Patent Publication Number: US-2023161347-A1

Title: Machine control using a predictive map

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a continuation of and claims priority of U.S. patent application Ser. No. 17/066,825, filed Oct. 9, 2020, the content of which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE DESCRIPTION 
     The present description relates to agricultural machines, forestry machines, construction machines, and turf management machines. 
     BACKGROUND 
     There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and windrowers. Some harvesters can be fitted with different types of heads to harvest different types of crops. 
     Topographic characteristics can have a number of deleterious effects on the harvesting operation. For instance, when a harvester travels over a sloped feature the pitch or roll of the harvester may impede performance of the harvester. Therefore, an operator may attempt to modify control of the harvester, upon encountering a slope during the harvesting operation. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a partial pictorial, partial schematic illustration of one example of a combine harvester. 
         FIG.  2    is a block diagram showing some portions of an agricultural harvester in more detail, according to some examples of the present disclosure. 
         FIGS.  3 A- 3 B  (collectively referred to herein as  FIG.  3   ) show a flow diagram illustrating an example of operation of an agricultural harvester in generating a map. 
         FIG.  4    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 topographical map, detecting an agricultural characteristic, and generating a functional predictive draper speed map for use in controlling the agricultural harvester during a harvesting operation. 
         FIG.  6    is a block diagram showing one example of a control zone generator. 
         FIG.  7    is a flow diagram illustrating one example of the operation of the control zone generator shown in  FIG.  6   . 
         FIG.  8    illustrates a flow diagram showing an example of operation of a control system in selecting a target settings value to control an agricultural harvester. 
         FIG.  9    is a block diagram showing one example of an operator interface controller. 
         FIG.  10    is a flow diagram illustrating one example of an operator interface controller. 
         FIG.  11    is a pictorial illustration showing one example of an operator interface display. 
         FIG.  12    is a block diagram showing one example of an agricultural harvester in communication with a remote server environment. 
         FIGS.  13 - 15    show examples of mobile devices that can be used in an agricultural harvester. 
         FIG.  16    is a block diagram showing one example of a computing environment that can be used in an agricultural harvester. 
     
    
    
