Patent Publication Number: US-2022232763-A1

Title: Machine control using a map with regime zones

Description:
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 harvesters can also be fitted with different types of heads to harvest different types of crops. 
     A variety of different conditions in fields have a number of deleterious effects on the harvesting operation. Therefore, an operator may attempt to modify control of the harvester, upon encountering such conditions 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 
     A map obtainable by an agricultural harvester contains regime zones with settings resolvers. A plurality of different target actuator settings corresponding to the geographic location of the agricultural work machine are identified. A regime zone is identified based on the geographic location of the agricultural harvester. One of the plurality of different target actuator settings is selected as a resolved target actuator setting based on the settings resolver corresponding to the identified regime zone and is used to control the agricultural harvester. s 
     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. 3A-3B  (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  is a block diagram showing one example of a predictive model generator and a predictive map generator. 
         FIG. 5  is a flow diagram showing an example of operation of an agricultural harvester in receiving a map, detecting a characteristic, and generating a functional predictive map for use in controlling the agricultural harvester during a harvesting operation. 
         FIG. 6  is a block diagram showing one example of a predictive model generator and a predictive map generator. 
         FIG. 7  shows a flow diagram illustrating one example of the operation of an agricultural harvester in receiving a map and detecting an in-situ sensor input in generating a functional predictive sensor data map. 
         FIG. 8  is a block diagram showing some examples of in-situ sensors. 
         FIG. 9  is a block diagram showing one example of a control zone generator. 
         FIG. 10  is a flow diagram illustrating one example of the operation of the control zone generator shown in  FIG. 9 . 
         FIG. 11  illustrates a flow diagram showing an example of the operation of a control system in selecting a target settings value to control the agricultural harvester. 
         FIG. 12  is a block diagram showing one example of an operator interface controller. 
         FIG. 13  is a flow diagram illustrating one example of an operator interface controller. 
         FIG. 14  is a pictorial illustration showing one example of an operator interface display. 
         FIG. 15  is a block diagram showing one example of an agricultural harvester in communication with a remote server environment. 
         FIGS. 16-18  show examples of mobile devices that can be used in an agricultural harvester. 
         FIG. 19  is a block diagram showing one example of a computing environment that can be used in an agricultural harvester and 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. In some examples, the predictive map can be used to control an agricultural work machine, such as an agricultural harvester. It may improve the performance of the agricultural harvester to control the operation of the agricultural harvester based upon the different conditions in the field that the agricultural harvester may engage. For instance, if the crops have reached maturity, the weeds may still be green, thus increasing the moisture content of the biomass that is encountered by the agricultural harvester. This problem may be exacerbated when the weed patches are wet (such as shortly after a rainfall or when weed patches contain dew) and before the weeds have had a chance to dry. Thus, when the agricultural harvester encounters an area of increased biomass, the operator may adjust the operation of the agricultural harvester to maintain a constant feed rate of material through the agricultural harvester. Maintaining a constant feed rate may maintain the performance of the agricultural harvester. Performance of an agricultural harvester may be deleteriously affected based on a number of different criteria. Such different criteria may include changes in biomass, crop state, topography, soil properties, and seeding characteristics, or other conditions. Therefore, it may also be useful to control the operation of the agricultural harvester based on other conditions that may be present in the field. For example, the performance of the agricultural harvester may be maintained at an acceptable level by controlling the operation of the agricultural harvester based on the biomass encountered by the agricultural harvester, the crop state of the crop being harvested, the topography of the field being harvested, soil properties of soil in the field being harvested, seeding characteristics in the field being harvested, yield in the field being harvested, or other conditions that are present in the field. 
     Also, given a particular operational setting of a particular subsystem of the agricultural harvester, it may be desirable to control other controllable subsystems on the agricultural harvester, in a particular way. For example, if the agricultural harvester is traveling at a first speed, then it may be desirable to have the header at a first height whereas if the agricultural harvester is operating at a second speed it may be desirable to have the header at a second height to maintain feed rate of material through the agricultural harvester at a desirable feed rate. 
     Some current systems provide vegetative index maps. A vegetative index map illustratively maps vegetative index values (which may be indicative of vegetative growth) across different geographic locations in a field of interest. One example of a vegetative index includes a normalized difference vegetation index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitations, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum. 
     A vegetative index map can be used to identify the presence and location of vegetation. In some examples, these maps enable vegetation to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, including crop or other weeds. 
     In some examples, a biomass map is provided. A biomass map illustratively maps a measure of biomass in the field being harvested at different locations in the field. A biomass map may be generated from vegetative index values, from historically measured or estimated biomass levels, from images or other sensor readings taken during a previous operation in the field, or in other ways. In some examples, biomass may be adjusted by a factor representing a portion of total biomass passing through the agricultural harvester. For corn, this factor is typically around 50%. For moisture in harvested crop material, this factor is typically 10%-30%. In some examples, the factor may represent a portion of weed material or weed seeds. 
     In some examples, a crop state map is provided. Crop state may define whether the crop is down, standing, partially down, the orientation of down or partially down crop, and other things. A crop state map illustratively maps the crop state in the field being harvested at different locations in the field. A crop state map may be generated from aerial or other images of the field, from images or other sensor readings taken during a prior operation in the field or in other ways prior to harvesting. 
     In some examples, a seeding map is provided. A seeding map may map seeding characteristics such as seed locations, seed variety, or seed population to different locations in the field. The seeding map may be generated during a past seed planting operation in the field. The seeding map may be derived from control signals used by a seeder when planting seeds or from sensors on the seeder that confirm that a seed was planted. Seeders may also include geographic position sensors that geolocate the seed characteristics on the field. 
     In some examples, a soil property map is provided. A soil property map illustratively maps a measure of one or more soil properties such as soil type or soil moisture in the field being harvested at different locations in the field. A soil properties map may be generated from vegetative index values, from historically measured or estimated soil properties, from images or other sensor readings taken during a previous operation in the field, or in other ways. 
     In some examples, other prior information maps are provided. Such prior information maps can include a topographic map of the field being harvested, a predictive yield map for the field being harvested, or other prior information maps. 
     The present discussion thus proceeds, in some examples, with respect to systems that receive a prior information map of a field or a map generated during a prior operation and also use an in-situ sensor to detect a variable indicative of one or more of a machine speed and an output from a feed rate control system. The systems generate a model that models a relationship between the prior information values on the prior information map and the output values from the in-situ sensor. The model is used to generate a functional predictive speed map that predicts, for example, a target machine speed at different locations in the field. The functional predictive speed map, generated during the harvesting operation, can be presented to an operator or other user or used in automatically controlling an agricultural harvester during the harvesting operation, or both. 
     The present discussion also proceeds, in some examples, with respect to systems that receive a speed map that maps predicted machine speed values to different geographic locations in the field and also use an in-situ sensor to detect a characteristic. The systems generate a model that models a relationship between values on the speed map and the characteristic values output by the in-situ sensor. The model is used to generate a functional predictive map that predicts characteristic values at different locations in the field. The functional predictive data map, generated during the harvesting operation, can be presented to an operator and used in automatically controlling an agricultural harvester during the harvesting operation. 
       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 selected 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 machine 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 . 
     Machine speed sensor  146  senses the travel speed of agricultural harvester  100  over the ground. Machine 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 axel, 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, or a wide variety of other systems or sensors that provide an indication of travel speed. 
     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. 
     Prior to describing how agricultural harvester  100  generates a functional predictive speed map, and uses the functional predictive speed 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 such as one or more characteristics of the field itself, one or more crop characteristics of harvested material, such as crop or grain present in the field, one or more environmental characteristics of the environment of the agricultural harvester or one or more characteristics of the agricultural harvester. 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. Crop characteristics can include one or more crop properties such as crop height, crop moisture, crop density, crop state; as well as characteristics of grain properties such as grain moisture, grain size, and grain test weight. Environmental characteristics can include weather characteristics, and the presence of standing water. Characteristics of the agricultural harvester can include characteristics indicative of machine settings or operator inputs or characteristics of machine operation such as machine speed, outputs from different controllers, 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 or values derived therefrom and the speed 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 may be used for controlling a machine, such as one or more subsystems of an agricultural harvester. 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 may be presented to a user visually, such as via a display, haptically, or audibly. The user may interact with the functional predictive map to perform editing operations and other user interface operations. In some instances, a functional predictive map can be used for one or more of 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 speed 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 . Then, using a speed map to control harvesting agricultural harvester  100  is described. 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 characteristics of a field concurrent with a harvesting operation. 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 information map  258  includes, for example, a vegetative index map, a biomass map, a crop state map, a topographic map, a soil property map, a seeding map, or a map from a prior operation. However, prior information map  258  may also encompass other types of data that were obtained prior to a harvesting operation or a map from a prior operation.  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, any of the sensors discussed above with respect to  FIG. 1 , a perception sensor (e.g., a forward looking mono or stereo camera system and image processing system), image sensors that are internal to agricultural harvester  100  (such as the clean grain camera or cameras mounted to identify material that is exiting agricultural harvester  100  through the residue subsystem or from the cleaning subsystem). 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. More examples of in-situ sensors  208  are described below with respect to  FIG. 8 . 
     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 metric mapped to the field by the prior information map  258 . For example, if the prior information map  258  maps a vegetative index value to different locations in the field, and the in-situ sensor  208  is sensing a value indicative of machine speed, then prior information variable-to-in-situ variable model generator  228  generates a predictive speed model that models the relationship between the vegetative index value and the machine speed value. The predictive speed model can also be generated based on vegetative index values 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 speed model generated by predictive model generator  210  to generate a functional predictive speed map that predicts the target machine speed 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. 
     Continuing with the preceding example, in which prior information map  258  is a vegetative index map and in-situ sensor  208  senses a value indicative of machine speed predictive map generator  212  can use the vegetative index values in prior information map  258 , and the model generated by predictive model generator  210 , to generate a functional predictive map  263  that predicts a target machine speed 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 vegetative index value and machine speed, then, given the vegetative index value at different locations across the field, predictive map generator  212  generates a predictive map  264  that predicts the target machine speed value at different locations across the field. The vegetative index value, obtained from the vegetative index map, at those locations and the relationship between vegetative index value and machine speed, obtained from the predictive model, are used to generate the predictive map  264 . 
     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 vegetative index map, 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 another example, the prior information map  258  may be a vegetative index map, and the variable sensed by the in-situ sensors  208  may be crop height. The predictive map  264  may then be a predictive crop height map that maps predicted crop height 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 vegetative index map, and the variable sensed by the in-situ sensors  208  may be crop height. The predictive map  264  may then be a predictive biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the prior information map  258  may be a vegetative index map, and the variable sensed by the in-situ sensors  208  may be yield. The predictive map  264  may then be a predictive speed map that maps predicted harvester speed values to different geographic locations in the field. 
