one or more routes which can be used to control steering subsystem  352  or a steering subsystem of a material delivery vehicle  379 , or both. As indicated by block  736 , logistics module  315  can generate, as a logistics output  668 , one or more maps with integrated display elements, the display elements generated and integrated into the maps by display element integration component  659 . For example, at block  738 , the one or more maps may include one or more functional predictive maps  263  with display elements integrated or one or more logistics maps  661  with display elements integrated, or both. Logistics module  315  can generate a variety of other logistics outputs, as indicated by block  740 . 
     At block  742 , control system  314  generate control signals based on the one or more logistics outputs  668 . For example, as indicated by block  744 , control system  314  can generate control signals to control one or more controllable subsystems  316  based on the one or more logistics outputs  668 . As indicated by block  746 , control system  314  can generate control signals to control one or more interface mechanisms (e.g.,  318  or  364 ) to generate displays, alerts, notifications, recommendations, as well as various other indications based on the one or more logistics outputs  668 . As indicated by block  748 , control system  314  can generate various other control signals based on the logistics outputs  668 , such as to communicate information to other items of material application system  300  or to control other items of material application system  300 . 
     At block  750  it is determined if the material application operation is complete. If the material application operation has not been completed, operation returns to block  702 . If the material application operation has been completed, then the operation ends. 
     The examples herein describe the generation of a predictive model and, in some examples, the generation of a functional predictive map based on the predictive model. The examples described herein are distinguished from other approaches by the use of a model which is at least one of multi-variate or site-specific (i.e., georeferenced, such as map-based). Furthermore, the model is revised as the work machine is performing an operation and while additional in-situ sensor data is collected. The model may also be applied in the future beyond the current worksite. For example, the model may form a baseline (e.g., starting point) for a subsequent operation at a different worksite or the same worksite at a future time. 
     The revision of the model in response to new data may employ machine learning methods. Without limitation, machine learning methods may include memory networks, Bayes systems, decisions trees, Eigenvectors, Eigenvalues and Machine Learning, Evolutionary and Genetic Algorithms, Cluster Analysis, Expert Systems/Rules, Support Vector Machines, Engines/Symbolic Reasoning, Generative Adversarial Networks (GANs), Graph Analytics and ML, Linear Regression, Logistic Regression, LSTMs and Recurrent Neural Networks (RNNSs), Convolutional Neural Networks (CNNs), MCMC, Random Forests, Reinforcement Learning or Reward-based machine learning. Learning may be supervised or unsupervised. 
     Model implementations may be mathematical, making use of mathematical equations, empirical correlations, statistics, tables, matrices, and the like. Other model implementations may rely more on symbols, knowledge bases, and logic such as rule-based systems. Some implementations are hybrid, utilizing both mathematics and logic. Some models may incorporate random, non-deterministic, or unpredictable elements. Some model implementations may make uses of networks of data values such as neural networks. These are just some examples of models. 
     The predictive paradigm examples described herein differ from non-predictive approaches where an actuator or other machine parameter is fixed at the time the machine, system, or component is designed, set once before the machine enters the worksite, is reactively adjusted manually based on operator perception, or is reactively adjusted based on a sensor value. 
     The functional predictive map examples described herein also differ from other map-based approaches. In some examples of these other approaches, an a priori control map is used without any modification based on in-situ sensor data or else a difference determined between data from an in-situ sensor and a predictive map are used to calibrate the in-situ sensor. In some examples of the other approaches, sensor data may be mathematically combined with a priori data to generate control signals, but in a location-agnostic way; that is, an adjustment to an a priori, georeferenced predictive setting is applied independent of the location of the work machine at the worksite. The continued use or end of use of the adjustment, in the other approaches, is not dependent on the work machine being in a particular defined location or region within the worksite. 
     In examples described herein, the functional predictive maps and predictive actuator control rely on obtained maps and in-situ data that are used to generate predictive models. The predictive models are then revised during the operation to generate revised functional predictive maps and revised actuator control. In some examples, the actuator control is provided based on functional predictive control zone maps which are also revised during the operation at the worksite. In some examples, the revisions (e.g., adjustments, calibrations, etc.) are tied to regions or zones of the worksite rather than to the whole worksite or some non-georeferenced condition. For example, the adjustments are applied to one or more areas of a worksite to which an adjustment is determined to be relevant (e.g., such as by satisfying one or more conditions which may result in application of an adjustment to one or more locations while not applying the adjustment to one or more other locations), as opposed to applying a change in a blanket way to every location in a non-selective way. 