     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 such as a predictive draper speed map. In some examples, the predictive map can be used to control the speed of one or more draper belts on an agricultural work machine. 
     As discussed above, performance of an agricultural harvester may be degraded when the agricultural harvester engages a topographic feature, such as a side slope. For example, when the agricultural harvester is navigating a side slope, one half of the header will be feeding the severed material downhill and the other half of the header will be feeding the severed material uphill. When both draper belts are kept at the same speed in this situation, material being propelled downhill can overshoot the center belt that guides the material into the feeder house. This can cause grain loss or even plug the header in some instances. 
     Or, for example, as an agricultural harvester ascends and descends slopes, the ground travel speed of the harvester may change. This change of speed has an effect on the amount of vegetation encountered by the harvester over a given amount of time. This increased amount of vegetation may require that the draper belt speed be increased to reduce the likelihood that material plugs the header. 
     A topographic map illustratively maps elevations of the ground across different geographic locations in a field of interest. Since ground slope is indicative of a change in elevation, having two or more elevation values allows for calculation of slope across the areas having known elevation values. Greater granularity of slope can be accomplished by having more areas with known elevation values. As an agricultural harvester travels across the terrain in known directions, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., areas of changing elevation). Topographic characteristics, when referred to below, can include, but are not limited to, the elevation, slope (e.g., including the machine orientation relative to the slope), and ground profile (e.g., roughness). 
     The present discussion, thus, proceeds with respect to systems that receive a prior information map of a field or map generated during a prior operation and also use an in-situ sensor to detect a variable indicative of one or more of an agricultural characteristic during a harvesting operation. The systems generate a model that models a relationship between the values on the prior information map and the output values from the in-situ sensor. The model is used to generate a functional predictive map that predicts, for example, draper speed at different locations in the field. The functional predictive 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 functional predictive map can be used to control the speed of one or more draper belts. 
       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 . In the illustrated example, the cutter  104  is included on the header  102 . Additionally, the header  104  can include one or more draper belts  177  (shown in  FIG.  2   ) and a feeder house delivery mechanism (a center belt)  181  (shown in  FIG.  2   ) that receives material from draper belts  177  and delivers the material to feeder house  106 . 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. An operator command is a command by an operator. 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 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  179  (shown in  FIG.  2   ) 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 draper belt speed map, and uses the functional predictive draper belt 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 in the field, such as characteristics of crop or weeds present in the field. Characteristics of the field may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed type, soil moisture, surface quality; characteristics of crop properties such as crop height, crop moisture, crop density, crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight; and characteristics of machine performance such as loss levels, job quality, fuel consumption, and power utilization. A relationship between the characteristic values obtained from in-situ sensor signals and the prior information map values is identified, and that relationship is used to generate a new functional predictive map. A functional predictive map predicts values at different geographic locations in a field, and one or more of those values 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 draper belt 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   . Again, while the present discussion proceeds with respect to the agricultural harvester and, particularly, a combine harvester, the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines. 
       FIG.  2    is a block diagram showing some portions of an example agricultural harvester  100 .  FIG.  2    shows that agricultural harvester  100  illustratively includes one or more processors or servers  201 , data store  202 , geographic position sensor  204 , communication system  206 , and one or more in-situ sensors  208  that sense one or more agricultural characteristics of a field concurrent with a harvesting operation. An agricultural characteristic can include any characteristic that can have an effect of the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, the weather. Other types of agricultural characteristics are also included. The in-situ sensors  208  generate values corresponding to the sensed characteristics. The agricultural harvester  100  also includes a predictive model or relationship generator (collectively referred to hereinafter as “predictive model generator  210 ”), predictive map generator  212 , control zone generator  213 , control system  214 , one or more controllable subsystems  216 , and an operator interface mechanism  218 . The agricultural harvester  100  can also include a wide variety of other agricultural harvester functionality  220 . The in-situ sensors  208  include, for example, on-board sensors  222 , remote sensors  224 , and other sensors  226  that sense characteristics of a field during the course of an agricultural operation. Predictive model generator  210  illustratively includes a prior information variable-to-in-situ variable model generator  228 , and predictive model generator  210  can include other items  230 . Control system  214  includes communication system controller  229 , operator interface controller  231 , a settings controller  232 , path planning controller  234 , feed rate controller  236 , header and reel controller  238 , draper belt controller  240 , deck plate position controller  242 , residue system controller  244 , machine cleaning controller  245 , zone controller  247 , and system  214  can include other items  246 . Controllable subsystems  216  include machine and header actuators  248 , propulsion subsystem  250 , steering subsystem  252 , residue subsystem  138 , machine cleaning subsystem  254 , and subsystems  216  can include a wide variety of other subsystems  256 . 
