Machine control using graphs with state regions

Abstract

A map available to an agricultural harvester includes a state region with a settings resolver. A plurality of different target actuator settings corresponding to a geographic location of the agricultural work machine are identified. A state region is identified based on the geographic location of the agricultural harvester. Based on the settings resolver corresponding to the identified state region, one of the plurality of different target actuator settings is selected as a resolved target actuator setting, and one of the plurality of different target actuator settings is used to control the agricultural harvester.

Description

TECHNICAL FIELD

[0001] This description relates to agricultural machines, forestry machines, construction machines, and lawn care machines. BACKGROUND

[0002] There are various different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and swathers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] Various different conditions in a field have a variety of deleterious effects on the harvesting operation. As such, when such conditions are encountered during a harvesting operation, an operator can attempt to modify the controls of the harvester.

[0004] The above discussion is provided only as general background information and does not serve as an aid in determining the scope of the subject matter claimed. SUMMARY

[0005] A map obtainable by an agricultural harvester includes a state region with a settings resolver. A plurality of different target actuator settings corresponding to a geographic location of the agricultural work machine are identified. A state region is identified based on the geographic location of the agricultural harvester. One of the plurality of different target actuator settings is selected as a resolved target actuator setting based on the settings resolver corresponding to the identified state region, and the one of the plurality of different target actuator settings is used to control the agricultural harvester.

[0006] This Summary is provided to introduce some 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 to determine the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is a partial schematic view of an example of a combine harvester.

[0008] Figure 2 is a block diagram showing some parts of an agricultural harvester in more detail according to some examples of the present disclosure.

[0009] Figures 3A-3B (Hereinafter referred to collectively as FIG. 3) shows a flowchart illustrating an example of the operation of an agricultural harvester in generating a map.

[0010] Figure 4 is a block diagram showing one example of a prediction model generator and a prediction map generator.

[0011] Figure 5 is a flowchart illustrating one example of operations of an agricultural harvester in receiving maps, detecting characteristics, and generating a functional prediction map for controlling the agricultural harvester during a harvesting operation.

[0012] Figure 6 is a block diagram illustrating one example of a prediction model generator and a prediction map generator.

[0013] Figure 7 is a flowchart illustrating one example of operations of an agricultural harvester in receiving maps and detecting field sensor inputs in generating a functional prediction sensor data map.

[0014] Figure 8 is a block diagram illustrating some examples of field sensors.

[0015] Figure 9 is a block diagram illustrating one example of a control zone generator.

[0016] Figure 10 is a flowchart illustrating one example of operations of the control zone generator illustrated in Figure 9

[0017] Figure 11 is a flowchart illustrating one example of operations of a control system in selecting a target setpoint value to control an agricultural harvester.

[0018] Figure 12 is a block diagram illustrating one example of an operator interface controller.

[0019] Figure 13 is a flowchart illustrating one example of an operator interface controller.

[0020] Figure 14 is a schematic diagram illustrating one example of an operator interface display.

[0021] Figure 15 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment.

[0022] Figures 16-18 shows an example of a mobile device that can be used with an agricultural harvester.

[0023] Figure 19 is a block diagram illustrating one example of a computing environment that can be used with an agricultural harvester and previous figures. DETAILED DESCRIPTION

[0024] ​To facilitate 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 in the described devices, systems, methods, and any further applications 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 can be combined with the features, components, and / or steps described with respect to other examples of the present disclosure.

[0025] This specification relates to using in-field data acquired contemporaneously with an agricultural operation in combination with prior data to generate a prediction map. In some examples, the prediction map can be used to control an agricultural harvesting machine, such as an agricultural harvester. Controlling operation of the agricultural harvester based on different conditions in the field that the agricultural harvester can engage can improve performance of the agricultural harvester. For example, if the crop has ripened, the weeds can still be green, thus increasing the moisture content of the biomass encountered by the agricultural harvester. This problem can be exacerbated when the weed clumps are wet (e.g., shortly after a rain or when the weed clumps contain dew) and before the weeds have a chance to dry. Thus, when the agricultural harvester encounters an area of increased biomass, the operator can adjust operation of the agricultural harvester to maintain a constant feed rate of material through the agricultural harvester. Maintaining a constant feed rate can maintain performance of the agricultural harvester. Performance of the agricultural harvester can be detrimentally affected based on many different criteria. Such different criteria can include variations in biomass, crop condition, terrain, soil properties, and seeding characteristics or other conditions. Thus, it can also be useful to control operation of the agricultural harvester based on other conditions that can be present in the field. For example, by controlling operation of the agricultural harvester based on biomass encountered by the agricultural harvester, crop condition of the crop being harvested, terrain of the field being harvested, soil properties of the soil in the field being harvested, seeding characteristics in the field being harvested, yield in the field being harvested, or other conditions present in the field, performance of the agricultural harvester can be maintained at an acceptable level.

[0026] Further, given a particular operational setting of a particular subsystem of the agricultural harvester, it can be desirable to control other controllable subsystems on the agricultural harvester in a particular manner. For example, if the agricultural harvester is traveling at a first speed, it can be desirable to have the header at a first height, while if the agricultural harvester is operating at a second speed, it can be desirable to have the header at a second height to maintain a feed rate of material through the agricultural harvester at an ideal feed rate.

[0027] Some current systems provide vegetation index maps. Vegetation index maps illustratively map vegetation index values (which can be indicative of vegetation growth) at different geographic locations in a field of interest. One example of a vegetation index includes the normalized difference vegetation index (NDVI). There are many other vegetation indices within the scope of the present disclosure. In some examples, vegetation indices can be derived from sensor readings of one or more electromagnetic radiation bands reflected by vegetation. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0028] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, these maps enable identification and geographic referencing of vegetation in the presence of bare soil, crop residue, or other vegetation, including crops or other weeds.

[0029] In some examples, biomass maps are provided. Biomass maps illustratively plot measurements of biomass at different locations in a field that is being harvested. Biomass maps can be generated from vegetation index values, from historically measured or estimated biomass levels, from images or other sensor readings taken during prior operations in the field, or otherwise. In some examples, biomass can be adjusted by a factor that represents a portion of the total biomass that passes through an agricultural harvester. For corn, this factor is typically about 50%. For moisture in the harvested crop material, this factor is typically 10-30%. In some examples, this factor can represent a portion of weed material or weed seeds.

[0030] In some examples, crop status maps are provided. Crop status can define whether a crop is standing, upright, partially standing, oriented, or otherwise. Crop status maps illustratively plot crop status at different locations in a field that is being harvested. Crop status maps can be generated from aerial images or other images of the field, from images or other sensor readings taken during prior operations in the field or otherwise prior to harvesting.

[0031] In some examples, seeding maps are provided. Seeding maps can map seeding characteristics (e.g., seed location, seed variety, or seed population) to different locations in a field. Seeding maps can be generated during past seeding operations in the field. Seeding maps can be derived from control signals used by a seeder when seeding or from sensors on the seeder that confirm that a seed has been seeded. Seeders can also include geographic position sensors that geolocate seed characteristics in the field.

[0032] In some examples, a soil property map is provided. The soil property map illustratively plots measured values of one or more soil properties (e.g., soil type or soil moisture at different locations in the field in the field being harvested). The soil property map can be generated from vegetation index values, from historically measured or estimated soil properties, from images or other sensor readings taken during prior operations in the field, or otherwise.

[0033] In some examples, other prior information maps are provided. Such prior information maps can include a topography map of the field being harvested, a predicted yield map of the field being harvested, or other prior information maps.

[0034] Accordingly, in some examples, the present discussion is directed to a system that receives a prior information map of a field or a map generated during a prior operation, and also uses in-field sensors to detect in-field sensors indicative of one or more of machine speed and a variable from the output of a feed rate control system. The system generates a model that models the relationship between the prior information values on the prior information map and the output values from the in-field sensors. The model is used to generate a functional prediction speed map, e.g., that predicts target machine speeds at different locations in the field. The functional prediction speed map generated during a harvesting operation can be presented to an operator or other user, or used to automatically control an agricultural harvester during the harvesting operation, or both.

[0035] In some examples, the present discussion is also directed to a system that receives a speed map that maps predicted machine speed values to different geographic locations in a field, and also uses in-field sensors to detect a characteristic. The system generates a model that models the relationship between the values on the speed map and the characteristic values of the in-field sensor output. The model is used to generate a functional prediction map that predicts characteristic values at different locations in the field. The functional prediction data map generated during a harvesting operation can be presented to an operator and used to automatically control an agricultural harvester during the harvesting operation.

[0036] Figure 1is a partial schematic view of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Additionally, while a combine harvester is provided as an example throughout this disclosure, it should be understood that the description applies to other types of harvesters as well, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvester, swathers, or other agricultural work machines. Thus, the present disclosure is intended to encompass the various types of harvesters described and is therefore not limited to combine harvesters. Moreover, the present disclosure is directed to other types of work machines, such as agricultural seeding and spraying machines, construction equipment, forestry equipment, and turf management equipment, in which the generation of a predictive map can be applied. Thus, the present disclosure is intended to encompass these various types of harvesters and other work machines and is therefore not limited to combine harvesters.

[0037] As shown, Figure 1 the agricultural harvester 100 schematically includes an operator cab 101, which can have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes front-end equipment, such as a header 102 and a cutter 104 generally indicated. The agricultural harvester 100 also includes a feederhouse 106, a feeder accelerator 108, and a threshing machine generally indicated at 110. The feederhouse 106 and the feeder accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to a frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move in a direction generally indicated by arrow 109 about the axis 105. Thus, a vertical position of the header 102 above the ground 111 on which the header 102 travels (header height) is controllable by actuating the actuators 107. While not shown in Figure 1 the agricultural harvester 100 can also include one or more actuators that operate to apply a pitch angle, a roll angle, or both, to the header 102 or portions of the header 102. Pitch refers to the angle at which the cutter 104 engages the crop. For example, the pitch angle is increased by controlling the header 102 to point a distal edge 113 of the cutter 104 more toward the ground. The pitch angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 more away from the ground. Roll angle refers to the orientation of the header 102 about a fore-aft longitudinal axis of the agricultural harvester 100.

[0038] The threshing machine 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Additionally, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning apparatus (collectively, cleaning subsystem 118) that includes a cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes a discharge beater 126, a tailings elevator 128, a clean grain elevator 130, and a discharge auger 134 and spout 136. The clean grain elevator moves clean grain into a clean grain tank 132. The agricultural harvester 100 also includes a residue subsystem 138 that can include a chopper 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144 (e.g., wheels or tracks). In some examples, a combine within the scope of the present disclosure can have more than one of any of the above-mentioned subsystems. In some examples, the agricultural harvester 100 can have a left and right cleaning subsystems, a separator, etc., which are not shown in Figure 1 FIG. 1.

[0039] In operation, and as outlined, the agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and gathers the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the agricultural harvester 100 can determine one or more of a height setting, a pitch angle setting, or a roll angle setting of the header 102. For example, the operator inputs one or more settings to a control system that controls the actuator 107 (described in more detail below). The control system can also receive settings from the operator to establish the pitch and roll angles of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the pitch and roll angles of the header 102. The actuator 107 maintains the header 102 at a height above the ground 111 based on the height setting, and, where applicable, at a desired pitch and roll angle. Each of the height, roll, and pitch settings can be implemented independently of the other settings. The control system responds to header errors (e.g., a difference between the height setting and a measured height of the header 104 above the ground 111, and in some examples, pitch and roll angle errors) with a responsiveness determined based on a selected level of sensitivity. If the level of sensitivity is set at a higher level of sensitivity, the control system responds to smaller header position errors and attempts to reduce detected errors more quickly than if the level of sensitivity is at a lower level of sensitivity.

