Patent Publication Number: US-2023148474-A1

Title: Predictive machine characteristic map generation and control system

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a continuation of Ser. No. 17/066,442, filed Oct. 8, 2020, which is a continuation-in-part of and claims priority of U.S. patent applications Ser. No. Ser. No. 16/783,475, filed Feb. 6, 2020, Ser. No. 16/783,511, filed Feb. 6, 2020, Ser. No. 16/380,531, filed Apr. 10, 2019, and Ser. No. 16/171,978, filed Oct. 26, 2018 the content of which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE DESCRIPTION 
     The present description relates to agricultural machines, forestry machines, construction machines and turf management machines. 
     BACKGROUND 
     There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and windrowers. Some harvester can also be fitted with different types of heads to harvest different types of crops. 
     Topographic characteristics can have a number of deleterious effects on the harvesting operation. For instance, when a harvester travels over a sloped feature the pitch or roll of the harvester may impede performance of the harvester. Therefore, an operator may attempt to modify control of the harvester, upon encountering a slope during the harvesting operation. 
     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 
     One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control. 
     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 examples that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a partial pictorial, partial schematic illustration of one example of a combine harvester. 
         FIG.  2    is a block diagram showing some portions of an agricultural harvester in more detail, according to some examples of the present disclosure. 
         FIGS.  3 A- 3 B  (collectively referred to herein as  FIG.  3   ) show a flow diagram illustrating an example of operation of an agricultural harvester in generating a map. 
         FIG.  4 A  is a block diagram showing one example of a predictive model generator and a predictive map generator. 
         FIG.  4 B  is a block diagram showing in-situ sensors. 
         FIG.  5    is a flow diagram showing an example of operation of an agricultural harvester in receiving a topographic map, detecting a machine characteristic, and generating a functional predictive map for presentation or use in controlling the agricultural harvester during a harvesting operation. 
         FIG.  6    is a block diagram showing one example of an agricultural harvester in communication with a remote server environment. 
         FIGS.  7 - 9    show examples of mobile devices that can be used in an agricultural harvester, according to some examples of the present disclosure. 
         FIG.  10    is a block diagram showing one example of a computing environment that can be used in an agricultural harvester and the architectures illustrated in previous figures. 
     
    
    
     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. 
     The present description relates to using in-situ data taken concurrently with an agricultural operation, in combination with prior data, to generate a predictive map and, more particularly, a predictive machine characteristic map. In some examples, the predictive machine map can be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, performance of an agricultural harvester may be degraded when the agricultural harvester engages a topographic feature, such as a slope. For instance, if the agricultural harvester is ascending a hill the power demands increase and machine performance may be diminished. This problem may be exacerbated when the soil is wet (such as shortly after a rainfall) and the tires or tracks face increased slippage. Also, performance of a harvester (or other agricultural machine) may be deleteriously affected based on the topography of a field. For example, the topography can cause the machine to roll a certain amount when navigating a side slope. Without limitation, machine pitch or roll can affect the stability of the machine, internal material distribution, spray application pressures on a sprayer, among others. For example, grain loss can be affected by a topographic characteristic that causes agricultural harvester  100  to either pitch or roll. The increased pitch can cause grain to go out the back more quickly, decreased pitch can keep the grain in the machine, and the roll elements can overload the sides of the cleaning system and drive up more grain loss on those sides. Similarly, grain quality can be impacted by both pitch and roll, and similar to grain loss, the reactions of the material other than grain staying in the machine or leaving the machine based on the pitch or roll can be influential on the quality output. In another example, a topographic characteristic influencing pitch will have an impact on the amount of tailings entering the tailings system, thus impacting a tailings sensor output. The consideration of the pitch and the time at that level can have a relationship to how much tailings volume increases and could be useful to estimate in the need to have controls for anticipating that level and making adjustments. 
     A topographic map illustratively maps 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 harvester travels across the terrain in known directions, the pitch and roll of the agricultural harvester 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 present discussion thus proceeds with respect to systems that receive a topographic map of a field and also use an in-situ sensor to detect a value indicative of one or more of an internal material distribution, power characteristic, ground speed, grain loss, tailings, grain quality, or another machine characteristic, during a harvesting operation. The systems generate a model that models a relationship between the topographic characteristics derived from the topographic map and the output values from the in-situ sensors. The model is used to generate a functional predictive machine map that predicts, for example, power usage at different locations in the field. The functional predictive machine map, generated during the harvesting operation, can be used in automatically controlling a harvester during the harvesting operation. In some cases, the functional predictive machine map is used to generate a mission or path planning for the agricultural harvester operating in the field, for example, to improve power utilization, speed or uniformity of internal material distribution throughout the operation. Of course, internal material distribution, power characteristics, ground speed, grain loss, tailings and grain quality are only examples of machine characteristics that can be predicted based on the topographic characteristics and other machine characteristics can be predicted and used to control the machine as well. 
       FIG.  1    is a partial pictorial, partial schematic, illustration of a self-propelled agricultural harvester  100 . In the illustrated example, agricultural harvester  100  is a combine harvester. Further, although combine harvesters are provided as examples throughout the present disclosure, it will be appreciated that the present description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvesters, windrowers, or other agricultural work machines. Consequently, the present disclosure is intended to encompass the various types of harvesters described and is, thus, not limited to combine harvesters. Moreover, the present disclosure is directed to other types of work machines, such as agricultural seeders and sprayers, construction equipment, forestry equipment, and turf management equipment where generation of a predictive map may be applicable. Consequently, the present disclosure is intended to encompass these various types of harvesters and other work machines and is, thus, not limited to combine harvesters. 
     As shown in  FIG.  1   , agricultural harvester  100  illustratively includes an operator compartment  101 , which can have a variety of different operator interface mechanisms, for controlling agricultural harvester  100 . Agricultural harvester  100  includes front-end equipment, such as a header  102 , and a cutter generally indicated at  104 . Agricultural harvester  100  also includes a feeder house  106 , a feed accelerator  108 , and a thresher generally indicated at  110 . The feeder house  106  and the feed accelerator  108  form part of a material handling subsystem  125 . Header  102  is pivotally coupled to a frame  103  of agricultural harvester  100  along pivot axis  105 . One or more actuators  107  drive movement of header  102  about axis  105  in the direction generally indicated by arrow  109 . Thus, a vertical position of header  102  (the header height) above ground  111  over which the header  102  travels is controllable by actuating actuator  107 . While not shown in  FIG.  1   , agricultural harvester  100  may also include one or more actuators that operate to apply a tilt angle, a roll angle, or both to the header  102  or portions of header  102 . Tilt refers to an angle at which the cutter  104  engages the crop. The tilt angle is increased, for example, by controlling header  102  to point a distal edge  113  of cutter  104  more toward the ground. The tilt angle is decreased by controlling header  102  to point the distal edge  113  of cutter  104  more away from the ground. The roll angle refers to the orientation of header  102  about the front-to-back longitudinal axis of agricultural harvester  100 . 
     Thresher  110  illustratively includes a threshing rotor  112  and a set of concaves  114 . Further, agricultural harvester  100  also includes a separator  116 . Agricultural harvester  100  also includes a cleaning subsystem or cleaning shoe (collectively referred to as cleaning subsystem  118 ) that includes a cleaning fan  120 , chaffer  122 , and sieve  124 . The material handling subsystem  125  also includes discharge beater  126 , tailings elevator  128 , clean grain elevator  130 , as well as unloading auger  134  and spout  136 . The clean grain elevator moves clean grain into clean grain tank  132 . Agricultural harvester  100  also includes a residue subsystem  138  that can include chopper  140  and spreader  142 . Agricultural harvester  100  also includes a propulsion subsystem that includes an engine that drives ground engaging components  144 , such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, agricultural harvester  100  may have left and right cleaning subsystems, separators, etc., which are not shown in  FIG.  1   . 