       FIG.  2    also shows that agricultural harvester  100  can receive prior information map  258 . As described below, the prior information map  258  includes, for example, a topographic map. 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 include, an optical sensor such as a camera. The in-situ sensors  208  also include remote in-situ sensors  224  that capture in-situ information. In-situ data include data taken from a sensor on-board the harvester or taken by any sensor where the data are detected during the harvesting operation. 
     Predictive model generator  210  generates a model that is indicative of a relationship between the values sensed by the in-situ sensor  208  and a value mapped to the field by the prior information map  258 . For example, if the prior information map  258  maps a topographic characteristic values to different locations in the field, and the in-situ sensor  208  is sensing a value indicative of a draper belt speed then prior information variable-to-in-situ variable model generator  228  generates a predictive draper belt speed model that models the relationship between the topographic characteristic and draper belt speed. The predictive draper belt speed model can also be generated based on topographic characteristic values from the prior information map  258  and multiple in-situ data values generated by in-situ sensors  208 . Predictive map generator  212  uses the predictive draper belt speed model generated by predictive model generator  210  to generate a functional predictive draper belt speed map that maps predictions of the value of the draper belt speed 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. 
     As shown in  FIG.  2   , predictive map  264  predicts the value of a sensed characteristic (sensed by in-situ sensors  208 ), or a characteristic related to the sensed characteristic, at various locations across the field based upon a prior information value in prior information map  258  at those locations and using the predictive model. For example, if predictive model generator  210  has generated a predictive model indicative of a relationship between a topographic characteristic and a draper belt speed then, given the topographic characteristic value at different locations across the field, predictive map generator  212  generates a predictive map  264  that predicts the value of the draper belt speed at different locations across the field. The topographic characteristic value, obtained from the topographic map, at those locations and the relationship between the topographic characteristic and the draper belt 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 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 draper belt speed displayed on the map, based on the operator&#39;s observation. Settings controller  232  can generate control signals to control various settings on the agricultural harvester  100  based upon predictive map  264 , the predictive control zone map  265 , or both. For instance, settings controller  232  can generate control signals to control machine and header actuators  248 . In response to the generated control signals, the machine and header actuators  248  operate to control, for example, one or more of the sieve and chaffer settings, 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), corn header functionality, internal distribution control and other actuators  248  that affect the other functions of the agricultural harvester  100 . Path planning controller  234  illustratively generates control signals to control steering subsystem  252  to steer agricultural harvester  100  according to a desired path. Path planning controller  234  can control a path planning system to generate a route for agricultural harvester  100  and can control propulsion subsystem  250  and steering subsystem  252  to steer agricultural harvester  100  along that route. Feed rate controller  236  can control various subsystems, such as propulsion subsystem  250  and machine actuators  248 , to control a feed rate based upon the predictive map  264  or predictive control zone map  265  or both. For instance, as agricultural harvester  100  approaches a weed patch having an intensity value above a selected threshold, feed rate controller  236  may reduce the speed of machine  100  to maintain constant feed rate of biomass through the machine. Header and reel controller  238  can generate control signals to control a header or a reel or other header functionality. Draper belt controller  240  can generate control signals to control a draper belt  171  or other draper functionality 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. 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 . Other controllers included on the agricultural harvester  100  can control other subsystems based on the predictive map  264  or predictive control zone map  265  or both as well. 
       FIGS.  3 A and  3 B  (collectively referred to herein as  FIG.  3   ) show a flow diagram illustrating one example of the operation of agricultural harvester  100  in generating a predictive map  264  and predictive control zone map  265  based upon prior information map  258 . 
     At  280 , agricultural harvester  100  receives prior information map  258 . Examples of prior information map  258  or receiving prior information map  258  are discussed with respect to blocks  281 ,  282 ,  284  and  286 . As discussed above, prior information map  258  maps values of a variable, corresponding to a first characteristic, to different locations in the field, as indicated at block  282 . As indicated at block  281 , receiving the prior information map  258  may involve selecting one or more of a plurality of possible prior information maps that are available. For instance, one prior information map may be a 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 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 that are exiting agricultural harvester  100 . The weed seed 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 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 indicative of a characteristic, for example, a draper belt speed, as indicated by block  288 . Examples of in-situ sensors are discussed with respect to blocks  222 ,  290 , and  226 . As explained above, the in-situ sensors  208  include on-board sensors  222 , such as a camera; 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  is 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, 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 draper belt speed map can be used to control one or more subsystems  216 . For instance, the predictive draper belt speed map can include draper belt control setting values georeferenced to locations within the field being harvested. The draper belt speed values from the predictive draper belt speed map can be extracted and used to control the draper belt speeds of the header. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive draper belt speed 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.  28  Some examples of detecting trigger criteria are discussed with respect to blocks  318 ,  320 ,  321 ,  322 , and  324 . For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors  208 . In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors  208  that exceeds a threshold triggers or causes the predictive model generator  210  to generate a new predictive model that is used by predictive map generator  212 . Thus, as agricultural harvester  100  continues a harvesting operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors  208  triggers the creation of a new relationship represented by a predictive model generated by predictive model generator  210 . Further, new predictive map  264 , predictive control zone map  265 , or both can be regenerated using the new predictive model. Block  318  represents detecting a threshold amount of in-situ sensor data used to trigger creation of a new predictive model. 
     In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors  208  are changing, 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 one or more of a topographic map  332  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 a draper belt speed sensor, such as draper belt sensor  336 , a material flow sensor  337  and a processing system  338 . In some instances, draper belt sensor  336  or material flow sensor  337  may be located on board the agricultural harvester  100 . In other examples, draper belt sensor  336  or material flow sensor  337  could be remote from agricultural harvester  100 . The processing system  338  processes sensor data generated from draper belt sensor  336  or material flow sensor  337  to generate processed data, indicative of a draper belt speed or a material flow characteristic, respectively. 
     In some examples, draper belt sensor  336  may be a rotational sensor coupled to a draper belt roller drive system component. For instance a tone wheel/hall effect type combination sensor. In another example, draper belt sensor  336  senses a user input from an operator that is indicative of a commanded draper belt speed. In other examples, draper belt sensor  336  may be a different type of sensor. Processing system  338  processes one or more sensor signals from the draper belt sensor  336  to generate processed sensor data identifying the draper belt speed. 
     Material flow sensor  337  can sense one or more characteristics of a material flow. Some characteristics of material flow include volumetric flow, mass flow, material content, flow uniformity, material bunching, material stalling, underfeeding, grain loss due to underfeeding, etc. In some examples, material flow sensor  337  includes an optical sensor, such as a camera, that can sense material flow as it moves through the field of view of the optical sensor. In another example, material flow sensor  337  includes an ultrasonic sensor, lidar sensor, or radar sensor. In other examples, material flow sensor  337  can include other sensors as well. 
     As shown in  FIG.  4   , the example predictive model generator  210  includes one or more of a draper belt speed-to-topographic characteristic model generator  342  and material flow-to-topographic characteristic model generator  343 . 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 predictive models. 
     Model generator  342  identifies a relationship between one or more draper belt speed values detected in sensor data  340 , at a geographic location corresponding to the sensor data  340 , and topographic characteristic values from the topographic map  332  corresponding to the same location in the field where the draper belt speed values were geolocated. Based on this relationship established by model generator  342 , model generator  342  generates a predictive model  350 . The predictive model  350  is used by draper belt map generator  352  to predict draper belt speed values at different locations in the field based upon the georeferenced topographic characteristic values contained in the topographic map  332  at the same locations in the field. 
     Model generator  343  identifies a relationship between one or more material flow characteristic values detected in sensor data  340 , at a geographic location corresponding to the sensor data  340 , and topographic characteristic values from the topographic map  332  corresponding to the same location in the field where the material flow characteristic values were geolocated. Based on this relationship established by model generator  343 , model generator  343  generates a predictive model  350 . The predictive model  350  is used by material flow map generator  353  to predict material flow characteristics at different locations in the field based upon the georeferenced topographic characteristic values contained in the topographic map  332  at the same locations in the field. 
     In light of the above, the predictive model generator  210  is operable to produce a plurality of predictive models, such as one or more of the predictive models generated by model generators  342 ,  343  and  348 . In another example, two or more of the predictive models described above may be combined into a single predictive model that can be used to predict two or more draper belt speeds or material flow characteristics based upon the topographic value at different locations in the field. Any of these predictive 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 draper belt map generator  352  and material flow map generator  353 . In other examples, the predictive map generator  212  may include additional 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 maps for other types of characteristics. Draper belt map generator  352  receives the predictive model  350  and generates a predictive map that predicts one or more draper belt speeds at different locations in the field based upon values from the topographic map  332  and the predictive model  350 . Material flow map generator  353  receives the predictive model  350  and generates a predictive map that predicts one or more material flow characteristics at different locations in the field based upon values from the topographic map  332  and the predictive model  350 . 