[0040] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material moves through the feeder house 106 toward the feed accelerator 108, which accelerates the crop material into the threshing machine 110. The crop material is threshed by the rotor 112, which rotates the crop against the concaves 114. The threshed crop material is moved by a separator rotor in the separator 116, where a portion of the residue is moved by the unloading beater 126 toward the residue subsystem 138. The portion of the residue that is conveyed to the residue subsystem 138 is chopped by the residue chopper 140 and spread on the field by the spreader 142. In other configurations, the residue is released from the agricultural harvester 100 into a pile. In other examples, the residue subsystem 138 can include a weed seed expeller (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0041] The grain falls to the cleaning subsystem 118. The chaffer 122 separates some of the larger pieces of material from the grain, and the sieve 124 separates some of the finer pieces of material from the clean grain. The clean grain falls onto an auger that moves the grain to the inlet end of the clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upward, depositing the clean grain in the clean grain bin 132. Residue is removed from the cleaning subsystem 118 by the airflow generated by the cleaning fan 120. The cleaning fan 120 directs air up through the sieve and the chaffer along an airflow path. The airflow carries residue in the agricultural harvester 100 rearward toward the residue handling subsystem 138.

[0042] The tailings elevator 128 returns the tailings to the threshing machine 110, where the tailings are re-threshed. Alternatively, the tailings can also be passed through a tailings elevator or another transport device to a separate re-threshing mechanism, where the tailings are also re-threshed.

[0043] Figure 1 It is also shown in one example that the agricultural harvester 100 includes a machine speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward view image capture mechanism 151 that can be in the form of a stereo camera or a monocular camera, and one or more loss sensors 152 disposed in the cleaning subsystem 118.

[0044] The machine speed sensor 146 senses the speed of travel of the agricultural harvester 100 over the ground. The machine speed sensor 146 can sense the speed of travel of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components such as wheels or tracks, drive shafts, axles, or other components. In some cases, a positioning system can be used to sense the speed of travel, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or various other systems or sensors that provide an indication of the speed of travel.

[0045] Loss sensors 152 illustratively provide output signals indicative of the amount of grain loss occurring in the right and left sides of the cleaning subsystem 118. In some examples, the sensors 152 are impact sensors that count grain impacts per unit of time or per unit of travel to provide an indication of grain loss occurring at the cleaning subsystem 118. Impact sensors for the right and left sides of the cleaning subsystem 118 can provide separate signals or combined or aggregated signals. In some examples, the sensors 152 can include a single sensor rather than providing separate sensors for each cleaning subsystem 118.

[0046] Separator loss sensors 148 provide signals indicative of grain loss in the left and right separators (not shown individually in FIG. 1). The separator loss sensors 148 can be associated with the left and right separators and can provide separate grain loss signals or combined or aggregated signals. In some cases, sensing grain loss in the separators can also be done using various different types of sensors. Figure 1

[0047] ​The agricultural harvester 100 can also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 can include one or more of the following sensors: a header height sensor that senses a height of the header 102 above the ground 111; a stability sensor that senses a vibration or bounce (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, produce a windrow, etc.; a cleaning fan speed sensor for sensing a fan 120 speed; a concave gap sensor that senses a gap between the rotor 112 and the concave 114; a threshing rotor speed sensor that senses a rotor speed of the rotor 112; a chaffer gap sensor that senses a size of openings in the chaffer 122; a screen gap sensor that senses a size of openings in the screen 124; a material other than grain (MOG) moisture sensor that senses a moisture level of MOG passing through the agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of the agricultural harvester 100; a machine orientation sensor that senses an orientation of the agricultural harvester 100; and a crop property sensor that senses various different types of crop properties such as crop type, crop moisture, and other crop properties. The crop property sensor can also be configured to sense properties of cut crop material as the crop material is processed by the agricultural harvester 100. For example, in some cases, the crop property sensor can sense: grain quality such as broken grain, MOG levels; grain constituents such as starch and protein; and a grain feed rate as grain travels through the feedhouse 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense a feed rate of biomass through the feedhouse 106, the separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor can also sense a feed rate through the elevator 130 or through other portions of the agricultural harvester 100 as a grain mass flow rate, or provide other output signals indicative of other sensed variables.

[0048] Before describing how the agricultural harvester 100 generates a functional prediction speed map and uses the functional prediction speed map for control, a brief description of some items on the agricultural harvester 100 and their operation will first be described. Figure 2and the description of FIG. 3 describes receiving a general type of prior information map and combining information from the prior information map with georeferenced sensor signals generated by on-site sensors, where the sensor signals are indicative of characteristics, e.g., one or more characteristics of the field itself, one or more crop characteristics of the material being harvested, such as a crop or grain present in the field, one or more environmental characteristics of the environment of the agricultural harvester, or one or more characteristics of the agricultural harvester. Characteristics of the field can include, but are not limited to, characteristics of the field such as slope, weed intensity, weed type, soil moisture, surface quality. Crop characteristics can include one or more crop properties such as crop height, crop moisture, crop density, crop condition, as well as characteristics of grain properties such as grain moisture, grain size, and grain test weight. Environmental characteristics can include weather characteristics and the presence of standing water. Characteristics of the agricultural harvester can include characteristics indicative of machine settings or operator input or characteristics of machine operation, e.g., machine speed, outputs from different controllers, machine performance such as loss levels, job quality, fuel consumption, and power utilization. Relationships between characteristic values obtained from the on-site sensor signals or from values derived by the on-site sensors and speed map values are identified, and the relationships are used to generate new functional prediction maps. The functional prediction maps predict values at different geographic locations in the field, and one or more of the values can be used to control one or more subsystems of the machine, such as the agricultural harvester. In some cases, the functional prediction maps can be presented to a user, such as an operator of the agricultural work machine, which can be the agricultural harvester. The functional prediction maps can be presented to the user in a visual manner, such as by a display, in a tactile manner, or in an audible manner. The user can interact with the functional prediction maps to perform editing operations and other user interface operations. In some cases, the functional prediction maps can be used to control the agricultural work machine, such as the agricultural harvester, presented to the operator or other user, and presented to the operator or user for one or more of operator or user interaction.

[0049] In reference to Figure 2 and the description of FIG. 3 describes a general method, reference is made to Figure 4 and Figure 5 a more specific method for generating a functional prediction speed map is described, which can be presented to an operator or user, or used to control the agricultural harvester 100, or both. Then, the use of the speed map to control the agricultural harvester 100 while harvesting is described. Again, while this discussion is directed to agricultural harvesters, and in particular combine harvesters, the scope of the disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

[0050] Figure 2is a block diagram showing some portions of an example agricultural harvester 100. Figure 2 The agricultural harvester 100 is shown to schematically include one or more processors or servers 201, a data storage device 202, a geo-location sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more characteristics of the field while the harvesting operation is in progress. The field sensors 208 generate values corresponding to the sensed characteristics. The agricultural harvester 100 also includes a predictive model or relationship generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive map generator 212, a control zone generator 213, a control system 214, one or more controllable systems 216, and an operator interface mechanism 218. The agricultural harvester 100 can also include a variety of other agricultural harvester functions 220. The field sensors 208 include, for example, on-board sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of the field during the course of the agricultural operation. The predictive model generator 210 schematically includes a priori information variable to field variable model generator 228, and the predictive model generator 210 can include other items 230. The control system 214 includes a communication system controller 229, an operator interface controller 231, a settings controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor belt controller 240, a table deck position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and the system 214 can include other items 246. The controllable systems 216 include machine and header actuators 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleaning subsystem 254, and the subsystems 216 can include a variety of other subsystems 256.

[0051] Figure 2 The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, the a priori information map 258 includes, for example, a vegetation index map, a biomass map, a crop condition map, a topography map, a soil property map, a planting map, or a map from a prior operation. However, the a priori information map 258 can also encompass other types of data obtained prior to the harvesting operation or maps from a prior operation. Figure 2The diagram also illustrates that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include joysticks, control levers, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuable elements (such as icons, buttons, etc.) on a user interface display device, microphones and speakers (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanism 218 using touch gestures. The examples described above are provided as illustrative examples and are not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may be used, and other types of operator interface mechanisms are within the scope of this disclosure.

[0052] The prior information map 258 can be downloaded to the agricultural harvester 100 and stored in the data storage device 202 using the communication system 206 or other means. In some examples, the communication system 206 may be a cellular communication system, a system for communication over a wide area network or a local area network, a system for communication over a near-field communication network, or a communication system configured to communicate over any one or a combination of various other networks. The communication system 206 may also include systems that facilitate the downloading or transfer of information to or from a secure digital (SD) card or a universal serial bus (USB) card, or both.

[0053] The geolocation sensor 204 schematically senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a GNSS receiver that receives signals from a Global Navigation Satellite System (GNSS) satellite transmitter. The geolocation sensor 204 may also include a real-time kinematic (RTK) component configured to improve the accuracy of the position data derived from the GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0054] The field sensor 208 can be referenced above. Figure 1 Any of the sensors described. Field sensor 208 includes onboard sensor 222 mounted on the onboard agricultural harvester 100. Such a sensor may include, for example, relative to… Figure 1Any of the sensors discussed above, the perception sensors (e.g., forward looking monocular or stereo camera systems and image processing systems), image sensors inside the agricultural harvester 100 such as one or more clean grain cameras mounted to identify material exiting the agricultural harvester 100 through the residue subsystem or from the clean-up subsystem. The field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors mounted on the harvester or data acquired by any sensor in which the data is detected during the harvesting operation. More examples of field sensors 208 are described below with respect to Figure 8 More examples of field sensors 208 are described below.

[0055] The prediction model generator 210 generates a model that indicates a relationship between values sensed by the field sensors 208 and a metric mapped to the field by the prior information map 258. For example, if the prior information map 258 maps vegetation index values to different locations in the field and the field sensors 208 sense values indicative of machine speed, the prior information variable to field variable model generator 228 generates a predicted speed model that models the relationship between the vegetation index values and the machine speed values. The predicted speed model can also be generated based on vegetation index values from the prior information map 258 and multiple field data values generated by the field sensors 208. The prediction map generator 212 then generates a functional predicted speed map that predicts target machine speeds sensed by the field sensors 208 at different locations in the field based on the prior information map 258 using the predicted power model generated by the prediction model generator 210.

[0056] In some examples, the type of values in the function prediction map 263 can be the same as the type of field data sensed by the field sensors 208. In some cases, the type of values in the function prediction map 263 can have different units than the data sensed by the field sensors 208. In some examples, the type of values in the function prediction map 263 can be different than the type of data sensed by the field sensors 208, but related to the type of data sensed by the field sensors 208. For example, in some examples, the type of data sensed by the field sensors 208 can dictate the type of values in the function prediction map 263. In some examples, the type of data in the function prediction map 263 can be different than the type of data in the prior information map 258. In some cases, the type of data in the function prediction map 263 can have different units than the data in the prior information map 258. In some examples, the type of data in the function prediction map 263 can be different than the type of data in the prior information map 258, but related to the type of data in the prior information map 258. For example, in some examples, the type of data in the prior information map 258 can dictate the type of data in the function prediction map 263. In some examples, the type of data in the function prediction map 263 is different than one or both of the type of field data sensed by the field sensors 208 and the type of data in the prior information map 258. In some examples, the type of data in the function prediction map 263 is the same as one or both of the type of field data sensed by the field sensors 208 and the type of data in the prior information map 258. In some examples, the type of data in the function prediction map 263 is the same as one of the type of field data sensed by the field sensors 208 or the type of data in the prior information map 258, and different than the other.