     In operation, and by way of overview, agricultural harvester  100  illustratively moves through a field in the direction indicated by arrow  147 . As agricultural harvester  100  moves, header  102  (and the associated reel  164 ) engages the crop to be harvested and gathers the crop toward cutter  104 . An operator of agricultural harvester  100  can be a local human operator, a remote human operator, or an automated system. The operator of agricultural harvester  100  may determine one or more of a height setting, a tilt angle setting, or a roll angle setting for header  102 . For example, the operator inputs a setting or settings to a control system, described in more detail below, that controls actuator  107 . The control system may also receive a setting from the operator for establishing the tilt angle and roll angle of the header  102  and implement the inputted settings by controlling associated actuators, not shown, that operate to change the tilt angle and roll angle of the header  102 . The actuator  107  maintains header  102  at a height above ground  111  based on a height setting and, where applicable, at desired tilt and roll angles. Each of the height, roll, and tilt settings may be implemented independently of the others. The control system responds to header error (e.g., the difference between the height setting and measured height of header  104  above ground  111  and, in some examples, tilt angle and roll angle errors) with a responsiveness that is determined based on a sensitivity level. If the sensitivity level is set at a greater level of sensitivity, the control system responds to smaller header position errors, and attempts to reduce the detected errors more quickly than when the sensitivity is at a lower level of sensitivity. 
     Returning to the description of the operation of agricultural harvester  100 , after crops are cut by cutter  104 , the severed crop material is moved through a conveyor in feeder house  106  toward feed accelerator  108 , which accelerates the crop material into thresher  110 . The crop material is threshed by rotor  112  rotating the crop against concaves  114 . The threshed crop material is moved by a separator rotor in separator  116  where a portion of the residue is moved by discharge beater  126  toward the residue subsystem  138 . The portion of residue transferred to the residue subsystem  138  is chopped by residue chopper  140  and spread on the field by spreader  142 . In other configurations, the residue is released from the agricultural harvester  100  in a windrow. In other examples, the residue subsystem  138  can include weed seed eliminators (not shown) such as seed baggers or other seed collectors, or seed crushers or other seed destroyers. 
     Grain falls to cleaning subsystem  118 . Chaffer  122  separates some larger pieces of material from the grain, and sieve  124  separates some of finer pieces of material from the clean grain. Clean grain falls to an auger that moves the grain to an inlet end of clean grain elevator  130 , and the clean grain elevator  130  moves the clean grain upwards, depositing the clean grain in clean grain tank  132 . Residue is removed from the cleaning subsystem  118  by airflow generated by cleaning fan  120 . Cleaning fan  120  directs air along an airflow path upwardly through the sieves and chaffers. The airflow carries residue rearwardly in agricultural harvester  100  toward the residue handling subsystem  138 . 
     Tailings elevator  128  returns tailings to thresher  110  where the tailings are re-threshed. Alternatively, the tailings also may be passed to a separate re-threshing mechanism by a tailings elevator or another transport device where the tailings are re-threshed as well. 
       FIG.  1    also shows that, in one example, agricultural harvester  100  includes ground speed sensor  146 , one or more separator loss sensors  148 , a clean grain camera  150 , a forward looking image capture mechanism  151 , which may be in the form of a stereo or mono camera, and one or more loss sensors  152  provided in the cleaning subsystem  118 . 
     Ground speed sensor  146  senses the travel speed of agricultural harvester  100  over the ground. Ground speed sensor  146  may sense the travel speed of the agricultural harvester  100  by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axle, or other components. In some instances, the travel speed may be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, a Doppler speed sensor, or a wide variety of other systems or sensors that provide an indication of travel speed. Ground speed sensors  146  can also include direction sensors such as a compass, a magnetometer, a gravimetric sensor, a gyroscope, GPS derivation, to determine the direction of travel in two or three dimensions in combination with the speed. This way, when agricultural harvester  100  is on a slope, the orientation of agricultural harvester  100  relative to the slope is known. For example, an orientation of agricultural harvester  100  could include ascending, descending or transversely travelling the slope. Machine or ground speed, when referred to in this disclosure can also include the two or three dimension direction of travel. 
     Loss sensors  152  illustratively provide an output signal indicative of the quantity of grain loss occurring in both the right and left sides of the cleaning subsystem  118 . In some examples, sensors  152  are strike sensors which count grain strikes per unit of time or per unit of distance traveled to provide an indication of the grain loss occurring at the cleaning subsystem  118 . The strike sensors for the right and left sides of the cleaning subsystem  118  may provide individual signals or a combined or aggregated signal. In some examples, sensors  152  may include a single sensor as opposed to separate sensors provided for each cleaning subsystem  118 . 
     Separator loss sensor  148  provides a signal indicative of grain loss in the left and right separators, not separately shown in  FIG.  1   . The separator loss sensors  148  may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregate signal. In some instances, sensing grain loss in the separators may also be performed using a wide variety of different types of sensors as well. 
     Agricultural harvester  100  may also include other sensors and measurement mechanisms. For instance, agricultural harvester  100  may include one or more of the following sensors: a header height sensor that senses a height of header  102  above ground  111 ; stability sensors that sense oscillation or bouncing motion (and amplitude) of agricultural harvester  100 ; a residue setting sensor that is configured to sense whether agricultural harvester  100  is configured to chop the residue, produce a windrow, etc.; a cleaning shoe fan speed sensor to sense the speed of fan  120 ; a concave clearance sensor that senses clearance between the rotor  112  and concaves  114 ; a threshing rotor speed sensor that senses a rotor speed of rotor  112 ; a chaffer clearance sensor that senses the size of openings in chaffer  122 ; a sieve clearance sensor that senses the size of openings in sieve  124 ; a material other than grain (MOG) moisture sensor that senses a moisture level of the MOG passing through agricultural harvester  100 ; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester  100 ; a machine orientation sensor that senses the orientation of agricultural harvester  100 ; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester  100 . For example, in some instances, the crop property sensors may sense grain quality such as broken grain, MOG levels; grain constituents such as starches and protein; and grain feed rate as the grain travels through the feeder house  106 , clean grain elevator  130 , or elsewhere in the agricultural harvester  100 . The crop property sensors may also sense the feed rate of biomass through feeder house  106 , through the separator  116  or elsewhere in agricultural harvester  100 . The crop property sensors may also sense the feed rate as a mass flow rate of grain through elevator  130  or through other portions of the agricultural harvester  100  or provide other output signals indicative of other sensed variables. 
     Examples of sensors used to detect or sense the power characteristics include, but are not limited to, a voltage sensor, a current sensor, a torque sensor, a hydraulic pressure sensor, a hydraulic flow sensor, a force sensor, a bearing load sensor and a rotational sensor. Power characteristics can be measured at varying levels of granularity. For instance, power usage can be sensed machine-wide, subsystem-wide or by individual components of the subsystems. 
     Examples of sensors used to detect internal material distribution include, but are not limited to, one or more cameras, capacitive sensors, electromagnetic or ultrasonic time-of-flight reflective sensors, signal attenuation sensors, weight or mass sensors, material flow sensors, etc. These sensors can be placed at one or more locations in agricultural harvester  100  to sense the distribution of the material in agricultural harvester  100 , during the operation of agricultural harvester  100 . 
     Examples of sensors used to detect or sense a pitch or roll of agricultural harvester  100  include accelerometers, gyroscopes, inertial measurement units, gravimetric sensors, magnetometers, etc. These sensors can also be indicative of the slope of the terrain that agricultural harvester  100  is currently on. 
     Prior to describing how agricultural harvester  100  generates a functional predictive machine map, and uses the functional predictive machine map for control, a brief description of some of the items on agricultural harvester  100 , and their operation, will first be described. The description of  FIGS.  2  and  3    describe receiving a general type of prior information map and combining information from the prior information map with a georeferenced sensor signal generated by an in-situ sensor, where the sensor signal is indicative of a characteristic in the field, such as characteristics of crop or weeds present in the field. Characteristics of the “field” may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed type, soil moisture, surface quality; characteristics of crop properties such as crop height, crop moisture, crop density, crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight; and characteristics of machine performance such as loss levels, job quality, fuel consumption, and power utilization. A relationship between the characteristic values obtained from in-situ sensor signals and the prior information map values is identified, and that relationship is used to generate a new functional predictive map. A functional predictive map predicts values at different geographic locations in a field, and one or more of those values can be used for controlling a machine. In some instances, a functional predictive map can be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map can be presented to a user visually, such as via a display, haptically, or audibly. The user can interact with the functional predictive map to perform editing operations and other user interface operations. In some instances, a functional predictive map both can be used for controlling an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user. 