     In some examples, the prior information map  258  is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors  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 pass through the field during a prior operation 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 another example, the prior information map  258  may be a weed intensity map generated during a prior operation, such as from a sprayer, and the variable sensed by the in-situ sensors  208  may be weed intensity. The predictive map  264  may then be a predictive weed intensity map that maps predicted weed intensity values to different geographic locations in the field. In such an example, a map of the weed intensities at time of spraying is geo-referenced recorded and provided to agricultural harvester  100  as a prior information map  258  of weed intensity. In-situ sensors  208  can detect weed intensity at geographic locations in the field and predictive model generator  210  may then build a predictive model that models a relationship between weed intensity at time of harvest and weed intensity at time of spraying. This is because the sprayer will have impacted the weed intensity at time of spraying, but weeds may still crop up in similar areas again by harvest. However, the weed areas at harvest are likely to have different intensity based on timing of the harvest, weather, weed type, among other things. 
     In some examples, predictive map  264  can be provided to the control zone generator  213 . Control zone generator  213  groups adjacent portions of an area into one or more control zones based on data values of predictive map  264  that are associated with those adjacent portions. A control zone may include two or more contiguous portions of 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 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 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. 
     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 weed type 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 one example, settings controller  232  can control a sensitivity setting which controls the responsiveness of control system  214  in controlling the position (such as height, tilt, or roll), in response to header position error, to comply with a header position setting such as a header height setting, a header tilt setting, or a header roll setting. 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, concave 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), such as draper belt speed, corn header functionality, internal distribution control and other actuators  248  that affect the other functions of the agricultural harvester  100 . In some examples, machine and header actuators  248  can be controlled to adjust a backshaft speed (also referred to as the header drive speed). For instance, in the example of a corn header, the backshaft speed can be adjusted to control the speed of one or more of stalk rolls, gathering chains, and an auger on the corn header. In some examples, machine and header actuators  248  can include a rotatable output mechanism, such as a drive shaft, the output of which can be controlled to control the backshaft speed. In some examples, machine and header actuators  248  can be controlled to adjust a speed of reel  164 . 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  may receive a variety of different inputs indicative of a feed rate of material through agricultural harvester  100  and can control various subsystems, such as propulsion subsystem  250  and machine actuators  248 , to control the feed rate based upon the predictive map  264  or predictive control zone map  265  or both. For instance, as agricultural harvester  100  approaches a weed patch having an intensity value above a selected threshold, feed rate controller  236  may generate a control signal to control propulsion subsystem  252  to reduce the speed of agricultural harvester  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, such as the position of the header or a speed of the reel. Draper belt controller  240  can generate control signals to control a draper belt or other draper functionality, such as draper belt speed, based upon the predictive map  264 , predictive control zone map  265 , or both. 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, based upon the different types of seeds or weeds passed through agricultural harvester  100 , a particular type of machine cleaning operation or a frequency with which a cleaning operation is performed may be controlled. 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. 3A and 3B  (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 vegetative index map generated from aerial 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 a planting machine or seeding machine 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 based on aerial images taken during a previous year, or earlier in the current growing season, or at other times. The data may be based on data detected in ways other than using aerial images. For instance, agricultural harvester  100  may be fitted with a sensor, such as an internal optical sensor, that identifies weed seeds or other types of material exiting agricultural harvester  100 . The weed seed or other data detected by the sensor during a previous year&#39;s harvest may be used as data used to generate the prior information map  258 . The sensed weed data or other data may be combined with other data to generate the prior information map  258 . For example, based upon a magnitude of the weed seeds exiting agricultural harvester  100  at different locations and based upon other factors, such as whether the seeds are being spread by a spreader or dropped in a windrow; the weather conditions, such as wind, when the seeds are being dropped or spread; drainage conditions which may move seeds around in the field; or other information, the location of those weed seeds can be predicted so that the prior information map  258  maps the predicted seed locations in the field. The data for the prior information map  258  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 of a characteristic, such as a speed characteristic, as indicated by block  288 . 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 layer may represent a different data type from the data type of another map layer or the map layers may have the same data type that were obtained at different times. Each map in the two or more different maps or each layer in the two or more different map layers of a map maps 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 ,  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 . 
     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 , 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 and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers 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, such as a supervisor at a remote location, however, may be able to see the predictive map  264  on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map  264  and also be able to change the predictive map  264 . In some instances, the predictive map  264  accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. The 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. Particularly, at block  300 , control system  214  detects 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 speed map can be used to control one or more subsystems  216 . For instance, the predictive speed map can include speed values georeferenced to locations within the field being harvested. The speed values from the predictive speed map can be extracted and used to control one or more of the controllable subsystems  216 , for example, the propulsion subsystem  250 . By controlling the propulsion subsystem  250 , a feed rate of material moving through the agricultural harvester  100  can be controlled. In other examples, header or other machine actuators  248  can be controlled, for example, to control, for instance, the backshaft speed, draper belt speed, or the reel speed. Similarly, header or other machine actuators  248  can be controlled to control the position of the header, such as the header height, header tilt, or header roll. For instance, the header height can be controlled to take in more or less material, and, thus, the header height can also be controlled to control feed rate of material through the agricultural harvester  100 . In other examples, if the predictive map  264  maps weed height relative to positions in the field, control of the header height can be implemented. For example, if the values present in the predictive weed map indicate one or more areas having weed height with a first height amount, then header and reel controller  238  can control the header height so that the header is positioned above the first height amount of the weeds within the one or more areas having weeds at the first height amount when performing the harvesting operation. Thus, the header and reel controller  238  can be controlled using georeferenced values present in the predictive weed map to position the header to a height that is above the predicted height values of weeds obtained from the predictive weed map. Further, the header height can be changed automatically by the header and reel controller  238  as the agricultural harvester  100  proceeds through the field using georeferenced values obtained from the predictive weed map. The preceding example involving weed height and intensity using a predictive weed map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive weed 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) continue 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 trigger 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, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map  258 ) are within a selected range or 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 are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator  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. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways. 
     Other learning trigger criteria can also be used. For instance, if predictive model generator  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 , change a size, shape, position, or existence of a control zone 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 one or more of predictive model generator  210  to relearn a model, predictive map generator  212  to regenerate map  264 , control zone generator  213  to regenerate one or more control zones on predictive control zone map  265 , and control system  214  to relearn a control algorithm or to perform machine learning on one or more of the controller components  232  through  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, as 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, a new control zone, 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, in generating a predictive model and a functional predictive map, respectively, other types of maps, including predictive maps, such as a functional predictive map generated during the harvesting operation. 
       FIG. 4  is a block diagram of a portion of the agricultural harvester  100  shown in  FIG. 1 . Particularly,  FIG. 4  shows, among other things, examples of the predictive model generator  210  and the predictive map generator  212  in more detail.  FIG. 4  also illustrates information flow among the various components shown. The predictive model generator  210  receives prior information map  258 , which may be a vegetative index map  332 , a predictive yield map  333 , a biomass map  335 , a crop state map  337 , a topographic map  339 , a soil property map  341  or a seeding map  343  as a prior information map. Predictive model generator  210  also receives a geographic location  334 , or an indication of a geographic location, from geographic position sensor  204 . In-situ sensors  208  illustratively include machine speed sensor  146 , or a sensor  336  that senses an output from feed rate controller  236 , as well as a processing system  338 . The processing system  338  processes sensor data generated from machine speed sensor  146  or from sensor  336 , or both, to generate processed data, some examples of which are described below. 
     In some examples, sensor  336  may be a sensor, that generates a signal indicative of the control outputs from feed rate controller  236 . The control signals may be speed control signals or other control signals that are applied to controllable subsystems  216  to control feed rate of material through agricultural harvester  100 . Processing system  338  processes the signals obtained via the sensor  336  to generate processed data  340  identifying the speed of agricultural harvester  100 . Processed data  340  may include a location of agricultural harvester  100  corresponding to the speed of agricultural harvester  100 . 
     In some examples, raw or processed data from in-situ sensor(s)  208  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. 
     The present discussion proceeds with respect to an example in which in-situ sensor  208  is machine speed sensor  146 . It will be appreciated that this is just one example, and the sensors mentioned above, as other examples of in-situ sensor  208  from which machine speed can be derived are contemplated herein as well. As shown in  FIG. 4 , the example predictive model generator  210  includes one or more of a vegetative index (VI) value-to-speed model generator  342 , a biomass-to-speed model generator  344 , topography-to-speed model generator  345 , yield-to-speed model generator  347 , crop state-to-speed model generator  349 , soil property-to-speed model generator  351  and a seeding characteristic-to-speed model generator  346 . In other examples, the predictive model generator  210  may include additional, fewer, or different components than those shown in the example of  FIG. 4 . Consequently, in some examples, the predictive model generator  210  may include other items  348  as well, which may include other types of predictive model generators to generate other types of models. 
     Model generator  342  identifies a relationship between machine speed detected in processed data  340 , at a geographic location corresponding to where the processed data  340  were obtained, and vegetative index values from the vegetative index map  332  corresponding to the same location in the field where the weed characteristic was detected. Based on this relationship established by model generator  342 , model generator  342  generates a predictive speed model. The predictive speed model is used by speed map generator  352  to predict target machine speed at different locations in the field based upon the georeferenced vegetative index values contained in the vegetative index map  332  at the same locations in the field. 
     Model generator  344  identifies a relationship between machine speed represented in the processed data  340 , at a geographic location corresponding to the processed data  340 , and the biomass value at the same geographic location. Again, the biomass value is the georeferenced value contained in the biomass map  335 . Model generator  344  then generates a predictive speed model that is used by speed map generator  354  to predict the target machine speed at a location in the field based upon the biomass value for that location in the field. 
     Model generator  346  identifies a relationship between the machine speed identified by processed data  340  at a particular location in the field and the seeding characteristic value from the seeding characteristic map  343  at that same location. Model generator  346  generates a predictive speed model that is used by speed map generator  356  to predict the target machine speed at a particular location in the field based upon the seeding characteristic value at that location in the field. 
     In light of the above, the predictive model generator  210  is operable to produce a plurality of predictive speed models, such as one or more of the predictive speed models generated by model generators  342 ,  344 ,  345 ,  346 ,  347 ,  348  and  351 . In another example, two or more of the predictive speed models described above may be combined into a single predictive speed model that predicts target machine speed based on two or more of the vegetative index value the biomass value, the topography, the yield, the seeding characteristic, the crop state, or the soil property, at different locations in the field. Any of these speed models, or combinations thereof, are represented collectively by predictive model  350  in  FIG. 4 . 