     Predictive map generator  212  outputs a predictive map  360  that is predictive of one or more draper belt speeds, one or more material flow characteristics, or some combination thereof. The generated predictive map  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 predictive map  360 . One or more predictive maps  360  may be provided to control system  214 , which generates control signals to control one or more of the controllable subsystems  216  based upon the one or more predictive maps  360 . 
       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 predictive map  360 . At block  362 , predictive model generator  210  and predictive map generator  212  receive a prior topographic map  332 . At block  364 , processing system  338  receives one or more sensor signals from draper belt sensor  336 , material flow sensor  337  or both. As discussed above, the sensor may be a draper belt speed sensor  336  that senses a draper belt speed  366 ; an operator input sensor that senses an operator input  367  indicative of draper speed; a material flow sensor that senses a material flow characteristic  368 ; or another type of sensor that senses some other characteristic  370 . 
     At block  372 , processing system  338  processes the one or more received sensor signals to generate data indicative of one or more draper belt speed, one or more material flow characteristic or some combination thereof. At block  382 , predictive model generator  210  also obtains the geographic location corresponding to the sensor data. For instance, the predictive model generator  210  can obtain the geographic position from geographic position sensor  204  and determine, based upon machine delays, machine speed, sensor calibrations, etc., a precise geographic location where the sensor data corresponds to. 
     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 obtained from a prior information map, such as topographic map  332 , and an agricultural characteristic being sensed by the in-situ sensor  208  or a related characteristic. For instance, predictive model generator  210  may generate a predictive draper belt model that models the relationship between a topographic characteristic and a sensed draper belt speed indicated by the sensor data obtained from in-situ sensor  208 . Or for instance, predictive model generator  210  may generate a predictive material flow model that models the relationship between a topographic characteristic and a sensed material flow characteristic indicated by the sensor data obtained from in-situ sensor  208 . 
     At block  386 , the predictive model, such as predictive model  350 , is provided to predictive map generator  212  which generates a predictive map  360  that maps a predicted characteristic based on the predictive model  350  and the topographic map  332 . For instance, predictive map generator  212  can generate a predictive map  360  that maps a predicted desirable draper belt speed based on the predictive model  350  and the topographic map  332 . Or for instance, predictive map generator  212  can generate a predictive map  360  that maps a predicted material flow characteristic based on the predictive model  350  and the topographic map  332 . The predictive map  360  can be generated during the course of an agricultural operation. Thus, as an agricultural harvester is moving through a field performing an agricultural operation, the predictive map  360  is generated as the agricultural operation is being performed. 
     At block  394 , predictive map generator  212  outputs the predictive map  360 . At block  391  predictive draper belt map generator  212  outputs the predictive map for presentation to and possible interaction by operator  260 . At block  393 , predictive map generator  212  may configure the map for consumption by control system  214 . At block  395 , predictive map generator  212  can also provide the map  360  to control zone generator  213  for generation and incorporation of control zones. At block  397 , predictive map generator  212  configures the predictive map  360  in other ways as well. The predictive map  360  (with or without the control zones) is provided to control system  214 . At block  396 , control system  214  generates control signals to control the controllable subsystems  216  based upon the predictive map  360 . As indicated by block  400 , the draper belt speed may be increased. The draper belt speed can be increased, for instance, when excessive material bunching is occurring. 
     As indicated by block  401 , the draper belt speed may be decreased. It is desirable, in some instances to decrease the draper belt speed when the ground is sloped downward in the direction of travel of the draper belt. The draper belt speed can be decreased, for instance, when there is excessive underfeeding from the material overshooting the feeder house delivery mechanism  181  (e.g., a center belt perpendicular to the header draper belts  177 ). 
     As indicated by block  402 , the individual draper belts may be controlled independently of one another. For example, when the header is orientationally rolled, the draper belts on each side can be controlled independently to account for the uneven effect of gravity on the material moving on the belts. 
     A controllable subsystem  216  can be controlled in other ways as well, as indicated by block  403 . 
       FIG.  6    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 choice 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.  7    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  436 ,  437 ,  438 , or  440 . 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. For example, regime zone definition criteria can be based on topographic characteristics. 
     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. 
       FIG.  8    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. 