     After the general approach is described with respect to  FIGS.  2  and  3   , a more specific approach for generating a functional predictive map that can be presented to an operator or user, or used to control agricultural harvester  100 , or both is described with respect to  FIGS.  4  and  5   . Again, while the present discussion proceeds with respect to the agricultural harvester and, particularly, a combine harvester, the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines. 
       FIG.  2    is a block diagram showing some portions of an example agricultural harvester  100 .  FIG.  2    shows that agricultural harvester  100  illustratively includes one or more processors or servers  201 , data store  202 , geographic position sensor  204 , communication system  206 , and one or more in-situ sensors  208  that sense one or more agricultural characteristics of a field concurrent with a harvesting operation. An agricultural characteristic can include any characteristic that can have an effect of the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The in-situ sensors  208  generate values corresponding to the sensed characteristics. The agricultural harvester  100  also includes a predictive model or relationship generator (collectively referred to hereinafter as “predictive model generator  210 ”), predictive map generator  212 , control zone generator  213 , control system  214 , one or more controllable subsystems  216 , and an operator interface mechanism  218 . The agricultural harvester  100  can also include a wide variety of other agricultural harvester functionality  220 . The in-situ sensors  208  include, for example, on-board sensors  222 , remote sensors  224 , and other sensors  226  that sense characteristics of a field during the course of an agricultural operation. Predictive model generator  210  illustratively includes a prior information variable-to-in-situ variable model generator  228 , and predictive model generator  210  can include other items  230 . Control system  214  includes communication system controller  229 , operator interface controller  231 , a settings controller  232 , path planning controller  234 , feed rate controller  236 , header and reel controller  238 , draper belt controller  240 , deck plate position controller  242 , residue system controller  244 , machine cleaning controller  245 , zone controller  247 , and system  214  can include other items  246 . Controllable subsystems  216  include machine and header actuators  248 , propulsion subsystem  250 , steering subsystem  252 , residue subsystem  138 , machine cleaning subsystem  254 , and subsystems  216  can include a wide variety of other subsystems  256 . 
       FIG.  2    also shows that agricultural harvester  100  can receive prior information map  258 . As described below, the prior map information map  258  includes, for example, a topographic map from a prior operation in the field, such as an unmanned aerial vehicle completing a range scanning operation from a known altitude, a topographic map sensed by a plane, a topographic map sensed by a satellite, a topographic map sensed by a ground vehicle, such as a GPS-equipped planter, etc. However, prior map information may also encompass other types of data that were obtained prior to a harvesting operation or a map from a prior operation. For instance, a topographic map can be retrieved from a remote source such as the United States Geological Survey (USGS).  FIG.  2    also shows that an operator  260  may operate the agricultural harvester  100 . The operator  260  interacts with operator interface mechanisms  218 . In some examples, operator interface mechanisms  218  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  260  may interact with operator interface mechanisms  218  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 operator interface mechanisms  218  may be used and are within the scope of the present disclosure. 
     Prior information map  258  may be downloaded onto agricultural harvester  100  and stored in data store  202 , using communication system  206  or in other ways. In some examples, communication system  206  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. Communication system  206  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. 
     Geographic position sensor  204  illustratively senses or detects the geographic position or location of agricultural harvester  100 . Geographic position sensor  204  can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensor  204  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 sensor  204  can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors. 
     In-situ sensors  208  may be any of the sensors described above with respect to  FIG.  1   . In-situ sensors  208  include on-board sensors  222  that are mounted on-board agricultural harvester  100 . Such sensors may include, for instance, a speed sensor (e.g., a GPS, speedometer, or compass), image sensors that are internal to agricultural harvester  100  (such as the clean grain camera or cameras mounted to identify material distribution in agricultural harvester  100 , for example, in the residue subsystem or the cleaning system), grain loss sensors, tailings characteristic sensors, and grain quality sensors. The in-situ sensors  208  also include remote in-situ sensors  224  that capture in-situ information. In-situ data include data taken from a sensor on-board the harvester or taken by any sensor where the data are detected during the harvesting operation. 
     Predictive model generator  210  generates a model that is indicative of a relationship between the values sensed by the in-situ sensor  208  and a characteristic mapped to the field by the prior information map  258 . For example, if the prior information map  258  maps a topographic characteristic to different locations in the field, and the in-situ sensor  208  is sensing a value indicative of power usage, then prior information variable-to-in-situ variable model generator  228  generates a predictive machine model that models the relationship between the topographic characteristics and the power usage. The predictive machine model can also be generated based on topographic characteristics from the prior information map  258  and multiple in-situ data values generated by in-situ sensors  208 . Then, predictive map generator  212  uses the predictive machine model generated by predictive model generator  210  to generate a functional predictive machine characteristic map that predicts the value of a machine characteristic, such as internal material distribution, sensed by the in-situ sensors  208  at different locations in the field based upon the prior information map  258 . 
     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  208 . 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  208 . 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  208  but have a relationship to the type of data type sensed by the in-situ sensors  208 . For example, in some examples, the data type sensed by the in-situ sensors  208  may be indicative of the type of values in the functional predictive map  263 . In some examples, the type of data in the functional predictive map  263  may be different than the data type in the prior information map  258 . In some instances, the type of data in the functional predictive map  263  may have different units from the data in the prior information map  258 . In some examples, the type of data in the functional predictive map  263  may be different from the data type in the prior information map  258  but has a relationship to the data type in the prior information map  258 . For example, in some examples, the data type in the prior information map  258  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  208  and the data type in the prior information map  258 . 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  208  and the data type in prior information map  258 . 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  208  or the data type in the prior information map  258 , and different than the other. 
     Predictive map generator  212  can use the topographic characteristics in prior information map  258 , and the model generated by predictive model generator  210 , to generate a functional predictive map  263  that predicts the machine characteristics at different locations in the field. Predictive map generator  212  thus outputs predictive map  264 . 
     As shown in  FIG.  2   , predictive map  264  predicts the value of a sensed characteristic (sensed by in-situ sensors  208 ), or a characteristic related to the sensed characteristic, at various locations across the field based upon a prior information value in prior information map  258  at those locations and using the predictive model. For example, if predictive model generator  210  has generated a predictive model indicative of a relationship between a topographic characteristic and power usage, then, given the topographic characteristics at different locations across the field, predictive map generator  212  generates a predictive map  264  that predicts the value of the power usage at different locations across the field. The topographic characteristic, obtained from the topographic map, at those locations and the relationship between topographic characteristic and machine characteristic, obtained from the predictive model, are used to generate the predictive map  264 . The predicted power usage can be used by a control system to adjust, for example, engine throttle or power allocation across various subsystems to meet the predicted power usage requirements. 
     Some variations in the data types that are mapped in the prior information map  258 , the data types sensed by in-situ sensors  208  and the data types predicted on the predictive map  264  will now be described. 
     In some examples, the data type in the prior information map  258  is different from the data type sensed by in-situ sensors  208 , yet the data type in the predictive map  264  is the same as the data type sensed by the in-situ sensors  208 . For instance, the prior information map  258  may be a topographic map, and the variable sensed by the in-situ sensors  208  may be a machine characteristic. The predictive map  264  may then be a predictive machine map that maps predicted machine characteristic values to different geographic locations in the field. 
     Also, in some examples, the data type in the prior information map  258  is different from the data type sensed by in-situ sensors  208 , and the data type in the predictive map  264  is different from both the data type in the prior information map  258  and the data type sensed by the in-situ sensors  208 . For instance, the prior information map  258  may be a topographic map, and the variable sensed by the in-situ sensors  208  may be machine pitch/roll. The predictive map  264  may then be a predictive internal distribution map that maps predicted internal distribution values to different geographic locations in the field. 
     In some examples, the prior information map  258  is from a prior operation through the field and the data type is different from the data type sensed by in-situ sensors  208 , yet the data type in the predictive map  264  is the same as the data type sensed by the in-situ sensors  208 . For instance, the prior information map  258  may be a seed population map generated during planting, and the variable sensed by the in-situ sensors  208  may be stalk size. The predictive map  264  may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, the prior information map  258  may be a seeding hybrid map, and the variable sensed by the in-situ sensors  208  may be crop state such as standing crop or down crop. The predictive map  264  may then be a predictive crop state map that maps predicted crop state values to different geographic locations in the field. 