     The predictive model  350  is provided to predictive map generator  212 . In the example of  FIG. 4 , predictive map generator  212  includes a speed map generator  352 . 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  358  which may include other types of map generators to generate speed maps. Speed map generator  352  receives the predictive model  350 , which predicts target machine speed based upon a value from one or more prior information maps  258 , along with the one or more prior information maps  258 , and generates a predictive map that predicts the target machine speed at different locations in the field. 
     Predictive map generator  212  outputs one or more functional predictive speed maps  360  that are predictive of one or more of target machine speed. The functional predictive speed map  360  predicts the target machine speed at different locations in a field. The functional predictive speed 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 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 , such as propulsion subsystem  250  based upon the predictive map  264 , predictive control zone map  265 , or both. 
       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 model  350  and the functional predictive speed map  360 . At block  362 , predictive model generator  210  and predictive map generator  212  receive a prior information map  258 . The prior information map  258  may be any of the maps  332 ,  333 ,  335 ,  337 ,  339 ,  341  or  343 . In addition, block  361  indicates that the received prior information map may be a single map. Block  363  indicates that the prior information map may be multiple maps or multiple map layers. Block  365  indicates that the prior information map  258  may take other forms as well. At block  364 , processing system  338  receives one or more signals from machine speed sensor  146  or sensor  336  or both. 
     At block  372 , processing system  338  processes the one or more received signals to generate processed data  340  indicative of a machine speed of agricultural harvester  100 . 
     At block  382 , predictive model generator  210  also obtains the geographic location  334  corresponding to the processed 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 processed data was taken or from which the processed data  340  was derived. 
     At block  384 , predictive model generator  210  generates one or more predictive models, such as predictive model  350 , that model a relationship between a value in the one or more prior information map  258 , and a machine speed being sensed by the in-situ sensor  208 .VI value-to-speed model generator  342  generates a predictive model that models a relationship between VI values in VI map  332  and machine speed sensed by in-situ sensor  208 . Biomass-to-speed model generator  344  generates a predictive model that models a relationship between biomass values in biomass map  335  and machine speed sensed by in-situ sensor  208 . Topography-to-speed model generator  345  generates a predictive model that models a relationship between one or more topography values such as pitch, roll, or slope in topographic map  339  and machine speed sensed by in-situ sensor  208 . Seeding characteristic-to-speed model generator  346  generates a predictive model that models a relationship between seeding characteristics in seeding map  343  and machine speed sensed by in-situ sensor  208 . Yield-to-speed model generator  347  generates a predictive model that models a relationship between yield values in yield map  333  and machine speed sensed by in-situ sensor  208 . Crop state-to-speed model generator  342  generates a predictive model that models a relationship between crop state values in crop state map  337  and machine speed sensed by in-situ sensor  208 . Soil property-to-speed model generator  351  generates a predictive model that models a relationship between soil property values in soil property map  341  and machine speed sensed by in-situ sensor  208 . At block  386 , the predictive model  350  is provided to predictive map generator  212  which generates a functional predictive speed map  360  that maps a predicted, target machine speed based on the prior information map  258  and the predictive speed model  350 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between VI values in VI map  332  and machine speed and using the VI map  332 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between yield values in yield map  333  and machine speed and using the yield map  333 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between biomass values in biomass map  335  and machine speed and using the biomass map  335 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between crop state values in crop state map  337  and machine speed and using the crop state map  337 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between topographic values in topographic map  339  and machine speed and using the topographic map  332 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between soil property values in soil property map  341  and machine speed and using the soil property map  341 . Speed map generator  352  may generate the functional predictive speed map  360  using a predictive model  350  that models a relationship between seeding characteristic values in seeding map  343  and machine speed and using the seeding map  343 . 
     Thus, as an agricultural harvester is moving through a field performing an agricultural operation, one or more functional predictive speed maps  360  are generated as the agricultural operation is being performed. 
     At block  394 , predictive map generator  212  outputs the functional predictive speed map  360 . At block  391  functional predictive speed map generator  212  outputs the functional predictive speed map  360  for presentation to and possible interaction by operator  260 . At block  393 , predictive map generator  212  may configure the map  360  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 functional predictive speed 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 functional predictive speed map  360  (with or without control zones). 
     Control system  214  can generate control signals to control header or other machine actuator(s)  248 . Control system  214  can generate control signals to control propulsion subsystem  250 . Control system  214  can generate control signals to control steering subsystem  252 . Control system  214  can generate control signals to control residue subsystem  138 . Control system  214  can generate control signals to control machine cleaning subsystem  254 . Control system  214  can generate control signals to control thresher  110 . Control system  214  can generate control signals to control material handling subsystem  125 . Control system  214  can generate control signals to control crop cleaning subsystem  118 . Control system  214  can generate control signals to control communication system  206 . Control system  214  can generate control signals to control operator interface mechanisms  218 . Control system  214  can generate control signals to control various other controllable subsystems  256 . 
       FIG. 6  is a block diagram of an example portion of the agricultural harvester  100  shown in  FIG. 1 . Particularly,  FIG. 6  shows, among other things, examples of predictive model generator  210  and predictive map generator  212 . In the illustrated example, the information map  259  is one or more of a functional predictive speed map  360 , a speed map with control zones  400 , or another speed map  401 . 
     Also, in the example shown in  FIG. 6 , in-situ sensor  208  can include one or more of a variety of different sensors  402  and processing system  406 . Some examples of in-situ sensors  208  and sensors  402  are described below with respect to  FIG. 8 . 
     Predictive model generator  210  may include speed characteristic-to-in-situ sensor data model generator  416 . In other examples, predictive model generator  210  can include additional or other model generators  424 . Predictive model generator  210  receives a geographic location indicator  324  from geographic position sensor  204  and generates a predictive model  426  that models a relationship between the information in one or more of the information maps  259  and the characteristic data provide by one or more of the in-situ sensors  402 . For instance, speed characteristic-to-characteristic model generator  416  generates a relationship between speed characteristic values (which may be on map  360 , on map  400 , or on map  401 ) and the values of a characteristic sensed by sensor  402 . Speed characteristic-to-in-situ data model generator  416  illustratively generates a model that represents a relationship between the travel speed or variable indicative of travel speed in information map  259  and the characteristic or the value of the characteristic sensed by in-situ sensor  402 . Predictive characteristic model  426  generated by the predictive model generator  210  can include the predictive model that may be generated by speed characteristic-to-characteristic model generator  416 . 
     In the example of  FIG. 6 , predictive map generator  212  includes predictive characteristic map generator  428 . In other examples, predictive map generator  212  can include additional or other map generators  434 . Predictive characteristic map generator  428  receives a predictive characteristic model  426  that models the relationship between machine speed on the information maps  259  and the characteristic sensed by sensor  402 . Predictive characteristic map generator  428  generates a functional predictive characteristic map  436  that predicts a characteristic, or a value of the characteristic, at different locations in the field based upon the machine speed in one or more of the information maps  259  at those locations in the field and based on predictive characteristic model  426 . 
     Predictive map generator  212  outputs the functional predictive characteristic map  436 . The functional predictive characteristic map  436  may be provided to control zone generator  213 , control system  214 , or both. Control zone generator  213  generates control zones to provide a functional predictive characteristic map  436  with control zones. 3 . The functional predictive map characteristic  436  (with or without control zones) may be provided to control system  214 , which generates control signals to control one or more of the controllable subsystems  216  based upon the functional predictive characteristic map  436  (with or without control zones). The functional predictive characteristic map  436  (with or without control zones) may be presented to operator  260  or another user. 
     Based upon functional predictive characteristic map  436  (with or without control zones) control system  214  can generate control signals to control one or more of the controllable subsystems  216 . For example, control system  214  can control header or other machine actuator(s)  248 . Control system  214  can generate control signals to control propulsion subsystem  250 . Control system  214  can generate control signals to control steering subsystem  252 . Control system  214  can generate control signals to control residue subsystem  138 . Control system  214  can generate control signals to control machine cleaning subsystem  254 . Control system  214  can generate control signals to control thresher  110 . Control system  214  can generate control signals to control material handling subsystem  125 . Control system  214  can generate control signals to control crop cleaning subsystem  118 . Control system  214  can generate control signals to control communication system  206 . Control system  214  can generate control signals to control operator interface mechanisms  218 . Control system  214  can generate control signals to control various other controllable subsystems  256 . 
       FIG. 7  shows a flow diagram illustrating one example of the operation of predictive model generator  210  and predictive map generator  212  in generating predictive characteristic model  426  and functional predictive characteristic map  436 . At block  442 , predictive model generator  210  and predictive map generator  212  receive a map, such as an information map  259 . The information map  259  may be functional predictive speed map  360 , speed map with control zones  400 , or another speed map  406 . At block  444 , in-situ sensor  402  generates a sensor signal containing sensor data indicative of the characteristic sensed by in-situ sensor  402 . The in-situ sensor  402  can be, for example, one or more of the sensors described below with respect to  FIG. 8 . 
     At block  454 , processing system  406  processes the data contained in the sensor signal received from the in-situ sensor  402  to obtain processed data  409 , shown in  FIG. 6 . The data contained in the sensor signal can be in a raw format that is processed to receive processed data  409 . For example, a temperature sensor signal includes electrical resistance data. This electrical resistance data can be processed into temperature data. In other examples, processing may comprise digitizing, encoding, formatting, scaling, filtering, or classifying data. 
     Returning to  FIG. 7 , at block  456 , predictive model generator  210  also receives a geographic location  334  from geographic position sensor  204 , as shown in  FIG. 6 . The geographic location  334  may be correlated to the geographic location from which the sensed variable or variables, sensed by in-situ sensors  402 , were taken. For instance, the predictive model generator  210  can obtain the geographic location  334  from geographic position sensor  204  and determine, based upon machine delays, machine speed, etc., a precise geographic location from which the processed data  409  was derived. 
     At block  458 , predictive model generator  210  generates a predictive model  426  that models a relationship between a mapped speed value in the received map, such as an information map  259 , and a characteristic represented in the processed data  409  or a related characteristic, such as a characteristic that correlates to the characteristic sensed by in-situ sensor  402 . 
     The predictive characteristic model  426  is provided to predictive map generator  212 . At block  466 , predictive map generator  212  generates a functional predictive characteristic map, such as functional predictive characteristic map  436 . The functional predictive characteristic map  436  predicts characteristic values at different locations in the field. Thus, as agricultural harvester  100  is moving through a field performing an agricultural operation, the functional predictive characteristic map  436  is generated as the agricultural operation is being performed. The functional predictive characteristic map  436  may be updated continuously, periodically, conditionally, manually, or upon the occurrence of some other event. 
     At block  468 , predictive map generator  212  outputs functional predictive characteristic map  436 . At block  470 , predictive map generator  212  configures the functional predictive characteristic map  436  for presentation to and possible interaction by an operator  260  or another user. At block  472 , predictive map generator  212  configures the functional predictive characteristic map  436  for consumption by control system  214 . At block  474 , predictive map generator  212  provides the functional predictive characteristic map  436  to control zone generator  213  for generation and incorporation of control zones. At block  476 , predictive map generator  212  configures the functional predictive characteristic map  436  in other ways. The functional predictive characteristic map  436  (with or without the control zones included therewith), may be presented to operator  260  or another user or provided to control system  214  as well. 