     At block  640 , zone controller  247  accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target settings into a resolved target 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. For instance, where the selected WMA is a machine or header actuator  248 , zone controller  247  provides the resolved target setting to settings controller  232  or header/real controller  238  or both to generate control signals based upon the resolved target setting, and those generated control signals are applied to the machine or header actuators  248 . 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  31  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.  9    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.  9    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.  10    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.  10    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 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. 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 draper belt speed map, the displayed field may show the different draper belt speed existing in the field georeferenced within the displayed field. Or for instance, if the received map is a material flow map, the displayed field may show grain loss to underfeeding. The mapped characteristics can be shown in the previously visited areas (as shown in block  714 ) and in the upcoming areas (as shown in block  712 ). Block  718  indicates an example in which the displayed field includes other items as well. 
     As shown in  FIG.  11   , display portion  738  includes an interactive flag display portion, indicated generally at  741 . Interactive flag display portion  741  includes a flag column  739  that shows flags that have been automatically or manually set. Flag actuator  740  allows operator  260  to mark a location, such as the current location of the agricultural harvester, or another location on the field designated by the operator and add information indicating the loss level found at the current location, due to for example, underfeeding. For instance, when the operator  260  actuates the flag actuator  740  by touching the flag actuator  740 , touch gesture handling system  664  in operator interface controller  231  identifies the current location as one where agricultural harvester  100  encountered high loss level. When the operator  260  touches the button  742 , touch gesture handling system  664  identifies the current location as a location where agricultural harvester  100  encountered medium loss level. When the operator  260  touches the button  744 , touch gesture handling system  664  identifies the current location as a location where agricultural harvester  100  encountered low loss level. Upon actuation of one of the flag actuators  740 ,  742 , or  744 , touch gesture handling system  664  can control visual control signal generator  684  to add a symbol corresponding to the identified loss level on field display portion  728  at a location the user identifies. In this way, areas of the field where the predicted value did not accurately represent an actual value can be marked for later analysis, and can also be used in machine learning. In other examples, the operator may designate areas ahead of or around the agricultural harvester  100  by actuating one of the flag actuators  740 ,  742 , or  744  such that control of the agricultural harvester  100  can be undertaken based on the value designated by the operator  260 . 
     Display portion  738  also includes an interactive marker display portion, indicated generally at  743 . Interactive marker display portion  743  includes a symbol column  746  that displays the symbols corresponding to each category of values or characteristics (in the case of  FIG.  11   , grain loss level due to underfeeding) that is being tracked on the field display portion  728 . Display portion  738  also includes an interactive designator display portion, indicated generally at  745 . Interactor designator display portion  745  includes a designator column  748  that shows the designator (which may be a textual designator or other designator) identifying the category of values or characteristics (in the case of  FIG.  11   , grain loss level due to underfeeding). Without limitation, the symbols in symbol column  746  and the designators in designator column  748  can include any display feature such as different colors, shapes, patterns, intensities, text, icons, or other display features, and can be customizable by interaction of an operator of agricultural harvester  100 . 
     Display portion  738  also includes an interactive value display portion, indicated generally at  747 . Interactive value display portion  747  includes a value display column  750  that displays selected values. The selected values correspond to the characteristics or values being tracked or displayed, or both, on field display portion  728 . The selected values can be selected by an operator of the agricultural harvester  100 . The selected values in value display column  750  define a range of values or a value by which other values, such as predicted values, are to be classified. Thus, in the example in  FIG.  11   , a predicted or measured grain loss level meeting or greater than 1.5 bushels/acre is classified as “high loss level”, a predicted or measured loss level meeting or greater than  1  bushels/acre (but less than  1 . 5  bushels/acre) is classified as “medium loss level”, and a predicted or measured loss level meeting or less than 0.5 bushels/acre is classified as “low loss level.” In some examples, the selected values may include a range, such that a predicted or measured value that is within the range of the selected value will be classified under the corresponding designator. For example, a predicted or measured loss level of 0.8 bushels/acre may, in the case the selected value includes a range, be designated as a “medium loss level” rather than a “low loss level” even though the predicted or measured loss level of 0.8 bushels/acre exceeds the “low loss level” value but is less than the “medium loss level” value. The selected values in value display column  750  are adjustable by an operator of agricultural harvester  100 . In one example, the operator  260  can select the particular part of field display portion  728  for which the values in column  750  are to be displayed. Thus, the values in column  750  can correspond to values in display portions  712 ,  714  or  730 . 