     In some examples described herein, the models determine and apply those adjustments to selective portions or zones of the worksite based on a set of a priori data, which, in some instances, is multivariate in nature. For example, adjustments may, without limitation, be tied to defined portions of the worksite based on site-specific factors such as topography, soil type, crop variety, soil moisture, as well as various other factors, alone or in combination. Consequently, the adjustments are applied to the portions of the field in which the site-specific factors satisfy one or more criteria and not to other portions of the field where those site-specific factors do not satisfy the one or more criteria. Thus, in some examples described herein, the model generates a revised functional predictive map for at least the current location or zone, the unworked part of the worksite, or the whole worksite. 
     As an example, in which the adjustment is applied only to certain areas of the field, consider the following. The system may determine that a detected in-situ characteristic value varies from a predictive value of the characteristic, such as by a threshold amount. This deviation may only be detected in areas of the field where the elevation of the worksite is above a certain level. Thus, the revision to the predictive value is only applied to other areas of the worksite having elevation above the certain level. In this simpler example, the predictive characteristic value and elevation at the point the deviation occurred and the detected characteristic value and elevation at the point the deviation cross the threshold are used to generate a linear equation. The linear equation is used to adjust the predictive characteristic value in areas of the worksite (which have not yet been operated on in the current operation, such as areas where material has not yet been applied in the current operation) in the functional predictive map as a function of elevation and the predicted characteristic value. This results in a revised functional predictive map in which some values are adjusted while others remain unchanged based on selected criteria, e.g., elevation as well as threshold deviation. The revised functional map is then used to generate a revised functional control zone map for controlling the machine. 
     As an example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows. 
     One or more maps of the field are obtained, such as one or more of a soil property map, a yield map, a residue map, a constituents map, a seeding map, a topographic map, a vegetative index map, and another type of map. 
     In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ nutrient values 
     A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive nutrient model. 
     A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive nutrient map that maps predictive nutrient values to one or more locations on the worksite based on a predictive nutrient model and the one or more obtained maps. 
     Control zones, which include machine settings values, can be incorporated into the functional predictive nutrient map to generate a functional predictive nutrient map with control zones. 
     As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows. 
     One or more maps of the field are obtained, such as one or more of a vegetative index map, an optical map, a weed map, and another type of map. 
     In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ weed values 
     A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive weed model. 
     A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive weed map that maps predictive weed values to one or more locations on the worksite based on a predictive weed model and the one or more obtained maps. 
     Control zones, which include machine settings values, can be incorporated into the functional predictive weed map to generate a functional predictive weed map with control zones. 
     As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows. 
     One or more maps of the field are obtained, such as one or more of a soil property map, a topographic map, a vegetative index map, a weed map, a contamination map, and another type of map. 
     In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ material consumption values 
     A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive material consumption model. 
     A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive material consumption map that maps predictive material consumption values to one or more locations on the worksite based on a predictive material consumption model and the one or more obtained maps. 
     Control zones, which include machine settings values, can be incorporated into the functional predictive material consumption map to generate a functional predictive material consumption map with control zones. 
     As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows. 
     One or more maps of the field are obtained. 
     In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ speed characteristic values 
     A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive speed model. 
     A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive speed map that maps predictive speed characteristic values to one or more locations on the worksite based on a predictive speed model and the one or more obtained maps. 
     Control zones, which include machine settings values, can be incorporated into the functional predictive speed map to generate a functional predictive speed map with control zones. 
     As the mobile machine continues to operate at the worksite, additional in-situ sensor data is collected. A learning trigger criteria can be detected, such as threshold amount of additional in-situ sensor data being collected, a magnitude of change in a relationship (e.g., the in-situ characteristic values varies to a certain [e.g., threshold] degree from a predictive value of the characteristic), and operator or user makes edits to the predictive map(s) or to a control algorithm, or both, a certain (e.g., threshold) amount of time elapses, as well as various other learning trigger criteria. The predictive model(s) are then revised based on the additional in-situ sensor data and the values from the obtained maps. The functional predictive maps or the functional predictive control zone maps, or both, are then revised based on the revised model(s) and the values in the obtained maps. 