[0057] Continuing the previous example, in which the prior information map 258 is a vegetation index map and the field sensors 208 sense values indicative of machine speed, the prediction map generator 212 can generate a function prediction map 263 predicting target machine speed at different locations in the field using the vegetation index values in the prior information map 258 and the model generated by the prediction model generator 210. The prediction map generator 212 therefore outputs a prediction map 264.

[0058] As Figure 2As shown, the prediction map 264 predicts values of a sensed characteristic (sensed by the field sensors 208) or a characteristic related to the sensed characteristic at various locations on the field based on the prior information values at the locations in the prior information map 258 and using a prediction model. For example, if the prediction model generator 210 has generated a prediction model that indicates a relationship between vegetation index values and machine speed usage, the prediction map generator 212 generates the prediction map 264 that predicts target machine speed values at different locations on the field given vegetation index values at the different locations on the field. The vegetation index values at the locations obtained from the vegetation index map and the relationship between vegetation index values and machine speed usage obtained from the prediction model are used to generate the prediction map 264.

[0059] Some variations in the type of data mapped in the prior information map 258, the type of data sensed by the field sensors 208, and the type of data predicted on the prediction map 264 will now be described.

[0060] In some examples, the type of data in the prior information map 258 is different from the type of data sensed by the field sensors 208, but the type of data in the prediction map 264 is the same as the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be yield. The prediction map 264 can then be a predicted yield map that maps predicted yield values to different geographic locations in the field. In another example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop height. The prediction map 264 can then be a predicted crop height map that maps predicted crop height values to different geographic locations in the field.

[0061] In addition, in some examples, the type of data in the prior information map 258 is different from the type of data sensed by the field sensors 208, and the type of data in the prediction map 264 is different from both the type of data in the prior information map 258 and the type of data sensed by the field sensors 208. For example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be crop height. The prediction map 264 can then be a predicted biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the prior information map 258 can be a vegetation index map, and the variable sensed by the field sensors 208 can be yield. The prediction map 264 can then be a predicted speed map that maps predicted harvester speed values to different geographic locations in the field.

[0062] In some examples, the prior information map 258 comes from previously passing through the field during a prior operation, and the data type is different from the data type sensed by the in-field sensors 208, but the data type in the prediction map 264 is the same as the data type sensed by the in-field sensors 208. For example, the prior information map 258 can be a seed population map generated during planting, and the variable sensed by the in-field sensors 208 can be stalk size. The prediction map 264 can then be a predicted stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, the prior information map 258 can be a seeding mix map, and the variable sensed by the in-field sensors 208 can be crop status, such as standing crop or lodged crop. The prediction map 264 can then be a predicted crop status map that maps predicted crop status values to different geographic locations in the field.

[0063] In some examples, the prior information map 258 comes from previously passing through the field during a prior operation, and the data type is the same as the data type sensed by the in-field sensors 208, and the data type in the prediction map 264 is also the same as the data type sensed by the in-field sensors 208. For example, the prior information map 258 can be a yield map generated during the previous year, and the variable sensed by the in-field sensors 208 can be yield. The prediction map 264 can then be a predicted yield map that maps predicted yield values to different geographic locations in the field. In such an example, the prediction model generator 210 can use relative yield differences in the georeferenced prior information map 258 from the previous year to generate a prediction model that models a relationship between relative yield differences on the prior information map 258 and yield values sensed by the in-field sensors 208 during the current harvesting operation. The prediction model is then used by the prediction map generator 210 to generate the predicted yield map.

[0064] In another example, the prior information map 258 can be a weed intensity map generated during a prior operation (e.g., from a sprayer), and the variable sensed by the field sensor 208 can be weed intensity. The prediction map 264 can then be a predicted weed intensity map that maps predicted weed intensity values to different geographic locations in the field. In such an example, a map of weed intensity at the time of spraying is recorded in geographic reference and provided to the agricultural harvester 100 as the prior information map 258 of weed intensity. The field sensor 208 can detect the weed intensity at the geographic location in the field, and then the prediction model generator 210 can establish a prediction model that models the relationship between weed intensity at the time of harvesting and weed intensity at the time of spraying. This is because the sprayer will have an impact on the weed intensity at the time of spraying, but the weeds can still reappear in similar areas at the time of harvesting. However, based on the time of harvesting, weather, weed type, etc., the weed areas at the time of harvesting can have different intensities.

[0065] In some examples, the prediction map 264 can be provided to a control zone generator 213. The control zone generator 213 groups a plurality of adjacent portions of a region associated with data values of the prediction map 264 into one or more control zones based on the data values. The control zones can include two or more contiguous portions of a region, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, the response time to change a setting of a controllable subsystem 216 can not be satisfactory to respond to changes in values contained in a map, such as the prediction map 264. In this case, the control zone generator 213 parses the map and identifies control zones of a defined size to accommodate the response time of the controllable subsystem 216. In another example, the size of the control zones can be determined to reduce wear caused by excessive actuator movement resulting from continuous adjustment. In some examples, there can be different control zone groups for each controllable subsystem 216 or group of controllable subsystems 216. The control zones can be added to the prediction map 264 to obtain a prediction control zone map 265. The prediction control zone map 265 can thus be similar to the prediction map 264 except that the prediction control zone map 265 includes control zone information defining the control zones. Thus, as described herein, the functional prediction map 263 can or can not include control zones. Both the prediction map 264 and the prediction control zone map 265 are functional prediction maps 263. In one example, the functional prediction map 263 does not include control zones, such as the prediction map 264. In another example, the functional prediction map 263 does include control zones, such as the prediction control zone map 265. In some examples, if an inter-plant production system is implemented, multiple crops can be present in the field at the same time. In this case, the prediction map generator 212 and the control zone generator 213 are able to identify the location and characteristics of two or more crops and then generate the prediction map 264 and the prediction control zone map 265 accordingly.

[0066] It should also be appreciated that the control zone generator 213 can cluster values to generate control zones and the control zones can be added to the prediction control zone map 265 or a separate map showing only the generated control zones. In some examples, the control zones can be used to control or calibrate the agricultural harvester 100 or both. In other examples, the control zones can be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in other examples, the control zones can be presented to the operator 260 or another user or stored for later use.

[0067] The prediction map 264 or the prediction control zone map 265 or both are provided to the control system 214, which generates control signals based on the prediction map 264 or the prediction control zone map 265 or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the prediction map 264 or the prediction control zone map 265 or control signals based on the prediction map 264 or the prediction control zone map 265 to other agricultural harvester machines that are harvesting in the same field. In some examples, the communication system controller 229 controls the communication system 206 to send the prediction map 264, the prediction control zone map 265, or both to other remote systems.

[0068] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanisms 218. The operator interface controller 231 is also operable to present the prediction map 264 or the prediction control zone map 265 or other information derived from or based on the prediction map 264, the prediction control zone map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanisms to display one or both of the prediction map 264 and the prediction control zone map 265 to the operator 260. The controller 231 can generate operator actuatable mechanisms, which are shown and can be actuated by the operator to interact with the displayed maps. The operator can edit the maps, for example, by correcting the displayed weed types on the maps based on the operator’s observations. The settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based on the prediction map 264, the prediction control zone map 265, or both. For example, the settings controller 232 can generate control signals to control the machine and header actuators 248. In one example, the settings controller 232 can control a sensitivity setting that controls the responsiveness of the control system 214 in controlling the position of the header (e.g., the height, pitch, or roll) in response to a header position error to conform to a header position setting (e.g., a header height setting, a header pitch setting, or a header roll setting). In response to the generated control signals, the machine and header actuators 248 operate to control one or more of, for example, a sieve and chaffer setting, a concave gap, a rotor setting, a clean grain fan speed setting, a header height, a header function, a reel speed, a reel position, a belt conveyor function such as a belt conveyor belt speed where the agricultural harvester 100 is coupled to a belt conveyor header, a corn header function, an in-cab distribution control, and other actuators 248 that affect other functions of the agricultural harvester 100. In some examples, the machine and header actuators 248 can be controlled to adjust a rear axle speed (also referred to as a header drive speed). For example, in the example of a corn header, the rear axle speed can be adjusted to control the speed of one or more of a corn stalk roller, a pickup chain, and an auger conveyor on the corn header. In some examples, the machine and header actuators 248 can include a rotatable output mechanism such as a drive shaft, the output of which can be controlled to control the rear axle speed. In some examples, the machine and header actuators 248 can be controlled to adjust the speed of the reel 164. The path planning controller 234 illustratively generates control signals to control the steering subsystem 252 to steer the agricultural harvester 100 according to a desired path.The path planning controller 234 can control the path planning system to generate a route for the agricultural harvester 100 and can control the propulsion subsystem 250 and the steering subsystem 252 to steer the agricultural harvester 100 along the route. The feed rate controller 236 can receive various different inputs indicative of the feed rate of material through the agricultural harvester 100 and can be able to control various subsystems, such as the propulsion subsystem 250 and the machine actuators 248, to control the feed rate based on the prediction map 264 or the prediction control zone map 265 or both. For example, as the agricultural harvester 100 approaches a patch of weeds with a strength value above a selected threshold, the feed rate controller 236 can generate a control signal to control the propulsion subsystem 252 to reduce the speed of the agricultural harvester 100 to maintain a constant feed rate of biomass through the agricultural harvester 100. The header and reel controller 238 can generate a control signal to control the header or reel or other header functions, such as the position of the header or the speed of the reel. The belt conveyor belt controller 240 can generate a control signal to control the belt conveyor belt or other belt conveyor functions, such as the belt conveyor belt speed, based on the prediction map 264, the prediction control zone map 265, or both. The deck plate position controller 242 can generate a control signal to control the position of the deck plate included on the header based on the prediction map 264 or the prediction control zone map 265 or both, and the residue system controller 244 can generate a control signal to control the residue subsystem 138 based on the prediction map 264 or the prediction control zone map 265 or both. The machine cleaning controller 245 can generate a control signal to control the machine cleaning subsystem 254. For example, based on the different types of seeds or weeds passing through the agricultural harvester 100, a particular type of machine cleaning operation or the frequency at which the cleaning operation is performed can be controlled. Other controllers included on the agricultural harvester 100 can also control other subsystems based on the prediction map 264 or the prediction control zone map 265 or both.

[0069] Figure 3A and Figure 3B FIG. 3 (collectively referred to herein as FIG. 3) illustrates a flowchart that illustrates one example of the operation of the agricultural harvester 100 in generating the prediction map 264 and the prediction control zone map 265 based on the prior information map 258.