     Display portion  738  also includes an interactive threshold display portion, indicated generally at  749 . Interactive threshold display portion  749  includes a threshold value display column  752  that displays action threshold values. Action threshold values in column  752  may be threshold values corresponding to the selected values in value display column  750 . If the predicted or measured values of characteristics being tracked or displayed, or both, satisfy the corresponding action threshold values in threshold value display column  752 , then control system  214  takes the action identified in column  754 . In some instances, a measured or predicted value may satisfy a corresponding action threshold value by meeting or exceeding the corresponding action threshold value. In one example, operator  260  can select a threshold value, for example, in order to change the threshold value by touching the threshold value in threshold value display column  752 . Once selected, the operator  260  may change the threshold value. The threshold values in column  752  can be configured such that the designated action is performed when the measured or predicted value of the characteristic exceeds the threshold value, equals the threshold value, or is less than the threshold value. In some instances, the threshold value may represent a range of values, or range of deviation from the selected values in value display column  750 , such that a predicted or measured characteristic value that meets or falls within the range satisfies the threshold value. For instance, in the example of  FIG.  11   , a predicted value that falls within 10% of 1.5 bushels/acre will satisfy the corresponding action threshold value (of within 10% of 1.5 bushels/acre) and an action, such as reducing the draper belt speed, will be taken by control system  214 . In other examples, the threshold values in column threshold value display column  752  are separate from the selected values in value display column  750 , such that the values in value display column  750  define the classification and display of predicted or measured values, while the action threshold values define when an action is to be taken based on the measured or predicted values. For example, while a predicted or measured loss value of 1.0 bushels/acre may be designated as a “medium loss level” for purposes of classification and display, the action threshold value may be 1.2 bushels/acre such that no action will be taken until the loss value satisfies the threshold value. In other examples, the threshold values in threshold value display column  752  may include distances or times. For instance, in the example of a distance, the threshold value may be a threshold distance from the area of the field where the measured or predicted value is georeferenced that the agricultural harvester  100  must be before an action is taken. For example, a threshold distance value of 10 feet would mean that an action will be taken when the agricultural harvester is at or within 10 feet of the area of the field where the measured or predicted value is georeferenced. In an example where the threshold value is time, the threshold value may be a threshold time for the agricultural harvester  100  to reach the area of the field where the measured or predictive value is georeferenced. For instance, a threshold value of 5 seconds would mean that an action will be taken when the agricultural harvester  100  is 5 seconds away from the area of the field where the measured or predicted value is georeferenced. In such an example, the current location and travel speed of the agricultural harvester can be accounted for. 
     Display portion  738  also includes an interactive action display portion, indicated generally at  751 . Interactive action display portion  751  includes an action display column  754  that displays action identifiers that indicated actions to be taken when a predicted or measured value satisfies an action threshold value in threshold value display column  752 . Operator  260  can touch the action identifiers in column  754  to change the action that is to be taken. In some examples, then a threshold is met, multiple actions may be taken. For instance, one draper belt speed may be increased while a second draper belt speed may be decreased in response to a threshold being satisfied. 
     The actions that can be set in column  754  can be any of a wide variety of different types of actions. For example, the actions can include a setting change action for changing a setting of an internal actuator or another WMA or set of WMAs or for implementing a settings change action that changes a setting of a threshing rotor speed, a cleaning fan speed, a position (e.g., tilt, height, roll, etc.) of the header, along with various other settings. These are examples only, and a wide variety of other actions are contemplated herein. 
     The items shown on user interface display  720  can be visually controlled. Visually controlling the interface display  720  may be performed to capture the attention of operator  260 . For instance, the display markers can be controlled to modify the intensity, color, or pattern with which the display markers are displayed. Additionally, the display markers may be controlled to flash. The described alterations to the visual appearance of the display markers are provided as examples. Consequently, other aspects of the visual appearance of the display markers may be altered. Therefore, the display markers can be modified under various circumstances in a desired manner in order, for example, to capture the attention of operator  260 . Additionally, while a particular number of items are shown on user interface display  720 , this need not be the case. In other examples, more or less items, including more or less of a particular item can be included on user interface display  720 . 
     Returning now to the flow diagram of  FIG.  10   , the description of the operation of operator interface controller  231  continues. At block  760 , operator interface controller  231  detects an input setting a flag and controls the touch sensitive user interface display  720  to display the flag on field display portion  728 . 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 user interface display  720  to display actuators for modifying user interface display  720  and for modifying machine control. For instance, block  770  represents that one or more of the actuators for setting or modifying the values in columns  739 ,  746 , and  748  can be displayed. Thus, the user can set flags and modify characteristics of those flags. For example, a user can modify the loss levels and loss level designators corresponding to the flags. Block  772  represents that action threshold values in column  752  are displayed. Block  776  represents that the actions in column  754  are displayed, and block  778  represents that the measured in-situ data in column  750  is displayed. Block  780  indicates that a wide variety of other information and actuators can be displayed on user interface display  720  as well. 
     At block  782 , operator input command processing system  654  detects and processes operator inputs corresponding to interactions with the user interface display  720  performed by the operator  260 . Where the user interface mechanism on which user interface display  720  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 in column  750  satisfy threshold conditions present in column  752 . 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 identified in column  754 . 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 current draper belt speed” 
               