     The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by and facilitate the functionality of the other components or items in those systems. 
     Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, they can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech. 
     A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure. 
     Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed. 
     It will be noted that the above discussion has described a variety of different systems, components, logic and interactions. It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components, some of which are described below, that perform the functions associated with those systems, components, or logic, or interactions. In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions may also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic and interactions described above. Other structures may be used as well. 
       FIG. 18  is a block diagram of a mobile agricultural material application machine  1000 , which may be similar to mobile material application machine  100  shown in  FIG. 10 . The mobile material application machine  1000  communicates with elements in a remote server architecture  900 . In some examples, remote server architecture  900  provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various examples, remote servers may deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers may deliver applications over a wide area network and may be accessible through a web browser or any other computing component. Software or components shown in  FIG. 10  as well as data associated therewith, may be stored on servers at a remote location. The computing resources in a remote server environment may be consolidated at a remote data center location, or the computing resources may be dispersed to a plurality of remote data centers. Remote server infrastructures may deliver services through shared data centers, even though the services appear as a single point of access for the user. Thus, the components and functions described herein may be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functions may be provided from a server, or the components and functions can be installed on client devices directly, or in other ways. 
     In the example shown in  FIG. 18 , some items are similar to those shown in  FIG. 10  and those items are similarly numbered.  FIG. 18  specifically shows that predictive model generator  310 , predictive map generator  312 , and logistics module  315  may be located at a server location  902  that is remote from the material application machine  1000 . Therefore, in the example shown in  FIG. 18 , material application machine  1000  accesses systems through remote server location  902 . In other examples, various other items may also be located at server location  902 , such as predictive model  311 , functional predictive maps  263  (including predictive maps  264  and predictive control zone maps  265 ), control zone generator  313 , and processing system  338 . 
       FIG. 18  also depicts another example of a remote server architecture.  FIG. 18  shows that some elements of  FIG. 10  may be disposed at a remote server location  902  while others may be located elsewhere. By way of example, data store  302  may be disposed at a location separate from location  902  and accessed via the remote server at location  902 . Regardless of where the elements are located, the elements can be accessed directly by material application machine  1000  through a network such as a wide area network or a local area network; the elements can be hosted at a remote site by a service; or the elements can be provided as a service or accessed by a connection service that resides in a remote location. Also, data may be stored in any location, and the stored data may be accessed by, or forwarded to, operators, users or systems. For instance, physical carriers may be used instead of, or in addition to, electromagnetic wave carriers. In some examples, where wireless telecommunication service coverage is poor or nonexistent, another machine, such as a fuel truck or other mobile machine or vehicle, may have an automated, semi-automated or manual information collection system. As the mobile machine  1000  comes close to the machine containing the information collection system, such as a fuel truck prior to fueling, the information collection system collects the information from the mobile machine  1000  using any type of ad-hoc wireless connection. The collected information may then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage is available. For instance, a fuel truck may enter an area having wireless communication coverage when traveling to a location to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information may be stored on the material application machine  1000  until the material application machine  1000  enters an area having wireless communication coverage. The material application machine  1000 , itself, may send the information to another network. 
     It will also be noted that the elements of  FIG. 10 , or portions thereof, may be disposed on a wide variety of different devices. One or more of those devices may include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palm top computer, a cell phone, a smart phone, a multimedia player, a personal digital assistant, etc. 
     In some examples, remote server architecture  902  may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain). 
       FIG. 19  is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user&#39;s or client&#39;s handheld device  16 , in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment of material application machine  100 , for use in generating, processing, or displaying the maps discussed above.  FIGS. 20-21  are examples of handheld or mobile devices. 
       FIG. 19  provides a general block diagram of the components of a client device  16  that can run some components shown in  FIG. 10 , that interacts with them, or both. In the device  16 , a communications link  13  is provided that allows the handheld device to communicate with other computing devices and under some examples provides a channel for receiving information automatically, such as by scanning. Examples of communications link  13  include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks. 