     At block  478 , control system  214  then generates control signals to control one or more of the controllable subsystems  216  based upon the functional predictive characteristic map  436  (with or without control zones) as well as an input from the geographic position sensor  204 . For example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control header or other machine actuator(s)  248 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control propulsion subsystem  250 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control steering subsystem  252 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control residue subsystem  138 . In another example, based upon functional predictive map  436  (with or without control zones), control system  214  can generate control signals to control machine cleaning subsystem  254 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control thresher  110 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control material handling subsystem  125 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control crop cleaning subsystem  118 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control communication system  206 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control operator interface mechanisms  218 . In another example, based upon functional predictive characteristic map  436  (with or without control zones), control system  214  can generate control signals to control various other controllable subsystems  256 . 
     For example, when the functional predictive characteristic map  436  or functional predictive characteristic map  436  containing control zones is provided to control system  214 , control system  214 , in response, generates control signals to control one or more of the various controllable subsystems  216 . For example, control system  214  may generate control signals to control the header or other machine actuators  248 , which may also include actuators for other front-end equipment, for example to control a backshaft speed of the header, to control a position (height, tilt, or roll) of the header, to control a reel speed, or to control a draper belt speed. 
     In another example in which control system  214  receives the functional predictive characteristic map  436  or functional predictive characteristic map  436  with control zones added, the settings controller  232  controls propulsion subsystem  250  (shown as one of the controllable subsystems  216  in  FIG. 2 ). 
     In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the path planning controller  234  controls steering subsystem  252  to steer agricultural harvester  100 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the residue system controller  244  controls residue subsystem  138 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the settings controller  232  controls thresher settings of thresher  110 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the settings controller  232  or another controller  246  controls material handling subsystem  125 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the settings controller  232  controls crop cleaning subsystem. In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the machine cleaning controller  245  controls machine cleaning subsystem  254  on agricultural harvester  100 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the communication system controller  229  controls communication system  206 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the operator interface controller  231  controls operator interface mechanisms  218  on agricultural harvester  100 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the deck plate position controller  242  controls machine/header actuators  248  to control a deck plate on agricultural harvester  100 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the draper belt controller  240  controls machine/header actuators  248  to control a draper belt on agricultural harvester  100 . In another example in which control system  214  receives the functional predictive characteristic map  436  or the functional predictive characteristic map  436  with control zones added, the other controllers  246  control other controllable subsystems  256  on agricultural harvester  100 . 
       FIG. 8  is a block diagram showing some examples of in-situ sensors  208 . Some of the sensors shown in  FIG. 8 , or different combinations of them, may have both a sensor  402  and a processing system  406 , while others may act as sensor  402  described above with respect to  FIGS. 6 and 7  where the processing system  406  is separate. Some of the possible in-situ sensors  208  shown in  FIG. 8  are shown and described above with respect to previous FIGS. and are similarly numbered.  FIG. 8  shows that in-situ sensors  208  can include non-machine sensors  479 , operator input sensors  480 , machine sensors  482 , harvested material property sensors  484 , field and soil property sensors  485 , and environmental characteristic sensors  487 . As shown in  FIG. 8 , in-situ sensors  208  may also include a wide variety of other sensors  226 . Operator input sensors  480  are sensors that sense operator inputs, such as sensing operator inputs through operator interface mechanisms  218 . Therefore, operator input sensors  480  can sense user movement of linkages, joysticks, a steering wheel, buttons, dials, or pedals. Operator input sensors  480  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  482  can sense different characteristics of agricultural harvester  100 . For instance, as discussed above, machine sensors  482  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  482  also include machine setting sensors  491  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  493  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  493  may sense the height of header  102  above the ground. Machine sensors  482  also include front-end equipment (e.g., header) orientation sensors  495 . Sensors  495  may sense the orientation of header  102  relative to agricultural harvester  100  or relative to the ground. Machine sensors  482  may include stability sensors  497 . Stability sensors  497  sense oscillation or bouncing motion (which may be measured by frequency or amplitude, or both) of agricultural harvester  100 . Machine sensors  482  also include residue setting sensors  499  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  482  may include cleaning shoe fan speed sensor  551  that senses the speed of cleaning fan  120 . Machine sensors  482  include concave clearance sensors  553  that sense the clearance between the rotor  112  and concaves  114  on agricultural harvester  100 . Machine sensors  482  include chaffer clearance sensors  555  that sense the size of openings in chaffer  122 . The machine sensors  482  include threshing rotor speed sensor  557  that senses a rotor speed of rotor  112 . Machine sensors  482  include rotor pressure sensor  559  that senses the pressure used to drive rotor  112 . Machine sensors  482  include sieve clearance sensor  561  that senses the size of openings in sieve  124 . The machine sensors  482  include MOG moisture sensor  563  that senses a moisture level of the MOG passing through agricultural harvester  100 . Machine sensors  482  include machine orientation sensor  565  that senses the orientation of agricultural harvester  100 . Machine sensors  482  include material feed rate sensors  567  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  482  include biomass sensors  569  that sense the biomass traveling through feeder house  106 , through separator  116 , or elsewhere in agricultural harvester  100 . The machine sensors  482  include fuel consumption sensor  571  that senses a rate of fuel consumption over time of agricultural harvester  100 . Machine sensors  482  include power utilization sensor  573  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  482  include tire pressure sensors  577  that sense the inflation pressure in tires  144  of agricultural harvester  100 . Machine sensor  482  may also include a wide variety of other machine performance sensors, or machine characteristic sensors, indicated by block  575 . The machine performance sensors and machine characteristic sensors  575  may sense machine performance or characteristics of agricultural harvester  100 . 
     Harvested material property sensors  484  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. 
     Field and soil property sensors  485  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  487  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. 
       FIG. 9  shows a block diagram illustrating one example of control zone generator  213 . Control zone generator  213  includes work machine actuator (WMA) selector  486 , control zone generation system  488 , and regime zone generation system  490 . Control zone generator  213  may also include other items  492 . Control zone generation system  488  includes control zone criteria identifier component  494 , control zone boundary definition component  496 , target setting identifier component  498 , and other items  520 . Regime zone generation system  490  includes regime zone criteria identification component  522 , regime zone boundary definition component  524 , settings resolver identifier component  526 , and other items  528 . Before describing the overall operation of control zone generator  213  in more detail, a brief description of some of the items in control zone generator  213  and the respective operations thereof will first be provided. 
     Agricultural harvester  100 , or other work machines, may have a wide variety of different types of controllable actuators that perform different functions. The controllable actuators on agricultural harvester  100  or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA may be independently controllable based upon values on a functional predictive map, or the WMAs may be controlled as sets based upon one or more values on a functional predictive map. Therefore, control zone generator  213  may generate control zones corresponding to each individually controllable WMA or corresponding to the sets of WMAs that are controlled in coordination with one another. 
     WMA selector  486  selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Control zone generation system  488  then generates the control zones for the selected WMA or set of WMAs. For each WMA or set of WMAs, different criteria may be used in identifying control zones. For example, for one WMA, the WMA response time may be used as the criteria for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how much a particular actuator or mechanism wears as a result of movement thereof) may be used as the criteria for identifying the boundaries of control zones. Control zone criteria identifier component  494  identifies particular criteria that are to be used in defining control zones for the selected WMA or set of WMAs. Control zone boundary definition component  496  processes the values on a functional predictive map under analysis to define the boundaries of the control zones on that functional predictive map based upon the values in the functional predictive map under analysis and based upon the control zone criteria for the selected WMA or set of WMAs. 
     Target setting identifier component  498  sets a value of the target setting that will be used to control the WMA or set of WMAs in different control zones. For instance, if the selected WMA is propulsion system  250  and the functional predictive map under analysis is a functional predictive speed map  438 , then the target setting in each control zone may be a target speed setting based on speed values contained in the functional predictive speed map  238  within the identified control zone. 
     In some examples, where agricultural harvester  100  is to be controlled based on a current or future location of the agricultural harvester  100 , multiple target settings may be possible for a WMA at a given location. In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator in propulsion system  250  that is being controlled in order to control the speed of agricultural harvester  100 , multiple different competing sets of criteria may exist that are considered by control zone generation system  488  in identifying the control zones and the target settings for the selected WMA in the control zones. For instance, different target settings for controlling machine speed may be generated based upon, for example, a detected or predicted feed rate value, a detected or predictive fuel efficiency value, a detected or predicted grain loss value, or a combination of these. However, at any given time, the agricultural harvester  100  cannot travel over the ground at multiple speeds simultaneously. Rather, at any given time, the agricultural harvester  100  travels at a single speed. Thus, one of the competing target settings is selected to control the speed of agricultural harvester  100 . 
     Therefore, in some examples, regime zone generation system  490  generates regime zones to resolve multiple different competing target settings. Regime zone criteria identification component  522  identifies the criteria that are used to establish regime zones for the selected WMA or set of WMAs on the functional predictive map under analysis. Some criteria that can be used to identify or define regime zones include, for example, crop type or crop variety based on an as-planted map or another source of the crop type or crop variety, weed type, weed intensity, or crop state, such as whether the crop is down, partially down or standing. Just as each WMA or set of WMAs may have a corresponding control zone, different WMAs or sets of WMAs may have a corresponding regime zone. Regime zone boundary definition component  524  identifies the boundaries of regime zones on the functional predictive map under analysis based on the regime zone criteria identified by regime zone criteria identification component  522 . 
     In some examples, regime zones may overlap with one another. For instance, a crop variety regime zone may overlap with a portion of or an entirety of a crop state regime zone. In such an example, the different regime zones may be assigned to a precedence hierarchy so that, where two or more regime zones overlap, the regime zone assigned with a greater hierarchical position or importance in the precedence hierarchy has precedence over the regime zones that have lesser hierarchical positions or importance in the precedence hierarchy. The precedence hierarchy of the regime zones may be manually set or may be automatically set using a rules-based system, a model-based system, or another system. As one example, where a downed crop regime zone overlaps with a crop variety regime zone, the downed crop regime zone may be assigned a greater importance in the precedence hierarchy than the crop variety regime zone so that the downed crop regime zone takes precedence. 
     In addition, each regime zone may have a unique settings resolver for a given WMA or set of WMAs. Settings resolver identifier component  526  identifies a particular settings resolver for each regime zone identified on the functional predictive map under analysis and a particular settings resolver for the selected WMA or set of WMAs. 
     Once the settings resolver for a particular regime zone is identified, that settings resolver may be used to resolve competing target settings, where more than one target setting is identified based upon the control zones. The different types of settings resolvers can have different forms. For instance, the settings resolvers that are identified for each regime zone may include a human settings resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the settings resolver may include a neural network or other artificial intelligence or machine learning system. In such instances, the settings resolvers may resolve the competing target settings based upon a predicted or historic quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting may reduce the time to harvest a field and reduce corresponding time-based labor and equipment costs but may increase grain losses. A reduced vehicle speed setting may increase the time to harvest a field and increase corresponding time-based labor and equipment costs but may decrease grain losses. When grain loss or time to harvest is selected as a quality metric, the predicted or historic value for the selected quality metric, given the two competing vehicle speed settings values, may be used to resolve the speed setting. In some instances, the settings resolvers may be a set of threshold rules that may be used instead of, or in addition to, the regime zones. An example of a threshold rule may be expressed as follows:
         If predicted biomass values within 20 feet of the header of the agricultural harvester  100  are greater that x kilograms (where x is a selected or predetermined value), then use the target setting value that is chosen based on feed rate over other competing target settings, otherwise use the target setting value based on grain loss over other competing target setting values.       