     In some examples, the prior information map  258  is from a prior operation through the field and the data type is the same as the data type sensed by in-situ sensors  208 , and the data type in the predictive map  264  is also the same as the data type sensed by the in-situ sensors  208 . For instance, the prior information map  258  may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors  208  may be yield. The predictive map  264  may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced prior information map  258  from the prior year can be used by predictive model generator  210  to generate a predictive model that models a relationship between the relative yield differences on the prior information map  258  and the yield values sensed by in-situ sensors  208  during the current harvesting operation. The predictive model is then used by predictive map generator  210  to generate a predictive yield map. 
     In some examples, predictive map  264  can be provided to the control zone generator  213 . Control zone generator  213  groups contiguous individual point data values on predictive map  264 , into control zones. A control zone may include two or more contiguous portions of an area, 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  216  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  213  parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems  216 . 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  216  or for groups of controllable subsystems  216 . 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 . In some examples, multiple crops may be simultaneously present in a field if an intercrop production system is implemented. In that case, predictive map generator  212  and control zone generator  213  are able to identify the location and characteristics of the two or more crops and then generate predictive map  264  and predictive control zone map  265  accordingly. 
     It will also be appreciated that control zone generator  213  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 only be used for controlling or calibrating agricultural harvester  100  or both. In other examples, the control zones may be presented to the operator  260  and used to control or calibrate agricultural harvester  100  and in other examples the control zones may just be presented to the operator  260  or another user or stored for later use. 
     Predictive map  264  or predictive control zone map  265  or both are provided to control system  214 , which generates control signals based upon the predictive map  264  or predictive control zone map  265  or both. In some examples, communication system controller  229  controls communication system  206  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 agricultural harvesters that are harvesting in the same field. In some examples, communication system controller  229  controls the communication system  206  to send the predictive map  264 , predictive control zone map  265 , or both to other remote systems. 
     In some examples, predictive map  264  can be provided to route/mission generator  267 . Route/mission generator  267  plots a travel path for agricultural harvester  100  to travel on during the harvesting operation based on predictive map  264 . The travel path can also include machine control settings corresponding to locations along the travel path as well. For example, if a travel path ascends a hill, then at a point prior to hill ascension, the travel path can include a control indicative of directing power to propulsion systems to maintain a speed or feed rate of agricultural harvester  100 . In some examples, route/mission generator  267  analyzes the different orientations of agricultural harvester  100  and the predicted machine characteristics that the orientations are predicted to generate according to predictive map  264 , for a plurality of different travel routes, and selects a route that has desirable results (such as, quick harvest time or desired power utilization or material distribution uniformity). 
     Operator interface controller  231  is operable to generate control signals to control operator interface mechanisms  218 . The operator interface controller  231  is 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  260 . Operator  260  may be a local operator or a remote operator. As an example, controller  231  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  260 . Controller  231  may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a power utilization displayed on the map, based on the operator&#39;s observation. Settings controller  232  can generate control signals to control various settings on the agricultural harvester  100  based upon predictive map  264 , the predictive control zone map  265 , or both. For instance, settings controller  232  can generate control signals to control machine and header actuators  248 . In response to the generated control signals, the machine and header actuators  248  operate to control, for example, one or more of the sieve and chaffer settings, thresher clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester  100  is coupled to a draper header), corn header functionality, internal distribution control and other actuators  248  that affect the other functions of the agricultural harvester  100 . Path planning controller  234  illustratively generates control signals to control steering subsystem  252  to steer agricultural harvester  100  according to a desired path. Path planning controller  234  can control a path planning system to generate a route for agricultural harvester  100  and can control propulsion subsystem  250  and steering subsystem  252  to steer agricultural harvester  100  along that route. Feed rate controller  236  can control various subsystems, such as propulsion subsystem  250  and machine actuators  248 , to control a feed rate based upon the predictive map  264  or predictive control zone map  265  or both. For instance, as agricultural harvester  100  approaches a declining terrain having an estimated speed value above a selected threshold, feed rate controller  236  may reduce the speed of machine  100  to maintain constant feed rate of biomass through the agricultural harvester  100 . Header and reel controller  238  can generate control signals to control a header or a reel or other header functionality. Draper belt controller  240  can generate control signals to control a draper belt or other draper functionality based upon the predictive map  264 , predictive control zone map  265 , or both. For example, as agricultural harvester  100  approaches a declining terrain having an estimated speed value above a selected threshold, draper belt controller  240  may increase the speed of the draper belts to prevent backup of material on the belts. Deck plate position controller  242  can generate control signals to control a position of a deck plate included on a header based on predictive map  264  or predictive control zone map  265  or both, and residue system controller  244  can generate control signals to control a residue subsystem  138  based upon predictive map  264  or predictive control zone map  265 , or both. Machine cleaning controller  245  can generate control signals to control machine cleaning subsystem  254 . For instance, as agricultural harvester  100  is about to transversely travel on a slope where it is estimated that the internal material distribution will be disproportionally on one side of cleaning subsystem  254 , machine cleaning controller  245  can adjust cleaning subsystem  254  to account for, or correct, the disproportionate material. Other controllers included on the agricultural harvester  100  can control other subsystems based on the predictive map  264  or predictive control zone map  265  or both as well. 
       FIGS.  3 A and  3 B  (collectively referred to herein as  FIG.  3   ) show a flow diagram illustrating one example of the operation of agricultural harvester  100  in generating a predictive map  264  and predictive control zone map  265  based upon prior information map  258 . 
     At  280 , agricultural harvester  100  receives prior information map  258 . Examples of prior information map  258  or receiving prior information map  258  are discussed with respect to blocks  281 ,  282 ,  284  and  286 . As discussed above, prior information map  258  maps values of a variable, corresponding to a first characteristic, to different locations in the field, as indicated at block  282 . As indicated at block  281 , receiving the prior information map  258  may involve selecting one or more of a plurality of possible prior information maps that are available. For instance, one prior information map may be a terrain profile map generated from aerial phase profilometry imagery. Another prior information map may be a map generated during a prior pass through the field which may have been performed by a different machine performing a previous operation in the field, such as a sprayer or other machine. The process by which one or more prior information maps are selected can be manual, semi-automated or automated. The prior information map  258  is based on data collected prior to a current harvesting operation. This is indicated by block  284 . For instance, the data may be collected by a GPS receiver mounted on a piece of equipment during a prior field operation. For instance, the data may be collected in a lidar range scanning operation during a previous year, or earlier in the current growing season, or at other times. The data may be based on data detected or received in ways other than using lidar range scanning. For instance, a drone equipped with a fringe projection profilometry system may detect the profile or elevation of the terrain. Or for instance, some topographic features can be estimated based on weather patterns, such as the formation of ruts due to erosion or the breakup of clumps over freeze-thaw cycles. In some examples, prior information map  258  may be created by combining data from a number of sources such as those listed above. Or for instance, the data for the prior information map  258 , such as a topographic map can be transmitted to agricultural harvester  100  using communication system  206  and stored in data store  202 . The data for the prior information map  258  can be provided to agricultural harvester  100  using communication system  206  in other ways as well, and this is indicated by block  286  in the flow diagram of  FIG.  3   . In some examples, the prior information map  258  can be received by communication system  206 . 
     Upon commencement of a harvesting operation, in-situ sensors  208  generate sensor signals indicative of one or more in-situ data values indicative of a machine characteristic, for example, power usage, machine speed, internal material distribution, grain loss, tailings or grain quality. Examples of in-situ sensors  288  are discussed with respect to blocks  222 ,  290 , and  226 . As explained above, the in-situ sensors  208  include on-board sensors  222 ; remote in-situ sensors  224 , such as UAV-based sensors flown at a time to gather in-situ data, shown in block  290 ; or other types of in-situ sensors, designated by in-situ sensors  226 . In some examples, data from on-board sensors is georeferenced using position, heading or speed data from geographic position sensor  204 . 