[0070] At 280, the agricultural harvester 100 receives the prior information map 258. Examples of the prior information map 258 or receiving the prior information map 258 are discussed with respect to blocks 281, 282, 284, and 286. As discussed above, as shown in block 282, the prior information map 258 maps values of a variable corresponding to a first characteristic to different locations in the field. As shown in block 281, receiving the prior information map 258 can include selecting one or more of a plurality of possible prior information maps available. For example, one prior information map can be a vegetation index map generated from aerial images. Another prior information map can be a map generated during a previous pass through the field, which can be performed by a different machine (such as a sprayer or a planter or other machine) performing a previous operation in the field. The process of selecting one or more prior information maps can be manual, semi-automatic, or automatic. The prior information map 258 is based on data collected prior to the current harvesting operation. This is indicated by block 284. For example, the data can be collected based on aerial images taken during a previous year or early in the current growing season or other time. The data can be based on data detected in a manner other than using aerial images. For example, the agricultural harvester 100 can be equipped with sensors, such as internal optical sensors, that identify weed seeds or other types of material exiting the agricultural harvester 100. The weed seeds or other data detected by the sensors during harvesting in the previous year can be used as data for generating the prior information map 258. The sensed weed data or other data can be combined with other data to generate the prior information map 258. For example, based on the number of weed seeds exiting the agricultural harvester 100 at different locations and based on other factors (e.g., whether the seeds were dropped by a spreader or fell in a pile; weather conditions (e.g., wind) at the time the seeds fell or were spread; drainage conditions that can move the seeds around the field; or other information), the locations of these weed seeds can be predicted such that the prior information map 258 maps the predicted seed locations in the field. The data of the prior information map 258 can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data storage device 202. The data of the prior information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is represented by block 286 in the flowchart of FIG. 3. In some examples, the prior information map 258 can be received by the communication system 206.

[0071] At the start of the harvesting operation, the field sensors 208 generate sensor signals indicative of one or more field data values of a characteristic such as a speed characteristic, as shown in block 288. Examples of field sensors 288 are discussed with respect to blocks 222, 290, and 226. As explained above, the field sensors 208 include on-board sensors 222, remote field sensors 224 such as UAV-based sensors that gather field data on each flight (as shown in block 290), or other types of field sensors specified by the field sensors 226. In some examples, data from the on-board sensors is georeferenced using position, heading, or speed data from the geolocation sensors 204.

[0072] The predictive model generator 210 controls the prior information variables to the field variable model generator 228 to generate a model that models the relationship between the mapped values contained in the prior information map 258 and the field values sensed by the field sensors 208, as shown in block 292. The characteristic or data type represented by the mapped values in the prior information map 258 and the field values sensed by the field sensors 208 can be the same characteristic or data type or different characteristics or data types.

[0073] The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212. The predictive map generator 212 uses the predictive model and the prior information map 258 to generate a predictive map 264 that predicts values of the characteristic sensed by the field sensors 208 at different geographic locations in the field being harvested, or different characteristics related to the characteristic sensed by the field sensors 208, as shown in block 294.

[0074] It should be noted that in some examples, the prior information map 258 can include two or more different maps or two or more different layers of a single map. Each layer can represent a different data type than another layer’s data type, or the layers can have the same data type obtained at different times. Each map of the two or more different maps or each layer of the two or more different layers of a map maps different types of variables to geographic locations in the field. In such examples, the prediction model generator 210 generates a prediction model that models relationships between the on-site data and each of the different variables mapped by the two or more different maps or two or more different layers of a map. Similarly, the on-site sensors 208 can include two or more sensors, each of which senses a different type of variable. Thus, the prediction model generator 210 generates a prediction model that models relationships between each type of variable mapped by the prior information map 258 and each type of variable sensed by the on-site sensors 208. The prediction map generator 212 can use the prediction model and each of the maps or layers of the map in the prior information map 258 to generate a functional prediction map 263 that predicts values of each sensed characteristic (or characteristics related to the sensed characteristics) sensed by the on-site sensors 208 at different locations in the field being harvested.

[0075] The prediction map generator 212 configures the prediction map 264 such that the prediction map 264 is operable (or consumable) by the control system 214. The prediction map generator 212 can provide the prediction map 264 to the control system 214 or to the control zone generator 213 or to both. Some examples of different ways in which the prediction map 264 can be configured or output are described with respect to blocks 296, 295, 299, and 297. For example, the prediction map generator 212 configures the prediction map 264 such that the prediction map 264 includes values that can be read by the control system 214 and used as a basis for generating control signals for one or more of the different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0076] The control region generator 213 can divide the prediction map 264 into control regions based on the values on the prediction map 264. Contiguously geolocated values within a threshold of each other can be grouped into control regions. The threshold can be a default threshold, or the threshold can be set based on operator input, based on input from an automation system, or based on other criteria. The size of the regions can be based on responsiveness of the control system 214, controllable subsystems 216, based on wear considerations, or based on other criteria, as indicated by block 295. The prediction map generator 212 configures the prediction map 264 for presentation to an operator or other user. The control region generator 213 can configure the prediction control region 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 prediction map 264 or the prediction control region map 265 or both can include one or more of the predicted values on the prediction map 264 related to geographic locations, control regions on the prediction control region map 265 related to geographic locations, and the set values or control parameters used based on the predicted values on the map 264 or the regions on the prediction control region map 265. In another example, the presentation can include more abstract information or more detailed information. The presentation can also include a confidence level that indicates the accuracy of the predicted values on the prediction map 264 or the regions on the prediction control region map 265 to match measured values that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Additionally, where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For example, there can be a hierarchy of individuals that are authorized to view and change the maps and other presented information. As an example, an onboard display device can display the map locally on the machine in near real time, or the map can also be generated at one or more remote locations, or both. In some examples, each physical display device at each location can be associated with a person or user permission level. The user permission level can be used to determine which display indicia are visible on the physical display device, and which values the corresponding person can change. For example, a local operator of the machine 100 can not be able to see the information corresponding to the prediction map 264 or make any changes to the machine operation. However, a supervisor such as a supervisor at a remote location can be able to see the prediction map 264 on the display, but be prevented from making any changes. A manager that can be at a separate remote location can be able to see all elements on the prediction map 264 and also be able to change the prediction map 264. In some cases, the prediction map 264 that can be accessed and changed by a manager located remotely can be used for machine control. This is one example of an authorization hierarchy that can be implemented. The prediction map 264 or the prediction control region map 265 or both can also be configured in other ways, as indicated by block 297.

[0077] At block 298, inputs from the geo-location sensor 204 and other field sensors 208 are received by the control system. In particular, at block 300, the control system 214 detects input from the geo-location sensor 204 identifying the geo-location of the agricultural harvester 100. Block 302 represents sensor input indicating the trajectory or heading of the agricultural harvester 100 being received by the control system 214, and block 304 represents the speed of the agricultural harvester 100 being received by the control system 214. Block 306 represents other information being received by the control system 214 from the various field sensors 208.

[0078] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the prediction map 264 or the prediction control zone map 265 or both, as well as the inputs from the geo-location sensor 204 and any other field sensors 208. At block 310, the 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 can vary based on one or more different factors. For example, the control signals that are generated and the controllable subsystems 216 that are controlled can be based on the type of prediction map 264 or prediction 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 delays in the flow of the crop through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0079] As an example, the generated prediction map 264 in the form of a predicted speed map can be used to control one or more subsystems 216. For example, the predicted speed map can include speed values that geographically reference locations within the field being harvested. The speed values from the predicted speed map can be extracted and used to control one or more of the controllable subsystems 216, e.g., the propulsion subsystem 250. By controlling the propulsion subsystem 250, the feed rate of material moving through the agricultural harvester 100 can be controlled. In other examples, the header or other machine actuators 248 can be controlled, e.g., to control a rear axle speed, a belt conveyor belt speed, or a reel speed. Similarly, the header or other machine actuators 248 can be controlled to control a position of the header, e.g., a header height, a header pitch, or a header roll. For example, the header height can be controlled to take up more or less material, and thus, the header height can also be controlled to control the feed rate of material through the agricultural harvester 100. In other examples, if the prediction map 264 maps weed heights with respect to locations in the field, then control of the header height can be implemented. For example, if values present in the predicted weed map indicate that one or more areas have a first amount of weed height, then the header and reel controller 238 can control the header height such that the header is positioned above the first amount of weed height when performing a harvesting operation in the one or more areas having the first amount of weed height. Thus, the header and reel controller 238 can be controlled using geographically referenced values present in the predicted weed map to position the header to a height above the predicted height values of weeds obtained from the predicted weed map. Further, the header height can be automatically changed by the header and reel controller 238 as the agricultural harvester 100 travels through the field using geographically referenced values obtained from the predicted weed map. The foregoing examples relating to using weed height and strength from a predicted weed map are provided by way of example only. Thus, a variety of other control signals can be generated using values obtained from a predicted weed map or other types of prediction maps to control one or more of the controllable subsystems 216.

[0080] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting has not been completed, processing proceeds to block 314, where the reading of field sensor data from the geo-position sensor 204 and the field sensors 208 (and possibly other sensors) continues.

[0081] In some examples, at block 316, the agricultural harvester 100 can also detect a learning trigger criterion to perform machine learning on one or more of the prediction map 264, the prediction control zone map 265, the models generated by the prediction model generator 210, the zones generated by the control zone generator 213, the one or more control algorithms implemented by the controllers in the control system 214, and other triggers for learning.

[0082] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322, and 324. For example, in some examples, triggering learning can include re-creating relationships used to generate prediction models when a threshold amount of field sensor data is obtained from the field sensors 208. In such examples, an amount of field sensor data received from the field sensors 208 that exceeds a threshold triggers or causes the prediction model generator 210 to generate a new prediction model used by the prediction map generator 212. Thus, as the agricultural harvester 100 continues harvesting operations, receiving a threshold amount of field sensor data from the field sensors 208 triggers creating new relationships represented by the prediction models generated by the prediction model generator 210. Further, the new prediction models can be used to re-generate a new prediction map 264, a prediction control zone map 265, or both. Block 318 represents detecting a threshold amount of field sensor data used to trigger creation of a new prediction model.

[0083] In other examples, the learning trigger criteria can be based on a degree of change in the field sensor data from the field sensors 208, such as a degree of change over time or compared to previous values. For example, if the change within the field sensor data (or the relationship between the field sensor data and information in the priori information map 258) is within a selected range, or less than a defined amount, or below a threshold, then a new prediction model is not generated by the prediction model generator 210. As a result, the prediction map generator 212 does not generate a new prediction map 264, a prediction control zone map 265, or both. However, if the change within the field sensor data is outside of the selected range, greater than the defined amount, or above the threshold, then the prediction model generator 210 generates a new prediction model using all or a portion of the newly received field sensor data used by the prediction map generator 212 to generate a new prediction map 264. At block 320, the degree of change in the field sensor data, such as a size of an amount of data outside of a selected range or a size of a change in a relationship between the field sensor data and information in the priori information map 258, can be used as a trigger to cause generation of a new prediction model and prediction map. Continuing the above-described example, the threshold, range, and defined amount can be set to default values, set by an operator or user through interaction with a user interface, set by an automated system, or otherwise set.

[0084] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different priori information map (different than the initially selected priori information map 258), then the switch to the different priori information map can trigger relearning by the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other. In another example, a transition of the agricultural harvester 100 to a different terrain or to a different control zone can also be used as a learning trigger criteria.

[0085] In some cases, the operator 260 can also edit the prediction map 264 or the prediction control zone map 265, or both. The editing can change values on the prediction map 264, change sizes, shapes, locations, or existence of control zones on the prediction control zone map 265, or both. Block 321 shows that the edited information can be used as a learning trigger criteria.

[0086] In some cases, the operator 260 can also observe that the automatic control of the controllable subsystem is not as desired by the operator. In such cases, the operator 260 can provide a manual adjustment to the controllable subsystem, which reflects that the operator 260 desires the controllable subsystem to operate differently than commanded by the control system 214. Accordingly, the manual change to the setting by the operator 260 can result in one or more of the following: the relearning of the model by the predictive model generator 210, the regeneration of the map 264 by the predictive map generator 212, the regeneration of one or more control regions on the predictive control region map 265 by the control region generator 213, and the relearning of the control algorithm by the control system 214 or performing machine learning on one or more of the controller components 232-246 in the control system 214. Block 324 represents the use of other trigger learning criteria.