     The settings resolvers may be logical components that execute logical rules in identifying a target setting. For instance, the settings resolver may resolve target settings while attempting to minimize harvest time or minimize the total harvest cost or maximize harvested grain or based on other variables that are computed as a function of the different candidate target settings. A harvest time may be minimized when an amount to complete a harvest is reduced to at or below a selected threshold. A total harvest cost may be minimized where the total harvest cost is reduced to at or below a selected threshold. Harvested grain may be maximized where the amount of harvested grain is increased to at or above a selected threshold. 
       FIG. 10  is a flow diagram illustrating one example of the operation of control zone generator  213  in generating control zones and regime zones for a map that the control zone generator  213  receives for zone processing (e.g., for a map under analysis). 
     At block  530 , control zone generator  213  receives a map under analysis for processing. In one example, as shown at block  532 , the map under analysis is a functional predictive map. For example, the map under analysis may be one of the functional predictive maps  360  or  436 . Block  534  indicates that the map under analysis can be other maps as well. 
     At block  536 , WMA selector  486  selects a WMA or a set of WMAs for which control zones are to be generated on the map under analysis. At block  538 , control zone criteria identification component  494  obtains control zone definition criteria for the selected WMAs or set of WMAs. Block  540  indicates an example in which the control zone criteria are or include wear characteristics of the selected WMA or set of WMAs. Block  542  indicates an example in which the control zone definition criteria are or include a magnitude and variation of input source data, such as the magnitude and variation of the values on the map under analysis or the magnitude and variation of inputs from various in-situ sensors  208 . Block  544  indicates an example in which the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which different subsystems operate, or other physical machine characteristics. Block  546  indicates an example in which the control zone definition criteria are or include a responsiveness of the selected WMA or set of WMAs in reaching newly commanded setting values. Block  548  indicates an example in which the control zone definition criteria are or include machine performance metrics. Block  550  indicates an example in which the control zone definition criteria are or includes operator preferences. Block  552  indicates an example in which the control zone definition criteria are or include other items as well. Block  549  indicates an example in which the control zone definition criteria are time based, meaning that agricultural harvester  100  will not cross the boundary of a control zone until a selected amount of time has elapsed since agricultural harvester  100  entered a particular control zone. In some instances, the selected amount of time may be a minimum amount of time. Thus, in some instances, the control zone definition criteria may prevent the agricultural harvester  100  from crossing a boundary of a control zone until at least the selected amount of time has elapsed. Block  551  indicates an example in which the control zone definition criteria are based on a selected size value. For example, a control zone definition criteria that is based on a selected size value may preclude definition of a control zone that is smaller than the selected size. In some instances, the selected size may be a minimum size. 
     At block  554 , regime zone criteria identification component  522  obtains regime zone definition criteria for the selected WMA or set of WMAs. Block  556  indicates an example in which the regime zone definition criteria are based on a manual input from operator  260  or another user. Block  558  illustrates an example in which the regime zone definition criteria are based on crop type or crop variety. Block  560  illustrates an example in which the regime zone definition criteria are based on weed type or weed intensity or both. Block  562  illustrates an example in which the regime zone definition criteria are based on or include crop state. Block  564  indicates an example in which the regime zone definition criteria are or include other criteria as well, such as a characteristic, or value thereof, detected by an in-situ sensor or a speed characteristic value, such as predicted speed characteristic value. 
     At block  566 , control zone boundary definition component  496  generates the boundaries of control zones on the map under analysis based upon the control zone criteria. Regime zone boundary definition component  524  generates the boundaries of regime zones on the map under analysis based upon the regime zone criteria. Block  568  indicates an example in which the zone boundaries are identified for the control zones and the regime zones. Block  570  shows that target setting identifier component  498  identifies the target settings for each of the control zones. The control zones and regime zones can be generated in other ways as well, and this is indicated by block  572 . 
     At block  574 , settings resolver identifier component  526  identifies the settings resolver for the selected WMAs in each regime zone defined by regimes zone boundary definition component  524 . As discussed above, the regime zone resolver can be a human resolver  576 , an artificial intelligence or machine learning system resolver  578 , a resolver  580  based on predicted or historic quality for each competing target setting, a rules-based resolver  582 , a performance criteria-based resolver  584 , or other resolvers  586 . 
     At block  588 , WMA selector  486  determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs are remaining to be processed, processing reverts to block  436  where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. When no additional WMAs or sets of WMAs for which control zones or regime zones are to be generated are remaining, processing moves to block  590  where control zone generator  213  outputs a map with control zones, target settings, regime zones, and settings resolvers for each of the WMAs or sets of WMAs. As discussed above, the outputted map can be presented to operator  260  or another user; the outputted map can be provided to control system  214 ; or the outputted map can be output in other ways. At block  592 , the control system  214  generates control signals to control one or more of the controllable subsystems based on the map with control zones, target settings, regime zones, and setting resolvers for each of the WMAs or sets of WMAs. 
       FIG. 11  illustrates one example of the operation of control system  214  in controlling agricultural harvester  100  based upon a map that is output by control zone generator  213 . Thus, at block  592 , control system  214  receives a map of the worksite. In some instances, the map can be a functional predictive map that may include control zones and regime zones, as represented by block  594 . In some instances, the received map may be a functional predictive map that excludes control zones and regime zones. Block  596  indicates an example in which the received map of the worksite can be a prior information map having control zones and regime zones identified on it. Block  598  indicates an example in which the received map can include multiple different maps or multiple different map layers. Block  610  indicates an example in which the received map can take other forms as well. 
     At block  612 , control system  214  receives a sensor signal from geographic position sensor  204 . The sensor signal from geographic position sensor  204  can include data that indicates the geographic location  614  of agricultural harvester  100 , the speed  616  of agricultural harvester  100 , the heading  618  or agricultural harvester  100 , or other information  620 . At block  622 , zone controller  247  selects a regime zone, and, at block  624 , zone controller  247  selects a control zone on the map based on the geographic position sensor signal. At block  626 , zone controller  247  selects a WMA or a set of WMAs to be controlled. At block  628 , zone controller  247  obtains one or more target settings for the selected WMA or set of WMAs. The target settings that are obtained for the selected WMA or set of WMAs may come from a variety of different sources. For instance, block  630  shows an example in which one or more of the target settings for the selected WMA or set of WMAs is based on an input from the control zones on the map of the worksite. Block  632  shows an example in which one or more of the target settings is obtained from human inputs from operator  260  or another user. Block  634  shows an example in which the target settings are obtained from an in-situ sensor  208 . Block  636  shows an example in which the one or more target settings is obtained from one or more sensors on other machines working in the same field either concurrently with agricultural harvester  100  or from one or more sensors on machines that worked in the same field in the past. Block  638  shows an example in which the target settings are obtained from other sources as well. 
     In some examples, as discussed above, there may be multiple different target settings for the same WMA or set of WMAs that are in conflict with one another, for example, when the agricultural harvester  100  is at a location on the field having overlapping control zones or overlapping regime zones, or both. In an example in which the selected WMA is an actuator in propulsion subsystem  250  that controls machine speed, there may be two or more different target speed settings. Those different target speed settings are then resolved to identify a single target speed setting that will be used to control the speed of agricultural harvester  100 . 
     At block  640 , zone controller  247  accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target speed settings into a resolved target speed setting. As discussed above, in some instances, the settings resolver may be a human resolver in which case zone controller  247  controls operator interface mechanisms  218  to present the competing target settings to operator  260  or another user for resolution. In some instances, the settings resolver may be a neural network or other artificial intelligence or machine learning system, and zone controller  247  submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some instances, the settings resolver may be based on a predicted or historic quality metric, on threshold rules, or on logical components. In any of these latter examples, zone controller  247  executes the settings resolver to obtain a resolved target setting based on the predicted or historic quality metric, based on the threshold rules, or with the use of the logical components. 
     At block  642 , with zone controller  247  having identified the resolved target setting, zone controller  247  provides the resolved target setting to other controllers in control system  214 , which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting. In some examples, when the selected WMA is an actuator in propulsion subsystem  250  that controls the speed of agricultural harvester  100 , zone controller  247  provides the resolved target speed setting to setting controller  232  to generate control signals based upon the resolved target speed setting and those generated control signals are applied to the selected actuator in propulsion subsystem  250  to control the speed of agricultural harvester  100 . In other examples, the selected WMA may be one or more actuators in any of one or more of the controllable subsystems of agricultural harvester  100 , such as controllable subsystems  216 . At block  644 , if additional WMAs or additional sets of WMAs are to be controlled at the current geographic location of the agricultural harvester  100  (as detected at block  612 ), then processing reverts to block  626  where the next WMA or set of WMAs is selected. The processes represented by blocks  626  through  644  continue until all of the WMAs or sets of WMAs to be controlled at the current geographical location of the agricultural harvester  100  have been addressed. If no additional WMAs or sets of WMAs are to be controlled at the current geographic location of the agricultural harvester  100  remain, processing proceeds to block  646  where zone controller  247  determines whether additional control zones to be considered exist in the selected regime zone. If additional control zones to be considered exist, processing reverts to block  624  where a next control zone is selected. If no additional control zones are remaining to be considered, processing proceeds to block  648  where a determination as to whether additional regime zones are remaining to be consider. Zone controller  247  determines whether additional regime zones are remaining to be considered. If additional regimes zone are remaining to be considered, processing reverts to block  622  where a next regime zone is selected. 
     At block  650 , zone controller  247  determines whether the operation that agricultural harvester  100  is performing is complete. If not, the zone controller  247  determines whether a control zone criterion has been satisfied to continue processing, as indicated by block  652 . For instance, as mentioned above, control zone definition criteria may include criteria defining when a control zone boundary may be crossed by the agricultural harvester  100 . For example, whether a control zone boundary may be crossed by the agricultural harvester  100  may be defined by a selected time period, meaning that agricultural harvester  100  is prevented from crossing a zone boundary until a selected amount of time has transpired. In that case, at block  652 , zone controller  247  determines whether the selected time period has elapsed. Additionally, zone controller  247  can perform processing continually. Thus, zone controller  247  does not wait for any particular time period before continuing to determine whether an operation of the agricultural harvester  100  is completed. At block  652 , zone controller  247  determines that it is time to continue processing, then processing continues at block  612  where zone controller  247  again receives an input from geographic position sensor  204 . It will also be appreciated that zone controller  247  can control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller instead of controlling the WMAs and sets of WMAs sequentially. 