     Predictive model generator  210  controls the prior information variable-to-in-situ variable model generator  228  to generate a model that models a relationship between the mapped values contained in the prior information map  258  and the in-situ values sensed by the in-situ sensors  208  as indicated by block  292 . The characteristics or data types represented by the mapped values in the prior information map  258  and the in-situ values sensed by the in-situ sensors  208  may be the same characteristics or data type or different characteristics or data types. 
     The relationship or model generated by predictive model generator  210  is provided to predictive map generator  212 . Predictive map generator  212  generates a predictive map  264  that predicts a value of the characteristic sensed by the in-situ sensors  208  at different geographic locations in a field being harvested, or a different characteristic that is related to the characteristic sensed by the in-situ sensors  208 , using the predictive model and the prior information map  258 , as indicated by block  294 . 
     It should be noted that, in some examples, the prior information map  258  may include two or more different maps or two or more different map layers of a single map. Each map in the two or more different maps or each layer in the two or more different map layers of a single map, map a different type of variable to the geographic locations in the field. In such an example, predictive model generator  210  generates a predictive model that models the relationship between the in-situ data and each of the different variables mapped by the two or more different maps or the two or more different map layers. Similarly, the in-situ sensors  208  can include two or more sensors each sensing a different type of variable. Thus, the predictive model generator  210  generates a predictive model that models the relationships between each type of variable mapped by the prior information map  258  and each type of variable sensed by the in-situ sensors  208 . Predictive map generator  212  can generate a functional predictive map  263  that predicts a value for each sensed characteristic sensed by the in-situ sensors  208  (or a characteristic related to the sensed characteristic) at different locations in the field being harvested using the predictive model and each of the maps or map layers in the prior information map  258 . 
     Predictive map generator  212  configures the predictive map  264  so that the predictive map  264  is actionable (or consumable) by control system  214 . Predictive map generator  212  can provide the predictive map  264  to the control system  214  or to control zone generator  213  or both. Some examples of different ways in which the predictive map  264  can be configured or output are described with respect to blocks  296 ,  293 ,  295 ,  299  and  297 . For instance, predictive map generator  212  configures predictive map  264  so that predictive map  264  includes values that can be read by control system  214  and used as the basis for generating control signals for one or more of the different controllable subsystems of the agricultural harvester  100 , as indicated by block  296 . 
     Route/mission generator  267  plots a travel path for agricultural harvester  100  to travel on during the harvesting operation based on predictive map  204 , as indicated by block  293 . Control zone generator  213  can divide the predictive map  264  into control zones based on the values on the predictive map  264 . 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  214 , the controllable subsystems  216 , or based on wear considerations, or on other criteria as indicated by block  295 . Predictive map generator  212  configures predictive map  264  for presentation to an operator or other user. Control zone generator  213  can configure predictive control zone map  265  for presentation to an operator or other user. This is indicated by block  299 . When presented to an operator or other user, the presentation of the predictive map  264  or predictive control zone map  265  or both may contain one or more of the predictive values on the predictive map  264  correlated to geographic location, the control zones on predictive control zone map  265  correlated to geographic location, and settings values or control parameters that are used based on the predicted values on map  264  or zones on predictive control zone map  265 . 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 predictive map  264  or the zones on predictive control zone map  265  conform to measured values that may be measured by sensors on agricultural harvester  100  as agricultural harvester  100  moves through the field. Further where information is presented to more than one location, an authentication or 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, only, or the maps may also be generated at one or more remote locations. 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 machine  100  may be unable to see the information corresponding to the predictive map  264  or make any changes to machine operation. A supervisor, at a remote location, however, may be able to see the predictive map  264  on the display, but not make changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map  264  and also change the predictive map  264  that is used in machine control. This is one example of an authorization hierarchy that may be implemented. The predictive map  264  or predictive control zone map  265  or both can be configured in other ways as well, as indicated by block  297 . 
     At block  298 , input from geographic position sensor  204  and other in-situ sensors  208  are received by the control system. Block  300  represents receipt by control system  214  of an input from the geographic position sensor  204  identifying a geographic location of agricultural harvester  100 . Block  302  represents receipt by the control system  214  of sensor inputs indicative of trajectory or heading of agricultural harvester  100 , and block  304  represents receipt by the control system  214  of a speed of agricultural harvester  100 . Block  306  represents receipt by the control system  214  of other information from various in-situ sensors  208 . 
     At block  308 , control system  214  generates control signals to control the controllable subsystems  216  based on the predictive map  264  or predictive control zone map  265  or both and the input from the geographic position sensor  204  and any other in-situ sensors  208 . At block  310 , control system  214  applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems  216  that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems  216  that are controlled may be based on the type of predictive map  264  or predictive control zone map  265  or both that is being used. Similarly, the control signals that are generated and the controllable subsystems  216  that are controlled and the timing of the control signals can be based on various latencies of crop flow through the agricultural harvester  100  and the responsiveness of the controllable subsystems  216 . 
     By way of example, a generated predictive map  264  in the form of a predictive machine map can be used to control one or more subsystems  216 . For instance, the predictive machine map can include machine speed values georeferenced to locations within the field being harvested. The machine speed values from the predictive machine map can be extracted and used to control the header and feeder house speed to ensure the header  104  and feeder house  106  can process the increase of material that agricultural harvester  100  engages as it moves faster through the field. The preceding example involving machine speed using a predictive machine map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive machine map or other type of predictive map to control one or more of the controllable subsystems  216 . 
     At block  312 , a determination is made as to whether the harvesting operation has been completed. If harvesting is not completed the processing advances to block  314  where in-situ sensor data from geographic position sensor  204  and in-situ sensors  208  (and perhaps other sensors) continues to be read. 
     In some examples, at block  316 , agricultural harvester  100  can also detect learning trigger criteria to perform machine learning on one or more of the predictive map  264 , predictive control zone map  265 , the model generated by predictive model generator  210 , the zones generated by control zone generator  213 , one or more control algorithms implemented by the controllers in the control system  214 , 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  318 ,  320 ,  321 ,  322  and  324 . 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  208 . In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors  208  that exceeds a threshold triggers or causes the predictive model generator  210  to generate a new predictive model that is used by predictive map generator  212 . Thus, as agricultural harvester  100  continues a harvesting operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors  208  triggers the creation of a new relationship represented by a predictive model generated by predictive model generator  210 . Further, new predictive map  264 , predictive control zone map  265 , or both can be regenerated using the new predictive model. Block  318  represents detecting a threshold amount of in-situ sensor data used to trigger creation of a new predictive model. 
     In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors  208  are changing from previous values or from a threshold value. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map  258 ) is within a range, is less than a defined amount, or below a threshold value, then a new predictive model is not generated by the predictive model generator  210 . As a result, the predictive map generator  212  does not generate a new predictive map  264 , predictive control zone map  265 , or both. However, if variations within the in-situ sensor data exceed the range or exceed the predefined amount or the threshold value, for example, or if a relationship between the in-situ sensor data and the information in prior information map  258  varies by a defined amount, for example, then the predictive model generator  210  generates a new predictive model using all or a portion of the newly received in-situ sensor data that the predictive map generator  212  uses to generate a new predictive map  264 . At block  320 , 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 prior information map  258 , can be used as a trigger to cause generation of a new predictive model and predictive map. The threshold, the range and the defined amount can be set to default values, or set by an operator or user interaction through a user interface, or set by an automated system or in other ways. 
     Other learning trigger criteria can also be used. For instance, if predictive model generator  210  switches to a different prior information map (different from the originally selected prior information map  258 ), then switching to the different prior information map may trigger re-learning by predictive model generator  210 , predictive map generator  212 , control zone generator  213 , control system  214 , or other items. In another example, transitioning of agricultural harvester  100  to a different topography or to a different control zone may be used as learning trigger criteria as well. 
     In some instances, operator  260  can also edit the predictive map  264  or predictive control zone map  265  or both. The edits can change a value on the predictive map  264  or, change the size, shape, position or existence of a control zone, or a value on predictive control zone map  265  or both. Block  321  shows that edited information can be used as learning trigger criteria. 