[0087] In other examples, the relearning can be performed periodically or intermittently, for example based on a selected time interval, such as a discrete time interval or a variable time interval, as indicated by block 326.

[0088] If the relearning is triggered (whether based on a learning trigger criterion or based on the passage of a time interval, as indicated by block 326), one or more of the predictive model generator 210, the predictive map generator 212, the control region generator 213, and the control system 214 perform machine learning to generate a new predictive model, a new predictive map, a new control region, and a new control algorithm, respectively, based on the learning trigger criterion. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data collected since the last time the learning operation was performed. The performance of the relearning is indicated by block 328.

[0089] If the harvesting operation has been completed, the operation moves from block 312 to block 330, in which one or more of the predictive map 264, the predictive control region map 265, and the predictive model generated by the predictive model generator 210 are stored. The predictive map 264, the predictive control region map 265, and the predictive model can be stored locally on the data storage device 202 or transmitted to a remote system for later use using the communication system 206.

[0090] It will be noted that while some examples herein describe the predictive model generator 210 and the predictive map generator 212 receiving a priori information maps when generating the predictive model and the functional predictive map, respectively, in other examples, the predictive model generator 210 and the predictive map generator 212 can receive other types of maps when generating the predictive model and the functional predictive map, respectively, including predictive maps, such as the functional predictive map generated during the harvesting operation.

[0091] Figure 4 is Figure 1 a block diagram of a portion of the agricultural harvester 100 shown in FIG. 1. In particular, Figure 4 An example of the prediction model generator 210 and the prediction map generator 212 is shown in particular further detail. FIG. 4 also illustrates the flow of information between the different components shown. The prediction model generator 210 receives a prior information map 258, which can be the vegetation index map 332, the predicted yield map 333, the biomass map 335, the crop status map 337, the terrain map 339, the soil property map 341, or the planting map 343 as the prior information map.

[0092] The prediction model generator 210 also receives a geographic location 334, or an indication of a geographic location, from the geographic location sensor 204. The on-site sensors 208 schematically include the machine speed sensor 146 or a sensor 336 that senses an output from the feed rate controller 236 and a processing system 338. The processing system 338 processes sensor data generated from the machine speed sensor 146 or from the sensor 336 or from both to generate processed data, some examples of which are described below.

[0093] In some examples, the sensor 336 can be a sensor that generates a signal indicative of a control output from the feed rate controller 236. The control signal can be a speed control signal or other control signal that is applied to the controllable subsystem 216 to control the feed rate of material through the agricultural harvester 100. The processing system 338 processes the signal obtained by the sensor 336 to generate processed data 340 that identifies the speed of the agricultural harvester 100. The processed data 340 can include a location of the agricultural harvester 100 that corresponds to the speed of the agricultural harvester 100.

[0094] In some examples, raw or processed data from the on-site sensor 208(s) can be presented to an operator 260 via the operator interface mechanism 218. The operator 260 can be located on the agricultural harvester 100 or at a remote location.

[0095] The present discussion is directed to an example in which the on-site sensor 208 is the machine speed sensor 146. It should be understood that this is merely an example, and the above-described sensor and other examples of the on-site sensor 208 from which a machine speed can be derived are also contemplated herein. As Figure 4As shown, the example prediction model generator 210 includes a vegetation index (VI) value to speed model generator 342, a biomass to speed model generator 344, a terrain to speed model generator 345, a yield to speed model generator 347, a crop state to speed model generator 349, a soil property to speed model generator 351, and a seeding characteristics to speed model generator 346. In other examples, the prediction model generator 210 can include additional components, fewer components, or different components than those shown in the example of FIG. 3. Thus, in some examples, the prediction model generator 210 can also include other items 348, which can include other types of prediction model generators to generate other types of power models. Figure 4

[0096] The model generator 342 identifies a relationship between the machine speed detected in the processed data 340 at a geographic location corresponding to the location at which the processed data 340 was obtained and the vegetation index value from the vegetation index map 332 corresponding to the same location in the field at which the weed characteristic was detected. Based on this relationship established by the model generator 342, the model generator 342 generates a prediction speed model. The prediction map generator 352 uses the prediction speed model to predict a target machine speed at a different location in the field based on the georeferenced vegetation index value in the vegetation index map 332 contained at the same location in the field.

[0097] The model generator 344 identifies a relationship between the machine speed represented in the processed data 340 at a geographic location corresponding to the processed data 340 and the biomass value at the same geographic location. Further, the biomass value is a georeferenced value contained in the biomass map 335. The model generator 344 then generates a prediction speed model that the speed map generator 354 uses to predict a target machine speed at a location in the field based on the biomass value of the location in the field.

[0098] The model generator 346 identifies a relationship between the machine speed identified by the processed data 340 at a particular location in the field and the seeding characteristics value from the seeding characteristics map 343 at the same location. The model generator 346 generates a prediction speed model that the speed map generator 356 uses to predict a target machine speed at the particular location in the field based on the seeding characteristics value of the particular location in the field.

[0099] ​In view of the above, the prediction model generator 210 is operable to produce a plurality of predicted speed models, such as one or more of the predicted speed models generated by the model generators 342, 344, 345, 346, 347, 348, and 351. In another example, two or more of the above-described predicted speed models can be combined into a single predicted speed model that predicts target machine speed at different locations in the field based on two or more of the vegetation index values, the biomass values, the terrain, the yield, the seeding characteristics, the crop state, or the soil properties. Any one of these speed models or combinations thereof are collectively represented by the prediction model 350 in Figure 4

[0100] The prediction model 350 is provided to the prediction map generator 212. In the example of FIG. 2, the prediction map generator 212 includes a speed map generator 352. In other examples, the prediction map generator 212 can include additional, fewer, or different map generators. Thus, in some examples, the prediction map generator 212 can include other items 358, which can include other types of map generators to generate speed maps. The speed map generator 352 receives the prediction model 350, which predicts target machine speed based on values from one or more of the prior information maps 258 and the one or more prior information maps 258, and generates a prediction map of target machine speed at different locations in the field. Figure 4

[0101] The prediction map generator 212 outputs one or more functional prediction speed maps 360 that predict one or more target machine speeds. The functional prediction speed maps 360 predict target machine speed at different locations in the field. The functional prediction speed maps 360 can be provided to the control zone generator 213, the control system 214, or both. The control zone generator 213 generates control zones and merges the control zones into a functional prediction map (i.e., a prediction map) to produce a predicted control zone map 265. One or both of the prediction map 264 and the predicted control zone map 265 can be provided to the control system 214, which generates control signals to control one or more of the controllable subsystems 216 (e.g., the propulsion subsystem 250) based on the prediction map 264, the predicted control zone 265, or both.

[0102] Figure 5 ​​is a flowchart of examples of the operations of the prediction model generator 210 and the prediction map generator 212 in generating the prediction model 350 and the functional prediction speed map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive a prior information map 258. The prior information map 258 can be any one of the maps 332, 333, 335, 337, 339, 341, or 343. Further, block 361 indicates that the received prior information map can be a single map. Block 363 indicates that the prior information map can be multiple maps or multiple layers of maps. Block 365 indicates that the prior information map 258 can also take other forms. At block 364, the processing system 338 receives one or more signals from the machine speed sensor 146 or the sensor 336 or both.

[0103] At block 372, the processing system 338 processes the one or more received signals to generate processed data 340 indicative of a machine speed of the agricultural harvester 100.

[0104] At block 382, the prediction model generator 210 also obtains a geographic location 334 corresponding to the processed data. For example, the prediction model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location at which the processed data or the derived processed data 340 was acquired based on the machine delay, the machine speed, and the like.

[0105] At block 384, the prediction model generator 210 generates one or more prediction models, e.g., prediction models 350, that model the relationship between values in one or more of the prior information maps 258 and the machine speed sensed by the field sensors 208. The VI value to speed model generator 342 generates a prediction model that models the relationship between the VI values in the VI map 332 and the machine speed sensed by the field sensors 208. The biomass to speed model generator 344 generates a prediction model that models the relationship between the biomass values in the biomass map 335 and the machine speed sensed by the field sensors 208. The terrain to speed model generator 345 generates a prediction model that models the relationship between one or more terrain values (e.g., pitch, roll, or slope) in the terrain map 339 and the machine speed sensed by the field sensors 208. The seeding characteristics to speed model generator 346 generates a prediction model that models the relationship between the seeding characteristics in the seeding map 343 and the machine speed sensed by the field sensors 208. The yield to speed model generator 347 generates a prediction model that models the relationship between the yield values in the yield map 333 and the machine speed sensed by the field sensors 208. The crop status to speed model generator 342 generates a prediction model that models the relationship between the crop status values in the crop status map 337 and the machine speed sensed by the field sensors 208. The soil properties to speed model generator 351 generates a prediction model that models the relationship between the soil characteristic values in the soil properties map 341 and the machine speed sensed by the field sensors 208. At block 386, the prediction models 350 are provided to the predicted map generator 212, which generates a functional predicted speed map 360 that maps a predicted target machine speed based on the prior information maps 258 and the prediction speed models 350. The speed map generator 352 can use the prediction models 350 that model the relationship between the VI values in the VI map 332 and the machine speed and use the VI map 332 to generate the functional predicted speed map 360. The speed map generator 352 can use the prediction models 350 that model the relationship between the yield values in the yield map 333 and the machine speed and use the yield map 333 to generate the functional predicted speed map 360. The speed map generator 352 can use the prediction models 350 that model the relationship between the biomass values in the biomass map 335 and the machine speed and use the biomass map 335 to generate the functional predicted speed map 360. The speed map generator 352 can use the prediction models 350 that model the relationship between the crop status values in the crop status map 337 and the machine speed and use the crop status map 337 to generate the functional predicted speed map 360.The speed map generator 352 can generate a functional prediction speed map 360 using the prediction model 350 that models a relationship between terrain values in the terrain map 339 and machine speed, and using the terrain map 332. The speed map generator 352 can generate a functional prediction speed map 360 using the prediction model 350 that models a relationship between soil property values in the soil property map 341 and machine speed, and using the soil property map 341. The speed map generator 352 can generate a functional prediction speed map 360 using the prediction model 350 that models a relationship between seeding characteristic values in the seeding map 343 and machine speed, and using the seeding map 343.

[0106] Thus, as the agricultural harvester moves through the field to perform an agricultural operation, one or more functional prediction speed maps 360 are generated as the agricultural operation is being performed. At block 394, the prediction map generator 212 outputs the functional prediction speed map 360. At block 391, the functional prediction speed map generator 212 outputs the functional prediction speed map 360 for presentation to and possible interaction by the operator 260. At block 393, the prediction map generator 212 can configure the map 360 for consumption by the control system 214. At block 395, the prediction map generator 212 can also provide the map 360 to the control zone generator 213 for generation of control zones. At block 397, the prediction map generator 212 can also otherwise configure the map 360. The functional prediction speed map 360, with or without control zones, is provided to the control system 214. At block 396, the control system 214 generates control signals based on the functional prediction speed map 360, with or without control zones, to control the controllable subsystems 216.