       FIG. 12  is a block diagram showing one example of an operator interface controller  231 . In an illustrated example, operator interface controller  231  includes operator input command processing system  654 , other controller interaction system  656 , speech processing system  658 , and action signal generator  660 . Operator input command processing system  654  includes speech handling system  662 , touch gesture handling system  664 , and other items  666 . Other controller interaction system  656  includes controller input processing system  668  and controller output generator  670 . Speech processing system  658  includes trigger detector  672 , recognition component  674 , synthesis component  676 , natural language understanding system  678 , dialog management system  680 , and other items  682 . Action signal generator  660  includes visual control signal generator  684 , audio control signal generator  686 , haptic control signal generator  688 , and other items  690 . Before describing operation of the example operator interface controller  231  shown in  FIG. 12  in handling various operator interface actions, a brief description of some of the items in operator interface controller  231  and the associated operation thereof is first provided. 
     Operator input command processing system  654  detects operator inputs on operator interface mechanisms  218  and processes those inputs for commands. Speech handling system  662  detects speech inputs and handles the interactions with speech processing system  658  to process the speech inputs for commands. Touch gesture handling system  664  detects touch gestures on touch sensitive elements in operator interface mechanisms  218  and processes those inputs for commands. 
     Other controller interaction system  656  handles interactions with other controllers in control system  214 . Controller input processing system  668  detects and processes inputs from other controllers in control system  214 , and controller output generator  670  generates outputs and provides those outputs to other controllers in control system  214 . Speech processing system  658  recognizes speech inputs, determines the meaning of those inputs, and provides an output indicative of the meaning of the spoken inputs. For instance, speech processing system  658  may recognize a speech input from operator  260  as a settings change command in which operator  260  is commanding control system  214  to change a setting for a controllable subsystem  216 . In such an example, speech processing system  658  recognizes the content of the spoken command, identifies the meaning of that command as a settings change command, and provides the meaning of that input back to speech handling system  662 . Speech handling system  662 , in turn, interacts with controller output generator  670  to provide the commanded output to the appropriate controller in control system  214  to accomplish the spoken settings change command. 
     Speech processing system  658  may be invoked in a variety of different ways. For instance, in one example, speech handling system  662  continuously provides an input from a microphone (being one of the operator interface mechanisms  218 ) to speech processing system  658 . The microphone detects speech from operator  260 , and the speech handling system  662  provides the detected speech to speech processing system  658 . Trigger detector  672  detects a trigger indicating that speech processing system  658  is invoked. In some instances, when speech processing system  658  is receiving continuous speech inputs from speech handling system  662 , speech recognition component  674  performs continuous speech recognition on all speech spoken by operator  260 . In some instances, speech processing system  658  is configured for invocation using a wakeup word. That is, in some instances, operation of speech processing system  658  may be initiated based on recognition of a selected spoken word, referred to as the wakeup word. In such an example, where recognition component  674  recognizes the wakeup word, the recognition component  674  provides an indication that the wakeup word has been recognized to trigger detector  672 . Trigger detector  672  detects that speech processing system  658  has been invoked or triggered by the wakeup word. In another example, speech processing system  658  may be invoked by an operator  260  actuating an actuator on a user interface mechanism, such as by touching an actuator on a touch sensitive display screen, by pressing a button, or by providing another triggering input. In such an example, trigger detector  672  can detect that speech processing system  658  has been invoked when a triggering input via a user interface mechanism is detected. Trigger detector  672  can detect that speech processing system  658  has been invoked in other ways as well. 
     Once speech processing system  658  is invoked, the speech input from operator  260  is provided to speech recognition component  674 . Speech recognition component  674  recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. Natural language understanding system  678  identifies a meaning of the recognized speech. The meaning may be a natural language output, a command output identifying a command reflected in the recognized speech, a value output identifying a value in the recognized speech, or any of a wide variety of other outputs that reflect the understanding of the recognized speech. For example, the natural language understanding system  678  and speech processing system  568 , more generally, may understand of the meaning of the recognized speech in the context of agricultural harvester  100 . 
     In some examples, speech processing system  658  can also generate outputs that navigate operator  260  through a user experience based on the speech input. For instance, dialog management system  680  may generate and manage a dialog with the user in order to identify what the user wishes to do. The dialog may disambiguate a user&#39;s command; identify one or more specific values that are needed to carry out the user&#39;s command; or obtain other information from the user or provide other information to the user or both. Synthesis component  676  may generate speech synthesis which can be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialog managed by dialog management system  680  may be exclusively a spoken dialog or a combination of both a visual dialog and a spoken dialog. 
     Action signal generator  660  generates action signals to control operator interface mechanisms  218  based upon outputs from one or more of operator input command processing system  654 , other controller interaction system  656 , and speech processing system  658 . Visual control signal generator  684  generates control signals to control visual items in operator interface mechanisms  218 . The visual items may be lights, a display screen, warning indicators, or other visual items. Audio control signal generator  686  generates outputs that control audio elements of operator interface mechanisms  218 . The audio elements include a speaker, audible alert mechanisms, horns, or other audible elements. Haptic control signal generator  688  generates control signals that are output to control haptic elements of operator interface mechanisms  218 . The haptic elements include vibration elements that may be used to vibrate, for example, the operator&#39;s seat, the steering wheel, pedals, or joysticks used by the operator. The haptic elements may include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through operator interface mechanisms. The haptic elements may include a wide variety of other haptic elements as well. 
       FIG. 13  is a flow diagram illustrating one example of the operation of operator interface controller  231  in generating an operator interface display on an operator interface mechanism  218 , which can include a touch sensitive display screen.  FIG. 13  also illustrates one example of how operator interface controller  231  can detect and process operator interactions with the touch sensitive display screen. 
     At block  692 , operator interface controller  231  receives a map. Block  694  indicates an example in which the map is a functional predictive map, and block  696  indicates an example in which the map is another type of map. At block  698 , operator interface controller  231  receives an input from geographic position sensor  204  identifying the geographic location of the agricultural harvester  100 . As indicated in block  700 , the input from geographic position sensor  204  can include the heading, along with the location, of agricultural harvester  100 . Block  702  indicates an example in which the input from geographic position sensor  204  includes the speed of agricultural harvester  100 , and block  704  indicates an example in which the input from geographic position sensor  204  includes other items. 
     At block  706 , visual control signal generator  684  in operator interface controller  231  controls the touch sensitive display screen in operator interface mechanisms  218  to generate a display showing all or a portion of a field represented by the received map. Block  708  indicates that the displayed field can include a current position marker showing a current position of the agricultural harvester  100  relative to the field. Block  710  indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which agricultural harvester  100  will be operating. Block  712  indicates an example in which the displayed field includes an upcoming area display portion that displays areas that are yet to be processed (such as unharvested areas) by agricultural harvester  100 , and block  714  indicates an example in which the displayed field includes previously visited display portions that represent areas of the field that agricultural harvester  100  has already processed (such as harvested areas). Block  716  indicates an example in which the displayed field displays various characteristics of the field having georeferenced locations on the map. For instance, if the received map is a weed map, the displayed field may show the different weed types existing in the field georeferenced within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block  714 ), in the upcoming areas (as shown in block  712 ), and in the next work unit (as shown in block  710 ). Block  718  indicates an example in which the displayed field includes other items as well, for example, the displayed field may include control zones or regime zones, or both. 
     At block  760 , operator interface controller  231  detects an input that marks an area of the field (such as setting a flag corresponding to an area of the field or the crossing of a map boundary) and controls the touch sensitive user interface display to display the flag on a field display portion. The detected input may be an operator input, as indicated at  762 , or an input from another controller, as indicated at  764 . At block  766 , operator interface controller  231  detects an in-situ sensor input indicative of a measured characteristic of the field from one of the in-situ sensors  208 . At block  768 , visual control signal generator  684  generates control signals to control the user interface display to display actuators for modifying the user interface display and for modifying machine control. For instance, block  770  represents that one or more of the actuators for setting or modifying the values, displayed on the user interface display, can be displayed. Thus, the user can set flags and modify characteristics of those flags. For example, a user can modify values, such as speeds or speed ranges, or both, corresponding to the flags. Block  772  represents that action threshold values can also be displayed. Block  776  represents that actions to be taken, such as increasing a speed of the machine, can be displayed, and block  778  represents that measured in-situ data can also be displayed. Block  780  indicates that a wide variety of other information and actuators can be displayed on a user interface display as well. 
     At block  782 , operator input command processing system  654  detects and processes operator inputs corresponding to interactions with the user interface display performed by the operator  260 . Where the user interface mechanism on which the user interface display is displayed is a touch sensitive display screen, interaction inputs with the touch sensitive display screen by the operator  260  can be touch gestures  784 . In some instances, the operator interaction inputs can be inputs using a point and click device  786  or other operator interaction inputs  788 . 
     At block  790 , operator interface controller  231  receives signals indicative of an alert condition. For instance, block  792  indicates that signals may be received by controller input processing system  668  indicating that detected values satisfy threshold conditions. As explained earlier, the threshold conditions may include values being below a threshold, at a threshold, or above a threshold. Block  794  shows that action signal generator  660  can, in response to receiving an alert condition, alert the operator  260  by using visual control signal generator  684  to generate visual alerts, by using audio control signal generator  686  to generate audio alerts, by using haptic control signal generator  688  to generate haptic alerts, or by using any combination of these. Similarly, as indicated by block  796 , controller output generator  670  can generate outputs to other controllers in control system  214  so that those controllers perform the corresponding action that is displayed on the user interface display. Block  798  shows that operator interface controller  231  can detect and process alert conditions in other ways as well. 
     Block  900  shows that speech handling system  662  may detect and process inputs invoking speech processing system  658 . Block  902  shows that performing speech processing may include the use of dialog management system  680  to conduct a dialog with the operator  260 . Block  904  shows that the speech processing may include providing signals to controller output generator  670  so that control operations are automatically performed based upon the speech inputs. 
     Table 1, below, shows an example of a dialog between operator interface controller  231  and operator  260 . In Table 1, operator  260  uses a trigger word or a wakeup word that is detected by trigger detector  672  to invoke speech processing system  658 . In the example shown in Table 1, the wakeup word is “Johnny”. 
     