     In some instances, it may also be that operator  260  observes that automated control of a controllable subsystem, is not what the operator desires. In such instances, the operator  260  may provide a manual adjustment to the controllable subsystem reflecting that the operator  260  desires the controllable subsystem to operate in a different way than is being commanded by control system  214 . Thus, manual alteration of a setting by the operator  260  can cause predictive model generator  210  to relearn a model, predictive map generator  212  to regenerate map  264 , control zone generator  213  to regenerate the control zones on predictive control zone map  265  and control system  214  to relearn its control algorithm or to perform machine learning on one of the controller components  232 - 246  in control system  214  based upon the adjustment by the operator  260 , as shown in block  322 . Block  324  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. This is indicated by block  326 . 
     If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block  326 , then one or more of the predictive model generator  210 , predictive map generator  212 , control zone generator  213  and control system  214  performs machine learning to generate a new predictive model, a new predictive map, new control zones, and a new control algorithm, respectively, based upon the learning trigger criteria. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block  328 . 
     If the harvesting operation has been completed, operation moves from block  312  to block  330  where one or more of the predictive map  264 , predictive control zone map  265 , and predictive model generated by predictive model generator  210  are stored. The predictive map  264 , predictive control zone map  265 , and predictive model may be stored locally on data store  202  or sent to a remote system using communication system  206  for later use. 
     It will be noted that while some examples herein describe predictive model generator  210  and predictive map generator  212  receiving a prior information map in generating a predictive model and a functional predictive map, respectively, in other examples, the predictive model generator  210  and predictive map generator  212  can receive other types of maps, including predictive maps, such as a functional predictive map generated during the harvesting operation. 
       FIG.  4 A  is a block diagram of a portion of the agricultural harvester  100  shown in  FIG.  1   . Particularly,  FIG.  4 A  shows, among other things, examples of the predictive model generator  210  and the predictive map generator  212  in more detail.  FIG.  4 A  also illustrates information flow among the various components shown. The predictive model generator  210  receives a topographic map  332  as a prior information map. Predictive model generator  210  also receive a geographic location  334 , or an indication of geographic location, from geographic position sensor  204 . In-situ sensors  208  illustratively include a machine sensor, such as machine sensor  336 , as well as a processing system  338 . In some instances, machine sensor  336  may be located on board the agricultural harvester  100 . The processing system  338  processes sensor data generated from on-board machine sensor  336  to generate processed data, some examples of which are described below. 
     In some examples, machine sensor  336  may generate electronic signals indicative of the characteristic that machine sensor  336  senses. Processing system  338  processes one or more of the sensor signals obtained via the machine sensor  336  to generate processed data identifying one or more machine characteristics. Machine characteristics identified by the processing system  338  may include an internal material distribution, a power usage, a power utilization, a machine speed, wheel slippage, etc. 
     In-situ sensor  208  may be or include optical sensors, such as a camera located in agricultural harvester  100  (referred to hereinafter as “process camera”) that views internal portions of agricultural harvester  100  that process the agricultural material for grain. Thus, in some examples, the processing system  338  is operable to detect the internal distribution of the agricultural material passing through the agricultural harvester  100  based on an image captured by machine sensor  208 . For instance, whether the agricultural material is distributed unevenly across the cleaning system, which could be due to machine roll or pitch. 
     In other examples, in-situ sensor  208  may be or includes a GPS that senses machine position. In this case, processing system  338  can derive speed and direction from the sensor signals as well. In another example, in-situ sensor  208  can include one or more power sensors that detect individual or aggregate power characteristics of one or more subsystems on agricultural harvester  100 . Processing system  338  in this case may aggregate or separate the power characteristic by subsystem or machine components. 
     Other machine properties and sensors may also be used. In some examples, raw or processed data from machine sensor  336  may be presented to operator  260  via operator interface mechanism  218 . Operator  260  may be onboard the agricultural harvester  100  or at a remote location. 
     As shown in  FIG.  4 A , the example predictive model generator  210  includes one or more of a power characteristic-to-topographic characteristic model generator  342 , machine speed-to-topographic characteristics model generator  344 , a material distribution-to-topographic characteristic model generator  345 , a grain loss-to-topographic characteristic model generator  346 , a tailings-to-topographic characteristic model generator  347 , and a grain quality-to-topographic characteristic model  348 . In other examples, the predictive model generator  210  may include additional, fewer, or different components than those shown in the example of  FIG.  4 A . Consequently, in some examples, the predictive model generator  210  may include other items  349  as well, which may include other types of predictive model generators to generate other types of machine characteristic models. 
     The present discussion proceeds with respect to an example in which machine sensor  336  is a power characteristic sensor, such as a hydraulic pressure sensor, voltage sensor, etc. It will be appreciated that these are just some examples, and the sensors mentioned above, as other examples of machine sensor  336 , are contemplated herein as well. Model generator  342  identifies a relationship between a power characteristic, at a geographic location corresponding to the processed data  340 , and the topographic characteristic value at the same geographic location. The topographic characteristic value is the georeferenced value contained in the topographic map  332 . Model generator  342  then generates a predictive machine model  350  that is used by power characteristic map generator  352  to predict power characteristics at a location in the field based upon the topographic characteristics for that location in the field. For instance, the power usage is sensed by the in-situ sensor  208  and the predictive map generator  352  outputs the estimated power usage requirements at various places in the field. 
     The present discussion proceeds with respect to an example in which machine sensor  336  is a machine speed sensor, such as a global positioning system device, speedometer, compass, etc. It will be appreciated that these are just some examples, and the sensors mentioned above, as other examples of machine sensor  336 , are contemplated herein as well. Model generator  344  identifies a relationship between a machine speed, at a geographic location corresponding to the processed sensor data  340 , and the topographic characteristic value at the same geographic location. Again, the topographic characteristic value is the georeferenced value contained in the topographic map  332 . Model generator  344  then generates a predictive machine model  350  that is used by machine speed map generator  354  to predict machine speeds at a location in the field based upon the topographic characteristic value for that location in the field. For instance, the machine speed and direction is sensed by the in-situ sensor  208  and the predictive map generator  354  outputs the estimated machine speed and direction at various places in the field. 
     The present discussion proceeds with respect to an example in which machine sensor  336  is an optical sensor. It will be appreciated that this is just one example, and the sensors mentioned above, as other examples of machine sensor  336 , are contemplated herein as well. Model generator  345  identifies a relationship between material distribution detected in processed data  340  (e.g., the material distribution in agricultural harvester  100  can be identified based on data captured by an optical sensor), at a geographic location corresponding to where the sensor data were obtained, and topographic characteristics from the topographic map  332  corresponding to the same location in the field where the material distribution was detected. Based on this relationship established by model generator  345 , model generator  345  generates a predictive machine model  350 . The predictive machine model  350  is used by material distribution map generator  355  to predict material distribution at different locations in the field based upon the georeferenced topographic characteristic contained in the topographic map  332  at the same locations in the field. 
     The present discussion proceeds with respect to an example in which machine sensor  336  is a grain loss sensor. It will be appreciated that this is just one example, and the sensors mentioned above, as other examples of machine sensor  336 , are contemplated herein as well. Model generator  346  identifies a relationship between grain loss detected in processed data  340  at a geographic location corresponding to where the sensor data was geolocated, and topographic characteristics from the topographic map  332  corresponding to the same location in the field where the grain loss was geolocated. Based on this relationship established by model generator  346 , model generator  346  generates a predictive machine model  350 . The predictive machine model  350  is used by grain loss map generator  356  to predict grain loss at different locations in the field based upon the georeferenced topographic characteristic contained in the topographic map  332  at the same locations in the field. 
     The present discussion proceeds with respect to an example in which machine sensor  336  is a tailings sensor. It will be appreciated that this is just one example, and the sensors mentioned above, as other examples of machine sensor  336 , are contemplated herein as well. Model generator  347  identifies a relationship between tailings detected in processed data  340  at a geographic location corresponding to where the sensor data was geolocated, and topographic characteristics from the topographic map  332  corresponding to the same location in the field where the tailings characteristic was geolocated. Based on this relationship established by model generator  347 , model generator  347  generates a predictive machine model  350 . The predictive machine model  350  is used by tailings map generator  357  to predict tailings characteristics at different locations in the field based upon the georeferenced topographic characteristic contained in the topographic map  332  at the same locations in the field. 