[0107] The control system 214 can generate control signals to control the header or other machine actuator 248. The control system 214 can generate control signals to control the propulsion subsystem 250. The control system 214 can generate control signals to control the steering subsystem 252. The control system 214 can generate control signals to control the residue subsystem 138. The control system 214 can generate control signals to control the machine cleaning subsystem 254. The control system 214 can generate control signals to control the threshing machine 110. The control system 214 can generate control signals to control the material handling subsystem 125. The control system 214 can generate control signals to control the crop cleaning subsystem 118. The control system 214 can generate control signals to control the communication system 206. The control system 214 can generate control signals to control the operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0108] Figure 6 is Figure 1 a block diagram of exemplary portions of the agricultural harvester 100 shown in FIG. 6. In particular, FIG. 6 shows examples of the prediction model generator 210 and the prediction map generator 212. In the example shown, the information map 259 is one or more of the functional predicted speed map 360, the speed map 400 with control zones, or another speed map 401.

[0109] Additionally, in Figure 6 the example shown, the field sensors 208 can include one or more of a variety of different sensors 402 and the processing system 406. Some examples of the field sensors 208 and the sensors 402 are described below with respect to Figure 8 FIG. 7.

[0110] The prediction model generator 210 can include a speed characteristic to field sensor data model generator 416. In other examples, the prediction model generator 210 can include additional or other model generators 424. The prediction model generator 210 generates a prediction model 426 from the geographic location sensor 204 geographic location indicators 324 and that models a relationship between information in one or more information maps 259 and characteristic data provided by one or more field sensors 402. For example, the speed characteristic to characteristic model generator 416 generates a relationship between a speed characteristic value (which can be located on the map 360, the map 400, or the map 401) and a characteristic value sensed by the sensor 402. The speed characteristic to field data model generator 416 exemplarily generates a model that represents a relationship between a travel speed or a variable indicative of a travel speed in the information map 259 and a characteristic or characteristic value sensed by the field sensor 402. The prediction characteristic models 426 generated by the prediction model generator 210 can include prediction models that can be generated by the speed characteristic to characteristic model generator 416.

[0111] In Figure 6 the example shown, the prediction map generator 212 includes a prediction characteristic map generator 428. In other examples, the prediction map generator 212 can include additional or other map generators 434. The prediction characteristic map generator 428 receives the prediction characteristic models 426 that model a relationship between a machine speed on the information map 259 and a characteristic sensed by the sensor 402. The prediction characteristic map generator 428 generates a functional prediction characteristic map 436 that predicts a characteristic or a value of a characteristic at different locations in a field based on machine speeds at the locations in one or more of the information maps 259 and based on the prediction characteristic models 426.

[0112] The function prediction map generator 212 outputs a function prediction characteristic map 436. The function prediction characteristic map 436 can be provided to the control region generator 213, the control system 214, or both. The control region generator 213 generates a control region to provide the function prediction characteristic map 436 with a control region. The function prediction characteristic map 436 (with or without a control region) can be provided to the control system 214, which generates control signals to control one or more controllable subsystems 216 based on the function prediction map 436 (with or without a control region). The function prediction characteristic map 436 (with or without a control region) can be presented to the operator 260 or another user.

[0113] Based on the function prediction characteristic map 436 (with or without a control region), the control system 214 can generate control signals to control one or more controllable subsystems 216. For example, the control system 214 can control a header or other machine actuator(s) 248. The control system 214 can generate control signals to control a propulsion subsystem 250. The control system 214 can generate control signals to control a steering subsystem 252. The control system 214 can generate control signals to control a residue subsystem 138. The control system 214 can generate control signals to control a machine cleaning subsystem 254. The control system 214 can generate control signals to control a threshing machine 110. The control system 214 can generate control signals to control a material handling subsystem 125. The control system 214 can generate control signals to control a crop cleaning subsystem 118. The control system 214 can generate control signals to control a communication system 206. The control system 214 can generate control signals to control an operator interface mechanism 218. The control system 214 can generate control signals to control various other controllable subsystems 256.

[0114] Figure 7 A flowchart illustrating one example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction characteristic model 426 and a function prediction characteristic map 436 is shown. At block 442, the prediction model generator 210 and the prediction map generator 212 receive a map, such as the information map 259. The information map 259 can be the function prediction speed map 360, the speed map 400 with a control region, or another speed map 406. At block 444, the field sensor 402 generates a sensor signal containing sensor data indicative of a characteristic sensed by the field sensor 402. The field sensor 402 can be one or more of the sensors described below, for example, with respect to Figure 8 The field sensor 402 can be one or more of the sensors described below, for example, with respect to

[0115] At block 454, the processing system 406 processes the data contained in the sensor signal received from the field sensor 402 to obtain processed data 409, such asFigure 6 The data contained in the sensor signals can be in a raw format that is processed to receive processed data 409. For example, a temperature sensor signal includes resistance data. The resistance data can be processed into temperature data. In other examples, the processing can include digitizing, encoding, formatting, scaling, filtering, or classifying the data.

[0116] Returning to Figure 7 At block 456, the predictive model generator 210 also receives geographic locations 334 from the geographic location sensors 204, as shown. Figure 6 The geographic locations 334 can be associated with the geographic locations from which the sensed variables sensed by the field sensors 402 are obtained. For example, the predictive model generator 210 can obtain the geographic locations 334 from the geographic location sensors 204 and determine the precise geographic locations from which the processed data 409 is derived based on machine delays, machine speeds, and the like.

[0117] At block 458, the predictive model generator 210 generates a predictive model 426 that models the relationship between the mapped speed values in the received graph (e.g., the information graph 259) and the characteristic or related characteristic represented in the processed data 409 (e.g., a characteristic related to the characteristic sensed by the field sensors 402).

[0118] The predictive characteristic model 426 is provided to the predictive graph generator 212. At block 466, the predictive graph generator 212 generates a functional predictive characteristic graph, such as the functional predictive characteristic graph 436. The functional predictive characteristic graph 436 predicts characteristic values at different locations in the field. Thus, the functional predictive characteristic graph 436 is generated while the agricultural operation is being performed as the agricultural harvester 100 moves through the field performing the agricultural operation. The functional predictive characteristic graph 436 can be continuously, periodically, conditionally, manually, or upon some other event, updated.

[0119] At block 468, the predictive graph generator 212 outputs the functional predictive characteristic graph 436. At block 470, the predictive graph generator 212 configures the functional predictive characteristic graph 436 for presentation to and possible interaction by the operator 260 or another user. At block 472, the predictive graph generator 212 configures the functional predictive characteristic graph 436 for use by the control system 214. At block 474, the predictive graph generator 212 provides the functional predictive characteristic graph 436 to the control zone generator 213 for use in the generation and merging of control zones. At block 476, the predictive graph generator 212 otherwise configures the functional predictive characteristic graph 436. The functional predictive characteristic graph 436 (with or without control zones) can be presented to the operator 260 or another user or also provided to the control system 214.

[0120] At block 478, the control system 214 then generates control signals to control one or more controllable subsystems 216 based on the functional prediction characteristic map 436 (with or without control regions) and input from the geographic position sensor 204. For example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a cutting table or other machine actuator 248. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a propulsion subsystem 250. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a steering subsystem 252. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a residue subsystem 138. In another example, based on the functional prediction map 436 (with or without control regions), the control system 214 can generate control signals to control a machine cleaning subsystem 254. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a threshing machine 110. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a material handling subsystem 125. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a crop cleaning subsystem 118. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control a communication system 206. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control an operator interface mechanism 218. In another example, based on the functional prediction characteristic map 436 (with or without control regions), the control system 214 can generate control signals to control various other controllable subsystems 256.

[0121] For example, when the functional prediction characteristic map 436 or the functional prediction characteristic map 436 containing control regions is provided to the control system 214, the control system 214 generates control signals in a responsive manner to control one or more of the various controllable subsystems 216. For example, the control system 214 can generate control signals to control a cutting table or other machine actuator 248, which can also include actuators for other front end equipment, such as controlling rear axle speed of the cutting table to control position (height, pitch, or roll), to control a reel speed, or to control a belt conveyor belt speed.

[0122] In another example in which the control system 214 receives the function prediction map 436 or the function prediction map 436 with control regions added, the control system 214 sets the controller 232 to control one of the controllable subsystems 216 in the propulsion subsystem 250 (shown as the controllable subsystem 216 in the propulsion subsystem 250). Figure 2

[0123] ​In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the path planning controller 234 controls the steering subsystem 252 to cause the agricultural harvester 100 to steer. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the setting controller 232 controls the threshing machine settings of the threshing machine 110. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the setting controller 232 controls the crop cleaning subsystem. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the machine cleaning controller 245 controls the machine cleaning subsystem 254 on the agricultural harvester 100. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the operator interface controller 231 controls the operator interface mechanisms 218 on the agricultural harvester 100. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the table position controller 242 controls the machine / header actuators 248 to control the table on the agricultural harvester 100. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the conveyor belt controller 240 controls the machine / header actuators 248 to control the conveyor belt on the agricultural harvester 100. In another example where the control system 214 receives the function prediction map 436 or the function prediction map 436 with added control regions, the other controller 246 controls other controllable subsystems 256 on the agricultural harvester 100.

[0124] Figure 8 is a block diagram illustrating some examples of the field sensors 208. Figure 8Some or different combinations of the illustrated sensors can have both a sensor 402 and a processing system 406, while others can function as described above with respect to Figure 6 and Figure 7 the sensor 402 described above with respect to Figure 6 and 7 the processing system 406 is separate. Figure 8 Some of the possible field sensors 208 illustrated in FIG. 6 are shown and described with respect to the previous figures, and are numbered similarly. Figure 8 The field sensors 208 are shown to include non-machine sensors 479, operator input sensors 480, machine sensors 482, harvested material property sensors 484, field and soil property sensors 485, and environmental property sensors 487. As Figure 8 illustrated, the field sensors 208 can also include a variety of other sensors 226. The operator input sensors 480 are sensors that sense operator input, such as operator input sensed through the operator interface mechanisms 218. Thus, the operator input sensors 480 can sense user movement of a joystick, a lever, a steering wheel, a button, a dial, or a pedal. The operator input sensors 480 can also sense user interaction with other operator input mechanisms, such as interaction with a touch-sensitive screen, interaction with a microphone that uses voice recognition, or interaction with any of a variety of other operator input mechanisms.