       
         
           
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
             
            
               
                   
                  Operator: “Johnny, tell me about the current speed” 
               
               
                   
                  Operator Interface Controller: “Current speed is at 3.0 with a target 
               
               
                   
                 speed of 3.1 mph.” 
               
               
                   
                  Operator: “Johnny, what should I do to maximize feedrate?” 
               
               
                   
                  Operator Interface Controller: “Increase speed to 5.1 mph.” 
               
               
                   
                   
               
            
           
         
       
     
     Table 2 shows an example in which speech synthesis component  676  provides an output to audio control signal generator  686  to provide audible updates on an intermittent or periodic basis. The interval between updates may be time-based, such as every five minutes, or coverage or distance-based, such as every five acres, or exception-based, such as when a measured value is greater than a threshold value. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
             
            
               
                  Operator Interface Controller: “Over last 10 minutes, the travel speed 
               
               
                 has been 3.0 mph.” 
               
               
                  Operator Interface Controller: “Approaching new regime zone.” 
               
               
                  Operator Interface Controller: “Warning: Current speed is below the 
               
               
                 target speed setting.” 
               
               
                  Operator Interface Controller: “Caution: new regime zone upcoming. 
               
               
                 Select target speed setting.” 
               
               
                   
               
            
           
         
       
     
     The example shown in Table 3 illustrates that some actuators or user input mechanisms on the touch sensitive display  720  (shown in  FIG. 14 ) can be supplemented with speech dialog. The example in Table 3 illustrates that action signal generator  660  can generate action signals to automatically select a new target setting. 
     
       
         
           
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
             
            
               
                   
                  Human: “Johnny, select target speed setting corresponding to crop 
               
               
                   
                 state.” 
               
               
                   
                  Operator Interface Controller: “Target speed setting for crop state 
               
               
                   
                 selected. Target machine speed now 3.1 mph.” 
               
               
                   
                   
               
            
           
         
       
     
     The example shown in Table 4 illustrates that action signal generator  660  can conduct a dialog with operator  260  to begin and end selection of target machine settings. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
             
            
               
                  Human: “Johnny select speed setting corresponding to crop state.” 
               
               
                  Operator Interface Controller: “Speed setting corresponding to crop state 
               
               
                  selected.” 
               
               
                  Human: “Johnny, end the selection of speed setting corresponding to 
               
               
                 crop state and select speed setting corresponding to feedrate.” 
               
               
                  Operator Interface Controller: “Crop state speed setting stopped. Speed 
               
               
                 setting corresponding to federate selected.” 
               
               
                   
               
            
           
         
       
     
     The example shown in Table 5 illustrates that action signal generator  160  can generate signals to select a machine setting in a different way than those shown in Tables 3 and 4. 
     
       
         
           
               
             
               
                 TABLE 5 
               
               
                   
               
             
            
               
                 Human: “Johnny, select speed setting corresponding to crop state for next 
               
               
                 100 feet.” 
               
               
                 Operator Interface Controller: “Crop state speed setting selected for next 
               
               
                 100 feet.” 
               
               
                   
               
            
           
         
       
     