     The present discussion proceeds with respect to an example in which machine sensor  336  is a grain quality sensor. It will be appreciated that this is just one example, and the sensors mentioned above, as other examples of machine sensor  336 , are contemplated herein as well. Model generator  348  identifies a relationship between grain quality detected in processed data  340  at a geographic location corresponding to where the sensor data was geolocated, and topographic characteristics from the topographic map  332  corresponding to the same location in the field where the grain quality was geolocated. Based on this relationship established by model generator  348 , model generator  348  generates a predictive machine model  350 . The predictive machine model  350  is used by grain quality map generator  358  to predict grain quality at different locations in the field based upon the georeferenced topographic characteristic contained in the topographic map  332  at the same locations in the field. 
     The predictive model generator  210  is operable to produce a plurality of predictive machine models, such as one or more of the predictive machine models generated by model generators  342 ,  344  and  345 . In another example, two or more of the predictive machine models  342 ,  344  and  345  described above may be combined into a single predictive machine model that predicts two or more machine characteristics of, for instance, material distribution, power characteristics, tailings characteristics, grain loss, grain quality and machine speed based upon the topographic characteristics at different locations in the field. Any of these machine models, or combinations thereof, are represented collectively by machine model  350  in  FIG.  4 A . 
     The predictive machine model  350  is provided to predictive map generator  212 . In the example of  FIG.  4 A , predictive map generator  212  includes a power characteristic map generator  352 , a machine speed map generator  354 , a material distribution map generator  355 , a grain loss map generator  356 , a tailings map generator  357 , and a grain quality map generator  358 . In other examples, the predictive map generator  212  may include additional, fewer, or different map generators. Thus, in some examples, the predictive map generator  212  may include other items  359  which may include other types of map generators to generate machine characteristic maps for other types of machine characteristics. 
     Power characteristic map generator  352  receives the predictive machine model  350 , which predicts power characteristics based upon a topographic characteristics from the topographic map  332 , and generates a predictive map that predicts the power characteristics at different locations in the field. For example, the predicted power characteristic could include a predicted required power. 
     Machine speed map generator  354  generates a predictive map that predicts machine speed at different locations in the field based upon the machine speed value at those locations in the field and the predictive machine model  350 . 
     Material distribution map generator  355  illustratively generates a material distribution map  360  that predicts material distribution at different locations in the field based upon the topographic characteristics at those locations in the field and the predictive machine model  350 . 
     Grain loss map generator  356  illustratively generates a grain loss map  360  that predicts grain loss at different locations in the field based upon the topographic characteristics at those locations in the field and the predictive machine model  350 . 
     Tailings map generator  357  illustratively generates a tailings map  360  that predicts tailings characteristics at different locations in the field based upon the topographic characteristics at those locations in the field and the predictive machine model  350 . 
     Grain quality map generator  358  illustratively generates a grain quality map  360  that predicts a characteristic indicative of grain quality at different locations in the field based upon the topographic characteristics at those locations in the field and the predictive machine model  350 . 
     Predictive map generator  212  outputs one or more predictive machine characteristic maps  360  that are predictive of a machine characteristic. Each of the predictive machine characteristic maps  360  predicts the respective machine characteristic at different locations in a field. Each of the generated predictive machine characteristic maps  360  may be provided to control zone generator  213 , control system  214 , or both. Control zone generator  213  generates control zones and incorporates those control zones into the functional predictive map, i.e., predictive map  360 , to produce predictive control zone map  265 . One or both of predictive map  264  and predictive control zone map  265  may be provided to control system  214 , which generates control signals to control one or more of the controllable subsystems  216  based upon the predictive map  264 , predictive control zone map  265 , or both. 
       FIG.  4 B  is a block diagram showing some examples of real-time (in-situ) sensors  208 . Some of the sensors shown in  FIG.  4 B , or different combinations of them, may have both a sensor  336  and a processing system  338 . Some of the possible in-situ sensors  208  shown in  FIG.  4 B  are shown and described above with respect to previous FIGS. and are similarly numbered.  FIG.  4 B  shows that in-situ sensors  208  can include operator input sensors  980 , machine sensors  982 , harvested material property sensors  984 , field and soil property sensors  985 , environmental characteristic sensors  987 , and they may include a wide variety of other sensors  226 . Non-machine sensors  983  include, operator input sensor(s)  980 , harvested material property sensor(s)  984 , field and soil property sensor(s)  985 , environmental characteristic sensor(s)  987  and can include other sensors  226  as well. Operator input sensors  980  may be sensors that sense operator inputs through operator interface mechanisms  218 . Therefore, operator input sensors  980  may sense user movement of linkages, joysticks, a steering wheel, buttons, dials, or pedals. Operator input sensors  980  can also sense user interactions with other operator input mechanisms, such as with a touch sensitive screen, with a microphone where speech recognition is utilized, or any of a wide variety of other operator input mechanisms. 
     Machine sensors  982  may sense different characteristics of agricultural harvester  100 . For instance, as discussed above, machine sensors  982  may include machine speed sensors  146 , separator loss sensor  148 , clean grain camera  150 , forward looking image capture mechanism  151 , loss sensors  152  or geographic position sensor  204 , examples of which are described above. Machine sensors  982  can also include machine setting sensors  991  that sense machine settings. Some examples of machine settings were described above with respect to  FIG.  1   . Front-end equipment (e.g., header) position sensor  993  can sense the position of the header  102 , reel  164 , cutter  104 , or other front-end equipment relative to the frame of agricultural harvester  100 . For instance, sensors  993  may sense the height of header  102  above the ground. Machine sensors  982  can also include front-end equipment (e.g., header) orientation sensors  995 . Sensors  995  may sense the orientation of header  102  relative to agricultural harvester  100 , or relative to the ground. Machine sensors  982  may include stability sensors  997 . Stability sensors  997  sense oscillation or bouncing motion (and amplitude) of agricultural harvester  100 . Machine sensors  982  may also include residue setting sensors  999  that are configured to sense whether agricultural harvester  100  is configured to chop the residue, produce a windrow, or deal with the residue in another way. Machine sensors  982  may include cleaning shoe fan speed sensor  951  that senses the speed of cleaning fan  120 . Machine sensors  982  may include concave clearance sensors  953  that sense the clearance between the rotor  112  and concaves  114  on agricultural harvester  100 . Machine sensors  982  may include chaffer clearance sensors  955  that sense the size of openings in chaffer  122 . The machine sensors  982  may include threshing rotor speed sensor  957  that senses a rotor speed of rotor  112 . Machine sensors  982  may include rotor pressure sensor  959  that senses the pressure used to drive rotor  112 . Machine sensors  982  may include sieve clearance sensor  961  that senses the size of openings in sieve  124 . The machine sensors  982  may include MOG moisture sensor  963  that senses a moisture level of the MOG passing through agricultural harvester  100 . Machine sensors  982  may include machine orientation sensor  965  that senses the orientation of agricultural harvester  100 . Machine sensors  982  may include material feed rate sensors  967  that sense the feed rate of material as the material travels through feeder house  106 , clean grain elevator  130 , or elsewhere in agricultural harvester  100 . Machine sensors  982  can include biomass sensors  969  that sense the biomass traveling through feeder house  106 , through separator  116 , or elsewhere in agricultural harvester  100 . The machine sensors  982  may include fuel consumption sensor  971  that senses a rate of fuel consumption over time of agricultural harvester  100 . Machine sensors  982  may include power utilization sensor  973  that senses power utilization in agricultural harvester  100 , such as which subsystems are utilizing power, or the rate at which subsystems are utilizing power, or the distribution of power among the subsystems in agricultural harvester  100 . Machine sensors  982  may include tire pressure sensors  977  that sense the inflation pressure in tires  144  of agricultural harvester  100 . Machine sensor  982  may include a wide variety of other machine performance sensors, or machine characteristic sensors, indicated by block  975 . The machine performance sensors and machine characteristic sensors  975  may sense machine performance or characteristics of agricultural harvester  100 . 