[0125] Machine sensors 482 can sense different characteristics of agricultural harvester 100. For example, as noted above, machine sensors 482 include machine speed sensor 146, separator loss sensor 148, clean grain camera 150, forward view image capture mechanism 151, loss sensor 152, or geographic position sensor 204, examples of which are described above. Machine sensors 482 also include machine settings sensors 491 that sense machine settings. Some examples of machine settings are described above with respect to FIG. 1. Front end device (e.g., header) position sensor 493 can sense the position of header 102, reel 164, cutter 104, or other front end device relative to the frame of agricultural harvester 100. For example, sensor 493 can sense the height of header 102 from the ground. Machine sensors 482 also include front end device (e.g., header) orientation sensor 495. Sensor 495 can sense the orientation of header 102 relative to agricultural harvester 100 or relative to the ground. Machine sensors 482 can include stability sensor 497. Stability sensor 497 senses the oscillation or bounce motion (which can be measured by frequency or amplitude or both) of agricultural harvester 100. Machine sensors 482 also include residue settings sensor 499 configured to sense whether agricultural harvester 100 is configured to chop residue, produce windrows, or process residue in another manner. Machine sensors 482 can include clean grain bin fan speed sensor 551 that senses the speed of clean grain fan 120. Machine sensors 482 include concave gap sensor 553 that senses the gap between rotor 112 and concave 114 on agricultural harvester 100. Machine sensors 482 include chaffer screen gap sensor 555 that senses the size of openings in chaffer screen 122. Machine sensors 482 include threshing rotor speed sensor 557 that senses the speed of rotor 112. Machine sensors 482 include rotor pressure sensor 559 that senses the pressure used to drive rotor 112. Sensors 482 include screen mesh gap sensor 561 that senses the size of openings in screen mesh 124. Machine sensors 482 include MOG moisture sensor 563 that senses the moisture level of MOG passing through agricultural harvester 100. Machine sensors 482 include machine orientation sensor 565 that senses the orientation of agricultural harvester 100. Machine sensors 482 include material feed rate sensor 567 that senses the rate of material as it travels through feedhouse 106, clean grain elevator 130, or elsewhere in agricultural harvester 100. Machine sensors 482 include biomass sensor 569 that senses the biomass passing through feedhouse 106, through separator 116, or elsewhere in agricultural harvester 100.Machine sensors 482 include a fuel consumption sensor 571 that senses a rate of fuel consumption of the agricultural harvester 100 over time. Machine sensors 482 include a power utilization sensor 573 that senses power utilization in the agricultural harvester 100, such as which subsystem is using power, or a rate at which a subsystem is using power, or an allocation of power among subsystems in the agricultural harvester 100. Machine sensors 482 include a tire pressure sensor 577 that senses an inflation pressure in the tires 144 of the agricultural harvester 100. Sensors 482 can also include a variety of other machine performance sensors, or machine characteristic sensors, as indicated by block 575. Machine performance sensors and machine characteristic sensors 575 can sense a performance or a characteristic of the agricultural harvester 100.

[0126] Harvested material characteristic sensors 484 can sense characteristics of the severed crop material as the crop material is being processed by the agricultural harvester 100. Crop characteristics can include things such as crop type, crop moisture, grain quality (e.g., broken grain), MOG levels, grain composition (e.g., starch and protein), MOG moisture, and other crop material properties.

[0127] Field and soil property sensors 485 can sense characteristics of the field and soil. Field and soil properties can include soil moisture, soil firmness, presence and location of standing water, soil type, and other soil and field characteristics.

[0128] Environmental characteristic sensors 487 can sense one or more environmental characteristics. Environmental characteristics can include things such as wind direction and speed, precipitation, fog, dust levels, or other obscuring matter, or other environmental characteristics.

[0129] Figure 9A block diagram illustrating one example of a control zone generator 213 is shown. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 can also include other items 492. The control zone generation system 488 includes a control zone criteria identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other items 520. The regime zone generation system 490 includes a regime zone criteria identification component 522, a regime zone boundary definition component 524, a setting resolver identifier component 526, and other items 528. Before describing the overall operation of the control zone generator 213 in more detail, a brief description of some of the items in the control zone generator 213 and their respective operations will first be provided.

[0130] The agricultural harvester 100 or other work machine can have multiple different types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other work machine are collectively referred to as work machine actuators (WMAs). Each WMA can be controlled independently based on values on the function prediction map, or the WMAs can be controlled in groups based on one or more values on the function prediction map. Thus, the control zone generator 213 can generate control zones corresponding to each individually controllable WMA, or to groups of WMAs that are controlled in coordination with each other.

[0131] The WMA selector 486 selects a WMA or group of WMAs for which a corresponding control zone is to be generated. The control zone generation system 488 then generates a control zone for the selected WMA or group of WMAs. Different criteria can be used in identifying the control zone for each WMA or group of WMAs. For example, for one WMA, WMA response time can be used as a criterion for defining the boundaries of the control zone. In another example, wear characteristics (e.g., how much a particular actuator or mechanism wears due to its movement) can be used as a criterion for identifying the boundaries of the control zone. The control zone criteria identifier component 494 identifies the particular criteria that will be used to define the control zone for the selected WMA or group of WMAs. The control zone boundary definition component 496 processes values on the function prediction map in the analysis to define the boundaries of the control zone on the function prediction map in the analysis based on the values on the function prediction map in the analysis and based on the control zone criteria for the selected WMA or group of WMAs.

[0132] The target setting identifier component 498 sets the value of the target setting that will be used to control the WMA or WMA group in the different control zones. For example, if the selected WMA is the propulsion system 250 and the function prediction map under analysis is the function prediction speed map 438, the target setting in each control zone can be a target speed setting based on the speed values contained in the function prediction speed map 238 within the identified control zone.

[0133] In some examples, where the agricultural harvester 100 is controlled based on the current or future location of the agricultural harvester 100, multiple target settings are possible for a WMA at a given location. In this case, the target settings can have different values and can be in competition. 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 the propulsion system 250 that is controlled to control the speed of the agricultural harvester 100, there can be multiple different competing standard groups that are considered by the control zone generation system 488 in identifying the control zones and the target settings for the selected WMA in the control zones. For example, different target settings for controlling the speed of the machine can be generated based on, for example, a detected or predicted feed rate value, a detected or predicted fuel efficiency value, a detected or predicted grain loss value, or a combination of these values. 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 the agricultural harvester 100.

[0134] Thus, in some examples, the state zone generation system 490 generates state zones to resolve the multiple different competing target settings. The state zone criteria identification component 522 identifies criteria for establishing a state zone for the selected WMA or WMA group on the function prediction map under analysis. Some criteria that can be used to identify or define a state zone include, for example, crop type or crop variety (based on a planting map or another source of crop type or crop variety), weed type, weed intensity, or crop state such as whether the crop is laid flat, partially laid flat, or standing. Just as each WMA or WMA group can have a corresponding control zone, different WMA or WMA groups can have a corresponding state zone. The state zone boundary definition component 524 identifies the boundaries of the state zones on the function prediction map under analysis based on the state zone criteria identified by the state zone criteria identification component 522.

[0135] In some examples, the status areas can overlap one another. For example, the crop variety status area can overlap with some or all of the crop status status area. In such examples, different status areas can be assigned a priority level such that, in the event of overlap between two or more status areas, the status area assigned a higher level position or importance in the priority level takes precedence over the status area with a lower level position or importance in the priority level. The priority level of the status areas can be set manually or can be set automatically using a rules-based system, a model-based system, or other system. As one example, in the event of overlap between the lodged crop status area and the crop variety status area, the lodged crop status area can be assigned a greater importance in the priority level than the crop variety status area such that the lodged crop status area takes precedence.

[0136] Further, for a given WMA or group of WMAs, each status area can have a unique settings resolver. The settings resolver identifier component 526 identifies a particular settings resolver for each status area identified on the function prediction map under analysis and identifies a particular settings resolver for the selected WMA or group of WMAs.

[0137] Once the settings resolver for a particular status area is identified, the settings resolver can be used to resolve a competing target setting in which more than one target setting is identified based on the control area. Different types of settings resolvers can have different forms. For example, the settings resolver identified for each status area can include a human settings resolver in which the competing target setting is presented to an operator or other user for resolution. In another example, the settings resolver can include a neural network or other artificial intelligence or machine learning system. In such a case, the settings resolver can resolve the competing target setting based on a predicted quality metric or historical quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting can decrease the time to harvest a field and decrease the corresponding time-based labor and equipment costs, but can increase grain loss. A decreased vehicle speed setting can increase the time to harvest a field and increase the corresponding time-based labor and equipment costs, but can decrease grain loss. When grain loss or harvesting time is selected as the quality metric, the predicted value or historical value of the selected quality metric can be used to resolve the speed setting given two competing vehicle speed setting values. In certain cases, the settings resolver can be a set of threshold rules that can be used in place of or in addition to the status areas. An example of a threshold rule can be expressed as follows:

[0138] If the predicted biomass value within 20 feet of header 20 of agricultural harvester 100 is greater than x kilograms (where x is a selected or predetermined value), then a target setting value based on feed rate rather than other competing target setting values is used, otherwise a target setting value based on grain loss rather than other competing target setting values is used.

[0139] The setting resolver can be a logical component that executes logical rules in identifying the target setting. For example, the setting resolver can resolve the target setting while attempting to minimize the harvesting time or minimize the total harvesting cost or maximize the harvested grain, or other variables calculated based on functions of different candidate target settings. The harvesting time can be minimized when the amount of harvesting completed is reduced to or below a selected threshold. The total harvesting cost can be minimized when the total harvesting cost is reduced to or below a selected threshold. The harvested grain can be maximized when the amount of harvested grain is increased to or above a selected threshold.

[0140] Figure 10 is a flowchart illustrating one example of the operation of control region generator 213 in generating control regions and status regions for the graph received by control region generator 213 for zone processing (e.g., the graph in analysis).

[0141] At block 530, control region generator 213 receives the graph in analysis for processing. In one example, as shown in block 532, the graph in analysis is a functional prediction graph. For example, the graph in analysis can be one of functional prediction graphs 360 or 436. Block 534 indicates that the graph in analysis can also be other graphs.

[0142] At block 536, the WMA selector 486 selects a WMA or group of WMAs for which to generate a control zone on the map in the analysis. At block 538, the control zone criteria identification component 494 obtains control zone definition criteria for the selected WMA or group of WMAs. Block 540 indicates an example in which the control zone criteria are or include wear characteristics of the selected WMA or group of WMAs. Block 542 indicates an example in which the control zone definition criteria are or include magnitudes and variations of input source data, such as magnitudes and variations of values on the map in the analysis or magnitudes and variations of inputs from various on-site sensors 208. Block 544 indicates an example in which the control zone definition criteria are or include physical machine characteristics, such as physical dimensions of the machine, speeds of different subsystem operations, or other physical machine characteristics. Block 546 indicates an example in which the control zone definition criteria are or include responsiveness of the selected WMA or group of WMAs in reaching set values of new commands. 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 include operator preferences. Block 552 indicates an example in which the control zone definition criteria are also or include other items. Block 549 indicates an example in which the control zone definition criteria are time-based, meaning that the agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has elapsed since the agricultural harvester 100 entered a particular control zone. In some cases, the selected amount of time can be a minimum amount of time. Thus, in some cases, the control zone definition criteria can prevent the agricultural harvester 100 from crossing the 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, control zone definition criteria based on a selected size value can exclude the definition of control zones that are smaller than the selected size. In some cases, the selected size can be a minimum size.

[0143] At block 554, the state area criteria identification component 522 obtains the state area definition criteria for the selected WMA or group of WMAs. Block 556 indicates an example in which the state area definition criteria are based on manual input from the operator 260 or another user. Block 558 shows an example in which the state area definition criteria are based on the crop type or crop variety. Block 560 shows an example in which the state area definition criteria are based on the weed type or weed intensity or both. Block 562 shows an example in which the state area definition criteria are based on or include the crop status. Block 564 shows an example in which the state area definition criteria are also or include other criteria, such as a characteristic or value of a characteristic detected by an in-field sensor or a speed characteristic value, such as a predicted speed characteristic value.

[0144] At block 566, the control area boundary definition component 496 generates the boundaries of the control areas on the map under analysis based on the control area criteria. The state area boundary definition component 524 generates the boundaries of the state areas on the map under analysis based on the state area criteria. Block 568 indicates an example in which the boundaries of the areas are identified for both the control areas and the state areas. Block 570 shows the target setting identifier component 498 identifying the target setting for each of the control areas. The control areas and state areas can also be generated in other ways, and this is indicated by block 572.

[0145] At block 574, the setting resolver identifier component 526 identifies the setting resolver for the selected WMA in each of the state areas defined by the state area boundary definition component 524. As discussed above, the state area resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver based on the predicted or historical quality of each competing target setting 580, a rule-based resolver 582, a performance criteria-based resolver 584, or other resolver 586.