     In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface  15 . Interface  15  and communication links  13  communicate with a processor  17  (which can also embody processors or servers from other FIGS.) along a bus  19  that is also connected to memory  21  and input/output (I/O) components  23 , as well as clock  25  and location system  27 . 
     I/O components  23 , in one example, are provided to facilitate input and output operations. I/O components  23  for various examples of the device  16  can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components  23  can be used as well. 
     Clock  25  illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor  17 . 
     Location system  27  illustratively includes a component that outputs a current geographical location of device  16 . This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Location system  27  can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions. 
     Memory  21  stores operating system  29 , network settings  31 , applications  33 , application configuration settings  35 , data store  37 , communication drivers  39 , and communication configuration settings  41 . Memory  21  can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory  21  may also include computer storage media (described below). Memory  21  stores computer readable instructions that, when executed by processor  17 , cause the processor to perform computer-implemented steps or functions according to the instructions. Processor  17  may be activated by other components to facilitate their functionality as well. 
       FIG. 20  shows one example in which device  16  is a tablet computer  1100 . In  FIG. 20 , computer  1100  is shown with user interface display screen  1102 . Screen  1102  can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. Tablet computer  1100  may also use an on-screen virtual keyboard. Of course, computer  1100  might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer  1100  may also illustratively receive voice inputs as well. 
       FIG. 21  is similar to  FIG. 20  except that the device is a smart phone  71 . Smart phone  71  has a touch sensitive display  73  that displays icons or tiles or other user input mechanisms  75 . Mechanisms  75  can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone  71  is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone. 
     Note that other forms of the devices  16  are possible. 
       FIG. 22  is one example of a computing environment in which elements of  FIG. 10  can be deployed. With reference to  FIG. 22 , an example system for implementing some embodiments includes a computing device in the form of a computer  1210  programmed to operate as discussed above. Components of computer  1210  may include, but are not limited to, a processing unit  1220  (which can comprise processors or servers from previous FIGS.), a system memory  1230 , and a system bus  1221  that couples various system components including the system memory to the processing unit  1220 . The system bus  1221  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect to  FIG. 10  can be deployed in corresponding portions of  FIG. 22 . 
     Computer  1210  typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed by computer  1210  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer readable media includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  1210 . Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     The system memory  1230  includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM)  1231  and random access memory (RAM)  1232 . A basic input/output system  1233  (BIOS), containing the basic routines that help to transfer information between elements within computer  1210 , such as during start-up, is typically stored in ROM  1231 . RAM  1232  typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit  1220 . By way of example, and not limitation,  FIG. 22  illustrates operating system  1234 , application programs  1235 , other program modules  1236 , and program data  1237 . 
     The computer  1210  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 22  illustrates a hard disk drive  1241  that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive  1255 , and nonvolatile optical disk  1256 . The hard disk drive  1241  is typically connected to the system bus  1221  through a non-removable memory interface such as interface  1240 , and optical disk drive  1255  are typically connected to the system bus  1221  by a removable memory interface, such as interface  1250 . 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 22 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  1210 . In  FIG. 22 , for example, hard disk drive  1241  is illustrated as storing operating system  1244 , application programs  1245 , other program modules  1246 , and program data  1247 . Note that these components can either be the same as or different from operating system  1234 , application programs  1235 , other program modules  1236 , and program data  1237 . 
     A user may enter commands and information into the computer  1210  through input devices such as a keyboard  1262 , a microphone  1263 , and a pointing device  1261 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  1220  through a user input interface  1260  that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display  1291  or other type of display device is also connected to the system bus  1221  via an interface, such as a video interface  1290 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  1297  and printer  1296 , which may be connected through an output peripheral interface  1295 . 
     The computer  1210  is operated in a networked environment using logical connections (such as a controller area network—CAN, local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer  1280 . 
     When used in a LAN networking environment, the computer  1210  is connected to the LAN  1271  through a network interface or adapter  1270 . When used in a WAN networking environment, the computer  1210  typically includes a modem  1272  or other means for establishing communications over the WAN  1273 , such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.  FIG. 22  illustrates, for example, that remote application programs  1285  can reside on remote computer  1280 . 
     It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.