               
                 Operator Interface Controller: “Current draper belt speed is 180 rpm” 
               
               
                 Operator: “Johnny, is there any underfeeding?” 
               
               
                 Operator Interface Controller: “Currently there is an undetectable  
               
               
                 amount of underfeeding.”  
               
               
                   
               
            
           
         
       
     
     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, left draper belt speed  
               
               
                 has averaged: 190 rpm right draper belt speed has averaged: 150 rpm” 
               
               
                   
               
            
           
         
       
     
     Operator Interface Controller: “Next 1 acre comprises left draper belt speed is predicted to average: 210 rpm right draper belt speed is predicted to average: 130 rpm” 
     The example shown in Table 3 illustrates that some actuators or user input mechanisms on the touch sensitive display  720  can be supplemented with speech dialog. The example in Table 3 illustrates that action signal generator  660  can generate action signals to automatically change a belt speed in the field being harvested. 
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
             
            
               
                 Human: “Johnny, change belt speed to 150 rpm.” 
               
               
                 Operator Interface Controller: “Belt speed set to 150 rpm.” 
               
               
                   
               
            
           
         
       
     
     The example shown in Table 4 illustrates that action signal generator  660  can conduct a dialog with operator  260  to begin and end control of a belt speed. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
             
            
               
                 Human: “Johnny, start marking slug feeding.” 
               
               
                 Operator Interface Controller: “Marking slug feeding.” 
               
               
                 Human: “Johnny, slowly increase belt speed to 250 rpm.” 
               
               
                 Operator Interface Controller: “Belt speed slowly increasing to 250 rpm.” 
               
               
                 Human: “Johnny, stop marking slug feeding.” 
               
               
                 Operator Interface Controller: “Slug feeding marking stopped. Current  
               
               
                 belts speed is 195 rpm.” 
               
               
                   
               
            
           
         
       
     
     The example shown in Table 5 illustrates that action signal generator  160  can generate signals to mark a weed patch in a different way than those shown in Tables 3 and 4. 
     
       
         
           
               
             
               
                 TABLE 5 
               
               
                   
               
             
            
               
                 Human: “Johnny, increase belt speed to 200 rpm for next 100 feet.” 
               
               
                   
               
            
           
         
       
     
     Operator Interface Controller: “Next 100 feet belt speed will be increased to 200 rpm.” Returning again to  FIG.  10   , 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 user interface display  720 . 
     Once the operation is complete, then any desired values that are displayed, or have been displayed on user interface display  720 , 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. 
     It can thus be seen that prior information index map is obtained by an agricultural harvester and shows topographic characteristic values at different geographic locations of a field being harvested. An in-situ sensor on the harvester senses a draper belt speed 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 topographic characteristic in the prior information map and the draper belt speed 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.  12    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.  12   , some items are similar to those shown in  FIG.  2    and those items are similarly numbered.  FIG.  12    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.  12   , agricultural harvester  600  accesses systems through remote server location  502 . 
       FIG.  12    also depicts another example of a remote server architecture.  FIG.  12    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.  13    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.  14 - 15    are examples of handheld or mobile devices. 
       FIG.  13    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.  14    shows one example in which device  16  is a tablet computer  600 . In  FIG.  14   , 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.  15    is similar to  FIG.  14    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.  16    is one example of a computing environment in which elements of  FIG.  2    can be deployed. With reference to  FIG.  16   , 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.  16   . 
     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.  16    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.  16    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.  16   , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG.  16   , 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.  16    illustrates, for example, that remote application programs  885  can reside on remote computer  880 . 
     It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein. 
     Although the subject matter has been described in language specific to structural features 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.