     Harvested material property sensors  984  may sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester  100 . The crop properties may include such things as crop type, crop moisture, grain quality (such as broken grain), MOG levels, grain constituents such as starches and protein, MOG moisture, and other crop material properties. Other sensors could sense straw “toughness”, adhesion of corn to ears, and other characteristics that might be beneficially used to control processing for better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, etc. 
     Field and soil property sensors  985  may sense characteristics of the field and soil. The field and soil properties may include soil moisture, soil compactness, the presence and location of standing water, soil type, and other soil and field characteristics. 
     Environmental characteristic sensors  987  may sense one or more environmental characteristics. The environmental characteristics may include such things as wind direction and wind speed, precipitation, fog, dust level or other obscurants, or other environmental characteristics. 
     In some examples, one or more of the sensors shown in  FIG.  4 B  are processed to receive processed data  340  and used inputs to model generator  210 . Model generator  210  generates a model indicative of the relationship between the sensor data and one or more of the prior or predictive information maps. The model is provided to map generator  212  that generates a map that maps predictive sensor data values corresponding to the sensor from  FIG.  4 B  or a related characteristic. 
       FIG.  5    is a flow diagram of an example of operation of predictive model generator  210  and predictive map generator  212  in generating the predictive machine model  350  and the predictive machine characteristic map  360 . At block  362 , predictive model generator  210  and predictive map generator  212  receive a prior topographic map  332 . At block  364 , processing system  338  receives one or more sensor signals from machine sensor  336 . As discussed above, the machine sensor  336  may be a power sensor  366 , a speed sensor  368 , a material distribution sensor  370  or another type of sensor  371 . 
     At block  372 , processing system  338  processes the one or more received sensor signals to generate data indicative of a characteristic of the machine. In some instances, as indicated at block  374 , the sensor data may be indicative of a power characteristic. In some instances, as indicated at block  378 , the sensor data may be indicative of the agricultural harvester speed. In some instances, as indicated at block  379 , the sensor data (e.g., an image or plurality of images) may be indicative of material distribution within agricultural harvester. The sensor data can include other data as well as indicated by block  380 . 
     At block  382 , predictive model generator  210  also obtains the geographic location corresponding to the sensor data. For instance, the predictive model generator  210  can obtain the geographic position from geographic position sensor  204  and determine, based upon machine delays, machine speed, etc., a precise geographic location where the sensor data  340  was captured or derived. Additionally, at block  382 , the orientation of the agricultural harvester  100  to the topographic feature may be determined. The orientation of agricultural harvester  100  is obtained, for instance, because a machine at a sloped position may exhibit different machine characteristics based on its orientation relative to the slope. 
     At block  384 , predictive model generator  210  generates one or more predictive machine models, such as machine model  350 , that model a relationship between a topographic characteristic obtained from a prior information map, such as prior information map  258 , and a machine characteristic being sensed by the in-situ sensor  208  or a related characteristic. For instance, predictive model generator  210  may generate a predictive machine model that models the relationship between a topographic characteristic and a sensed machine characteristic indicated by sensor data  340  obtained from in-situ sensor  208 . 
     At block  386 , the predictive machine model, such as predictive machine model  350 , is provided to predictive map generator  212  which generates a predictive machine characteristic map  360  that maps a predicted machine characteristic based on the topographic map and the predictive machine model  350 . In some examples, the predictive machine characteristic map  360  predicts power characteristics, as indicated by block  387 . In some examples, the predictive machine characteristic map  360  predicts machine speed, as indicated by block  388 . In some examples, the predictive machine characteristic map  360  predicts material distribution in the harvester, as indicated by block  389 . In some examples, the predictive machine characteristic map  360  predicts tailings characteristics, such as tailings flow, tailings level and tailings content, as indicated by block  390 . In some examples, the predictive machine characteristic map  360  predicts grain loss, as indicated by block  391 . In some examples, the predictive machine characteristic map  360  predicts grain quality, as indicated by block  392 . Still in other examples, the predictive map  360  predicts other items or a combination of the above items, as indicated by block  393 . 
     The predictive machine characteristic map  360  can be generated during the course of an agricultural operation. Thus, as an agricultural harvester is moving through a field performing an agricultural operation, the predictive machine characteristic map  360  is generated as the agricultural operation is being performed. 
     At block  394 , predictive map generator  212  outputs the predictive machine characteristic map  360 . At block  391  predictive machine characteristic map generator  212  outputs the predictive machine characteristic map for presentation and possible interaction by operator  260 . At block  393 , predictive map generator  212  may configure the map for consumption by control system  214 . At block  395 , predictive map generator  212  can also provide the map  360  to control zone generator  213  for generation of control zones. At block  397 , predictive map generator  212  configures the map  360  in other ways as well. The predictive machine characteristic map  360  (with or without the control zones) is provided to control system  214 . At block  396 , control system  214  generates control signals to control the controllable subsystems  216  based upon the predictive machine characteristic map  360 . 
     It can thus be seen that the present system takes a prior information map that maps a characteristic such as a topographic characteristic information to different locations in a field. The present system also uses one or more in-situ sensors that sense in-situ sensor data that is indicative of a machine characteristic, such as power usage, machine speed, material distribution, grain loss, tailings or grain quality, and generates a model that models a relationship between the machine characteristic sensed using the in-situ sensor, or a related characteristic, and the characteristic mapped in the prior information map. Thus, the present system generates a functional predictive map using a model, in-situ data, and a prior information map and may configure the generated functional predictive map for consumption by a control system or for presentation to a local or remote operator or other user. For example, the control system may use the map to control one or more systems of an agricultural harvester. 
     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, 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.  6    is a block diagram of agricultural harvester  600 , which may be similar to agricultural harvester  100  shown in  FIG.  2   . The agricultural harvester  600  communicates with elements in a remote server architecture  500 . In some examples, remote server architecture  500  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.  2    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.  6   , some items are similar to those shown in  FIG.  2    and those items are similarly numbered.  FIG.  6    specifically shows that predictive model generator  2102  or predictive map generator  212 , or both, may be located at a server location  502  that is remote from the agricultural harvester  600 . Therefore, in the example shown in  FIG.  6   , agricultural harvester  600  accesses systems through remote server location  502 . 
       FIG.  6    also depicts another example of a remote server architecture.  FIG.  6    shows that some elements of  FIG.  2    may be disposed at a remote server location  502  while others may be located elsewhere. By way of example, data store  202  may be disposed at a location separate from location  502  and accessed via the remote server at location  502 . Regardless of where the elements are located, the elements can be accessed directly by agricultural harvester  600  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 combine harvester  600  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 combine harvester  600  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 agricultural harvester  600  until the agricultural harvester  600  enters an area having wireless communication coverage. The agricultural harvester  600 , itself, may send the information to another network. 
     It will also be noted that the elements of  FIG.  2   , 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  500  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.  7    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 hand held 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 agricultural harvester  100  for use in generating, processing, or displaying the maps discussed above.  FIGS.  8 - 9    are examples of handheld or mobile devices. 
       FIG.  7    provides a general block diagram of the components of a client device  16  that can run some components shown in  FIG.  2   , 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.  8    shows one example in which device  16  is a tablet computer  600 . In  FIG.  8   , computer  600  is shown with user interface display screen  602 . Screen  602  can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. Tablet computer  600  may also use an on-screen virtual keyboard. Of course, computer  600  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  600  may also illustratively receive voice inputs as well. 
       FIG.  9    is similar to  FIG.  8    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.  10    is one example of a computing environment in which elements of  FIG.  2     can  be deployed. With reference to  FIG.  10   , an example system for implementing some embodiments includes a computing device in the form of a computer  810  programmed to operate as discussed above. Components of computer  810  may include, but are not limited to, a processing unit  820  (which can comprise processors or servers from previous FIGS.), a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  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.  2    can be deployed in corresponding portions of  FIG.  10   . 
     Computer  810  typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed by computer  810  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  810 . 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  830  includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG.  10    illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG.  10    illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive  855 , and nonvolatile optical disk  856 . The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     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.  10   , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG.  10   , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . Note that these components can either be the same as or different from operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , 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  820  through a user input interface  860  that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  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  880 . 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.  FIG.  10    illustrates, for example, that remote application programs  885  can reside on remote computer  880 . 
     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.