     Returning again to  FIG. 13 , block  906  illustrates that operator interface controller  231  can detect and process conditions for outputting a message or other information in other ways as well. For instance, other controller interaction system  656  can detect inputs from other controllers indicating that alerts or output messages should be presented to operator  260 . Block  908  shows that the outputs can be audio messages. Block  910  shows that the outputs can be visual messages, and block  912  shows that the outputs can be haptic messages. Until operator interface controller  231  determines that the current harvesting operation is completed, as indicated by block  914 , processing reverts to block  698  where the geographic location of harvester  100  is updated and processing proceeds as described above to update the user interface display. 
     Once the operation is complete, then any desired values that are displayed, or have been displayed on the user interface display, can be saved. Those values can also be used in machine learning to improve different portions of predictive model generator  210 , predictive map generator  212 , control zone generator  213 , control algorithms, or other items. Saving the desired values is indicated by block  916 . The values can be saved locally on agricultural harvester  100 , or the values can be saved at a remote server location or sent to another remote system. 
       FIG. 14  is a pictorial illustration showing one example of a user interface display  720  that can be generated on a touch sensitive display screen. In other implementations, the user interface display  720  may be generated on other types of displays. The touch sensitive display screen may be mounted in the operator compartment of agricultural harvester  100  or provided on the mobile device or elsewhere. 
     In the example shown in  FIG. 14 , user interface display illustrates that the touch sensitive display screen includes a display feature for operating a microphone  722  and a speaker  724 . Thus, the touch sensitive display may be communicably coupled to the microphone  722  and the speaker  724 . Block  726  indicates that the touch sensitive display screen can include a wide variety of user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. The operator  260  can actuate the user interface control actuators to perform various functions. 
     In the example shown in  FIG. 14 , user interface display  720  includes a field display portion  728  that displays at least a portion of the field in which the agricultural harvester  100  is operating. In  FIG. 14 , the field display portion  728  corresponds to a functional predictive map with control zone and regime zones. The field display portion  728  is shown with a current position marker  708  that corresponds to a current position of agricultural harvester  100  in the portion of the field shown in field display portion  728 . In one example, the operator may control the touch sensitive display in order to zoom into portions of field display portion  728  or to pan or scroll the field display portion  728  to show different portions of the field. The current position marker  708  may also be configured to identify the direction of travel of agricultural harvester  100 , a speed of travel of agricultural harvester  100 , or both. In  FIG. 14 , the shape of the current position marker  708  provides an indication as to the orientation of the agricultural harvester  100  within the field which may be used as an indication of a direction of travel of the agricultural harvester  100 . 
     The field display portion  728  further includes regime zones  732 A to  732 D, unharvested areas  712 A to  712 D, and harvested areas  714 A to  714 D. The harvested areas  714 A to  714 D represent areas of the field that have been harvested (such as by agricultural harvester  100 , or another machine), whereas unharvested areas  712 A to  712 D represent areas of the field that have yet to be harvested. Each regime zone  732  can include an unharvested area  712  and a harvested area  714 . As illustrated, regime zone  732 A includes unharvested area  712 A and harvested area  714 A, regime zone  732 B includes unharvested area  712 B and harvested area  714 B, regime zone  732 C includes unharvested area  712 C and harvested area  714 C, and regime zone  732 D includes unharvested area  712 D and harvested area  714 D. The different regime zones  732 , unharvested areas  712 , and harvested areas  714  can be displayed in various ways, including depictions with various display features such as colors, patterns, shades, symbols, as well as other numerous display features. 
     In the example of  FIG. 14 , user interface display  720  also has a current settings resolver indicator  721  that displays a current settings resolver for machine settings, a current speed indicator  723  that displays a current speed of agricultural harvester  100 , user actuatable display markers  733  which are, in one example, configured to receive a user input for adjusting a current speed of the agricultural harvester  100 , and a control display portion  738 . Control display portion  738  allows the operator to view information and to interact with user interface display  720  in various ways. 
     The actuators and display markers on user interface display  720  may be displayed as, for example, individual items, fixed lists, scrollable lists, drop down menus, or drop down lists. Additionally, the actuators and display markers can be touch sensitive such that the operator  260  may touch the touch sensitive actuators and display markers, such as with a finger or device, to activate the respective touch sensitive actuator or display marker. 
     In the example shown in  FIG. 14 , control display portion  738  includes a selection display column  746  and a control zone display column  748 . Selection display column  746  displays selectable or otherwise variable target machine settings corresponding to one or more WMAs of agricultural harvester  100 . In the illustrated example, selection display column  746  includes target speed settings  736 A to  736 C and user actuatable selection display markers  737 A to  737 C. Control zone display column  748  displays the various control zones corresponding to the field or to a location on the field. In the illustrated example, control zone display column  748  includes control zone display markers  739 A to  739 C. In the illustrated example, each control zone display marker  739  has a corresponding target speed setting  736  for an actuator in propulsion subsystem  250 , and each target speed setting  736  is selectable by the operator  260  by actuation of a selection display marker  737 . Thus, in the illustrated example, target speed setting  736 A corresponds to feed_rate control zone display marker  739 A, target speed setting  736 B corresponds to labor and grain loss control zone display marker  739 B, and target speed setting  736 C corresponds to crop state control zone display marker  739 C. Thus, in the illustrated example, the target speed setting recommended by the feed rate control zone, in which the agricultural harvester is controlled based on feed rate, such as desired or selected feed rate levels or values, is 5.1 miles per hour (mph) or approximately 8.21 kilometers per hour (kph). The target speed setting for a labor and grain loss control zone, in which the agricultural harvester is controlled based on labor costs and grain loss, such as desired or selected labor costs and grain loss levels or values, is 4.7 mph or approximately 7.56 kph. The target speed setting for a crop state control zone, in which the operation of the agricultural harvester  100  is controlled based on crop state, such as predicted or measured crop state levels or values, is 3.1 mph or approximately 4.99 kph. 
     As discussed previously herein, there may be multiple control zones for a given location of the field, and multiple target settings may be possible for a WMA at a given location, such as multiple target speed settings for an actuator in propulsion subsystem  250  at a given location on the field. In the illustrated example, the agricultural harvester is coming upon a competing settings area  733  in which there are multiple target speed settings for a WMA or set of WMAs in propulsion subsystem  250 , each target speed setting corresponding to a respective control zone. Where there are competing target settings for a WMA or a set of WMAs, regime zone generation system  490  generates regime zones to resolve multiple different competing target settings for the WMA or set of WMAs. Each regime zone may have a unique settings resolver for resolving competing target settings for a given WMA or set of WMAs. As shown in  FIG. 14 , the agricultural harvester and the competing settings area  733  are located in regime zone  732 C. Regime zone  732 C has a human settings resolver, as indicated by settings resolver indicator  721 . Thus, the operator of the agricultural harvester can resolve the competing target speed settings in the competing settings area  733  by selection of one of the target speed settings displayed in selection display column  746 , such as by actuation of one of the user actuatable selection display markers  737 . In the illustrated example, the operator of agricultural harvester has selected, such as by user input, for example, touch input, the target speed setting of 3.1 mph corresponding to the target speed setting for the crop state control zone. Thus, the WMA or set of WMAs in propulsion subsystem  250  will be controlled to propel the agricultural harvester at 3.1 mph in the competing settings area  733  based on the operator selection. 
     As previously discussed, various other settings resolvers can be used or displayed or both, such as an artificial intelligence settings resolver, a machine learning settings resolver, a settings resolver based on predicted or historic quality for competing target settings, a rules-based resolver, a performance criteria-based resolver, as well as various other resolvers. It should be understood that machine speed is merely one example of a machine setting that may be recommended by different regime zones and selected by a settings resolver for controlling the operation of agricultural harvester  100 . Various other machine settings corresponding to various other actuators of agricultural machine  100  can also be recommended by various regime zones and selected by various settings resolvers. 
     Additionally, while regime zones are shown as being displayed in the example illustrated in  FIG. 14 , it should be understood that control zones can also be displayed, alternatively or in addition to regime zones. Further, while the regime zones illustrated in  FIG. 14  are of a particular number, shape, and size it should be understood that any number of regime zone(s) can be displayed of a variety of shapes and sizes. Additionally, and as described previously herein, regime zones can overlap at one or more locations on a field. 
     It can thus be seen that a map is obtained by an agricultural harvester and shows machine speed values at different geographic locations of a field being harvested. An in-situ sensor on the harvester senses a characteristic as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for different locations in the field based on the values of the machine speed in the map and the characteristic sensed by the in-situ sensor. A control system controls controllable subsystem based on the control values in the predictive map. 
     A control value is a value upon which an action can be based. A control value, as described herein, can include any value (or characteristics indicated by or derived from the value) that may be used in the control of agricultural harvester  100 . A control value can be any value indicative of an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value may include any of the values provided by a map, such as any of the maps described herein. For instance, a control value can be a value provided by an information map, a value provided by prior information map, or a value provided predictive map, such as a functional predictive map. A control value can also include any of the characteristics indicated by or derived from the values detected by any of the sensors described herein. In other examples, a control value can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine. 
     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. The processors and servers are functional parts of the systems or devices to which the processors and servers 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, the user actuatable operator interface mechanisms 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, including but not limited to artificial intelligence components, such as neural networks, 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. 15  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. 15 , some items are similar to those shown in  FIG. 2  and those items are similarly numbered.  FIG. 15  specifically shows that predictive model generator  210  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. 15 , agricultural harvester  600  accesses systems through remote server location  502 . 
       FIG. 15  also depicts another example of a remote server architecture.  FIG. 15  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. 16  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. 17-18  are examples of handheld or mobile devices. 
       FIG. 16  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. 17  shows one example in which device  16  is a tablet computer  600 . In  FIG. 17 , computer  601  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  601  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  601  may also illustratively receive voice inputs as well. 
       FIG. 18  is similar to  FIG. 17  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. 19  is one example of a computing environment in which elements of  FIG. 2  can be deployed. With reference to  FIG. 19 , 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. 19 . 
     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. 19  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. 19  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. 19 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 19 , 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. 19  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. 
     Example 1 is a method of controlling an agricultural work machine comprising: 
     accessing a map having a set of regime zones defined on the map, each regime zone having a corresponding settings resolver; 
     detecting a geographic location of the agricultural work machine in a field; 
     identifying a plurality of different target actuator settings corresponding to the geographic location of the agricultural work machine; 
     identifying a regime zone based on the geographic location of the agricultural work machine; 
     selecting one of the plurality of different target actuator settings as a resolved target actuator setting based on the settings resolver corresponding to the identified regime zone; and 
     controlling an actuator of the agricultural work machine based on the resolved target actuator setting. 
     Example 2 is the method of any or all previous examples and further comprising: 
     identifying the settings resolver corresponding to the identified regime zone. 
     Example 3 is the method of any or all previous examples, wherein identifying the settings resolver comprises: 
     identifying, as the settings resolver corresponding to the regime zone, a human settings resolver; and 
     displaying the plurality of different target actuator settings on a user interface display. 
     Example 4 is the method of any or all previous examples, wherein selecting one of the plurality of different target actuator settings comprises: 
     detecting, via the user interface display, an operator input selecting one of the displayed plurality of different target actuator settings as the resolved target actuator setting. 
     Example 5 is the method of claim  2  of any or all previous examples, wherein identifying the setting resolver comprises: 
     executing one of an artificial intelligence component, a machine learning component, and an artificial neural network component to identify the resolved target actuator setting. 
     Example 6 is the method of any or all previous examples, wherein identifying the setting resolver comprises: 
     executing a rules-based setting resolver to identify the resolved target speed setting. 
     Example 7 is the method of any or all previous examples, wherein identifying a regime zone based on the geographic location of the agricultural work machine comprises: 
     identifying a plurality of different overlapping regime zones. 
     Example 8 is the method of any or all previous examples and further comprising: 
     accessing a precedence hierarchy of regime zones; and 
     selecting one of the plurality of different overlapping regime zones as the identified regime zone based on a position in the precedence hierarchy of regime zones of each of the plurality of different overlapping regime zones. 
     Example 9 is the method of any or all previous examples, wherein the map comprises a set of control zones defined on the map, each control zone comprising a corresponding target actuator setting and 
     wherein identifying a plurality of different target actuator settings comprises: 
     identifying a plurality of different control zones based on the geographic location of the agricultural work machine; and 
     identifying, as the plurality of different target actuator settings, the target actuator settings corresponding to the identified plurality of different control zones. 
     Example 10 is the method of any or all previous examples, wherein identifying a plurality of different target actuator settings comprises: 
     identifying, as the plurality of different target actuator settings, target actuator settings from a plurality of different sources, the plurality of different sources comprising at least one of an in-situ sensor, an operator input, and an input received from a different agricultural work machine. 
     Example 11 is an agricultural work machine comprising: 
     a controllable actuator subsystem; 
     a geographic position sensor that detects a geographic location of the agricultural work machine in a field; 
     a zone controller that accesses a map comprising a set of regime zones defined on the map, each regime zone comprising a corresponding settings resolver, the zone controller identifying a plurality of different target actuator settings corresponding to the geographic location of the agricultural work machine, identifying a regime zone based on the geographic location of the agricultural work machine, and selecting one of the plurality of different target actuator settings as a resolved target setting based on the setting resolver corresponding to the identified regime zone; and 
     a settings controller controlling the actuator subsystem of the agricultural work machine based on the resolved target actuator setting. 
     Example 12 is the agricultural work machine of any or all previous examples, wherein the zone controller is configured to identify the settings resolver corresponding to the identified regime zone prior to selecting one of the plurality of different target actuator settings as a resolved target actuator setting. 
     Example 13 is the agricultural work machine of any or all previous examples and further comprising: 
     a user interface display, wherein the zone controller is configured to identify the setting resolver by identifying, as the settings resolver corresponding to the regime zone, a human setting resolver, and display the plurality of different target actuator settings on the user interface display. 
     Example 14 is the agricultural work machine of any or all previous examples and further comprising: 
     an operator interface controller configured to detect, via the user interface display, an operator input selecting one of the displayed plurality of different target actuator settings as the resolved target actuator setting. 
     Example 15 is the agricultural work machine of any or all previous examples, wherein the settings resolver comprises at least one of an artificial intelligence component, a machine learning component, and an artificial neural network component. 
     Example 16 is the agricultural work machine of any or all previous examples, wherein the settings resolver comprises a rules-based settings resolver. 
     Example 17 is the agricultural work machine of any or all previous examples, wherein the map comprises a plurality of different overlapping regime zones and wherein the zone controller is configured to access a precedence hierarchy of regime zones and select one of the plurality of different overlapping regime zones as the identified regime zone based on a position in the precedence hierarchy of regime zones of each of the plurality of different overlapping regime zones. 
     Example 18 is the agricultural work machine of any or all previous examples, wherein the map comprises a set of control zones defined on the map, each control zone comprising a corresponding target actuator setting and wherein the zone controller identifies a plurality of different control zones based on the geographic location of the agricultural work machine, and identifies, as the plurality of different target actuator settings, the target actuator settings corresponding to the identified plurality of different control zones. 
     Example 19 is the agricultural work machine of any or all previous examples and further comprising: 
     an in-situ sensor that generates a sensor signal indicative of a sensed characteristic; 
     an operator input mechanism that generates an operator input signal based on an operator input; and 
     a communication system that receives communication from a remote work machine, and 
     wherein the zone controller identifies the plurality of different target actuator settings based on at least one of the sensor signal, the operator input signal, and the communication received from the remote work machine. 
     Example 20 is a method of controlling an agricultural work machine comprising: 
     accessing a map comprising a set of regime zones defined on the map, each regime zone comprising a corresponding settings resolver; 
     detecting a geographic location of the agricultural work machine in a field; 
     identifying a plurality of different target settings corresponding to the geographic location of the agricultural work machine; 
     identifying a regime zone based on the geographic location of the agricultural work machine; 
     selecting one of the plurality of different target settings as a resolved target setting based on the settings resolver corresponding to the identified regime zone; and 
     controlling a controllable subsystem of the agricultural work machine based on the resolved target setting. 
     Although the subject matter has been described in language specific to structural features 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.