[0146] At block 588, the WMA selector 486 determines whether there are more WMAs or groups of WMAs to process. If there are additional WMAs or groups of WMAs that need to be processed, the process returns to block 436 where the next WMA or group of WMAs for which control regions and status regions are to be defined is selected. When there are no additional WMAs or groups of WMAs for which control regions or status regions are to be generated left, the process moves to block 590 where the control region generator 213 outputs a map for each of the WMAs or groups of WMAs with control regions, target settings, status regions, and setting resolvers. As discussed above, the output map can be presented to the operator 260 or another user; the output map can be provided to the control system 214; or the output map can be output in other ways. At block 592, the control system 214 generates control signals to control one or more of the controllable subsystems based on the map with control regions, target settings, status regions, and setting resolvers for each of the WMAs or groups of WMAs.

[0147] Figure 11 One example of the operation of the control system 214 in controlling the agricultural harvester 100 based on the map output by the control region generator 213 is shown. Thus, at block 592, the control system 214 receives the map of the work site. In some cases, the map can be a functional prediction map that can include control regions and status regions (as indicated by block 594). In some cases, the received map can be a functional prediction map that excludes control regions and status regions. Block 596 indicates examples in which the received map of the work site can be a priori information map with control regions and status regions identified thereon. Block 598 indicates examples in which the received map can include multiple different maps or multiple different map layers. Block 610 indicates examples in which the received map can also take other forms.

[0148] At block 612, the control system 214 receives a sensor signal from the geo-location sensor 204. The sensor signal from the geo-location sensor 204 can include data indicative of a geographic location 614 of the agricultural harvester 100, a speed 616 of the agricultural harvester 100, a heading 618 of the agricultural harvester 100, or other information 620. At block 622, the zone controller 247 selects a state zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geo-location sensor signal. At block 626, the zone controller 247 selects a WMA or group of WMAs to be controlled. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or group of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from a variety of different sources. For example, block 630 illustrates an example in which one or more of the target settings for the selected WMA or group of WMAs is based on input from the control zone on the map from the job site. Block 632 illustrates an example in which one or more of the target settings is obtained from manual input by the operator 260 or another user. Block 634 illustrates an example in which the target settings are obtained from the field sensors 208. Block 636 illustrates an example in which one or more target settings are obtained from one or more sensors on other machines that are simultaneously operating in the same field as the agricultural harvester 100 or from one or more sensors on machines that have operated in the same field in the past. Block 638 illustrates an example in which the target settings are also obtained from other sources.

[0149] In some examples, as discussed above, there can be multiple different target settings for the same WMA or a group of WMAs that conflict with each other, for example, when the agricultural harvester 100 is located in the field at a location that has overlapping control zones or overlapping state zones, or both. In examples in which the selected WMA is an actuator in the propulsion subsystem 250 that controls the speed of the machine, there can be two or more different target speed settings. These different target speed settings are then resolved to identify a single target speed setting that will be used to control the speed of the agricultural harvester 100.

[0150] At block 640, the zone controller 247 accesses a setting resolver for the selected state zone and controls the setting resolver to resolve the competitive target speed setting into a resolved target speed setting. As discussed above, in some cases, the setting resolver can be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competitive target setting to the operator 260 or another user for resolution. In some cases, the setting resolver can be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competitive target setting to the neural network, artificial intelligence, or machine learning system for selection. In certain cases, the setting resolver can be based on predicted quality metrics or historical quality metrics, based on threshold rules, or based on a logic component. In any of these latter examples, the zone controller 247 executes the setting resolver to obtain the resolved target setting based on predicted quality metrics or historical quality metrics, based on threshold rules, or in the case of using a logic component.

[0151] At block 642, with the resolved target setting identified by the zone controller 247, the zone controller 247 provides the resolved target setting to other controllers in the control system 214, which generate control signals based on the resolved target setting and apply the control signals to the selected WMA or group of WMAs. In some examples, when the selected WMA is an actuator in the propulsion subsystem 250 that controls the speed of the agricultural harvester 100, the zone controller 247 provides the resolved target speed setting to the settings controller 232 to generate control signals based on the resolved target speed setting, and those generated control signals are applied to the selected actuator in the propulsion subsystem 250 to control the speed of the agricultural harvester 100. In other examples, the selected WMA can be one or more actuators in one or more of the controllable subsystems of the agricultural harvester 100 (e.g., the controllable subsystems 216). At block 644, if additional WMAs or groups of WMAs are to be controlled at the current geographic location of the agricultural harvester 100 (as detected at block 612), the process returns to block 626, where the next WMA or group of WMAs is selected. The process represented by blocks 626-644 continues until all of the WMAs or groups of WMAs to be controlled at the current geographic location of the agricultural harvester 100 have been addressed. If no additional WMAs or groups of WMAs remain to be controlled at the current geographic location of the agricultural harvester 100, the process proceeds to block 646, where the zone controller 247 determines whether additional control zones to be considered exist in the selected state zone. If additional control zones to be considered exist, the process returns to block 624, where the next control zone is selected. If no additional control zones need to be considered, the process proceeds to block 648, where a determination is made as to whether there are additional state zones to be considered. The zone controller 247 determines whether there are additional state zones to be considered. If there are additional state zones to be considered, the process returns to block 622, where the next state zone is selected.

[0152] At block 650, the zone controller 247 determines whether the operation being performed by the agricultural harvester 100 is complete. If not, the zone controller 247 determines whether control zone criteria have been met to continue processing, as shown at block 652. For example, as mentioned above, the control zone definition criteria can include criteria defining when the agricultural harvester 100 can cross the control zone boundary. For example, whether the agricultural harvester 100 can cross the control zone boundary can be defined by a selected time period, meaning that the agricultural harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can continuously perform the processing. Thus, the zone controller 247 does not wait for any particular time period before continuing to determine whether the operation of the agricultural harvester 100 is complete. At block 652, the zone controller 247 determines that it is time to continue processing, and then processing continues at block 612, where the zone controller 247 again receives input from the geo-location sensor 204. It should also be understood that the zone controller 247 can use a multiple-input, multiple-output controller to simultaneously control the WMAs and WMA groups, rather than sequentially controlling the WMAs and WMA groups.

[0153] Figure 12 is a block diagram illustrating one example of an operator interface controller 231. In the illustrated example, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction system 656, a speech processing system 658, and an action signal generator 660. The operator input command processing system 654 includes a speech handling system 662, a touch gesture processing system 664, and other 666. The other controller interaction system 656 includes a controller input processing system 668 and a controller output generator 670. The speech processing system 658 includes a trigger detector 672, a recognition component 674, a synthesis component 676, a natural language understanding system 678, a dialog management system 680, and other 682. The action signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a haptic control signal generator 688, and other 690. A brief description of some of the items in the operator interface controller 231 and their associated operations is provided before describing the operation of the example operator interface controller 231 in handling various operator interface actions. Figure 12

[0154] ​The operator input command processing system 654 detects operator input on the operator interface mechanisms 218 and processes these command inputs. The speech handling system 662 detects speech input and processes interactions with the speech processing system 658 to process speech command inputs. The touch gesture processing system 664 detects touch gestures on touch sensitive elements in the operator interface mechanisms 218 and processes these command inputs.

[0155] Other controller interaction system 656 handles interactions with other controllers in the control system 214. The controller input processing system 668 detects and processes inputs from other controllers in the control system 214 and the controller output generator 670 generates outputs and provides these outputs to other controllers in the control system 214. The speech processing system 658 recognizes speech inputs, determines the meaning of these inputs, and provides outputs indicating the meaning of the verbal inputs. For example, the speech processing system 658 can recognize a speech input from the operator 260 as a set change command where the operator 260 is commanding the control system 214 to change a setting of the controllable subsystem 216. In such an example, the speech processing system 658 recognizes the content of the verbal command, identifies the meaning of the command as a set change command, and provides the meaning of the input back to the speech handling system 662. The speech handling system 662 in turn interacts with the controller output generator 670 to provide the command output to the appropriate controller in the control system 214 to complete the verbal set change command.

[0156] The voice processing system 658 can be invoked in a variety of different ways. For example, in one example, the voice handling system 662 provides input from a microphone (as one of the operator interface mechanisms 218) continuously to the voice processing system 658. The microphone detects voice from the operator 260, and the voice handling system 662 provides the detected voice to the voice processing system 658. The trigger detector 672 detects a trigger that indicates that the voice processing system 658 is invoked. In some cases, the voice recognition component 674 performs continuous voice recognition on all voice spoken by the operator 260 as the voice processing system 658 receives continuous voice input from the voice handling system 662. In some cases, the voice processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the voice processing system 658 can be initiated based on recognizing a selected spoken word, referred to as a wake-up word. In such examples, the recognition component 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized in the case that the recognition component 674 recognizes the wake-up word. The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, the voice processing system 658 can be invoked by the 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 trigger input. In such examples, the trigger detector 672 can detect that the voice processing system 658 has been invoked when the trigger input via the user interface mechanism is detected. The trigger detector 672 can also detect that the voice processing system 658 has been invoked in other ways.

[0157] Once the voice processing system 658 is invoked, voice input from the operator 260 is provided to the voice recognition component 674. The voice recognition component 674 recognizes linguistic elements in the voice input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies a meaning of the recognized voice. The meaning can be any of a natural language output, a command output identifying a command reflected in the recognized voice, a value output identifying a value in the recognized voice, or a variety of other outputs reflecting an understanding of the recognized voice. For example, more generally, the natural language understanding system 678 and the voice processing system 568 can understand a meaning of voice recognized in the context of the agricultural harvester 100.

[0158] In some examples, the speech processing system 658 can also generate output that navigates the operator 260 through a user experience based on the speech input. For example, the dialog management system 680 can generate and manage a dialog with the user in order to identify what the user wants to do. The dialog box can disambiguate user commands, identify one or more particular values needed to perform the user command; or obtain other information from the user or provide other information to the user or both. The synthesis component 676 can generate speech synthesis that can be presented to the user through an audio operator interface mechanism such as a speaker. Thus, the dialog managed by the dialog management system 680 can be exclusively a spoken dialog or a combination of a visual dialog and a spoken dialog.

[0159] The action signal generator 660 generates action signals to control the operator interface mechanisms 218 based on output from one or more of the operator input command processing system 654, the other controller interaction system 656, and the speech processing system 658. The visual control signal generator 684 generates control signals to control visual items in the operator interface mechanisms 218. The visual items can be lights, display screens, warning indicators, or other visual items. The audio control signal generator 686 generates output to control audio elements of the operator interface mechanisms 218. The audio elements include speakers, audible warning mechanisms, horns, or other audible elements. The haptic control signal generator 688 generates control signals that are output to control haptic elements of the operator interface mechanisms 218. The haptic elements include vibratory elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals, or joystick used by the operator. The haptic elements can include tactile feedback or force feedback elements that provide tactile or force feedback to the operator through the operator interface mechanisms. The haptic elements can also include a wide variety of other haptic elements.

[0160] Figure 13 is a flowchart showing one example of the operation of the operator interface controller 231 in generating an operator interface display on the operator interface mechanisms 218, which can include a touch-sensitive display screen. Figure 13 Also shown is one example of how the operator interface controller 231 can detect and process operator interaction with the touch-sensitive display screen.

[0161] At block 692, the operator interface controller 231 receives a map. Block 694 indicates an example in which the map is a function prediction map, while block 696 indicates an example in which the map is another type of map. At block 698, the operator interface controller 231 receives input from the geo-location sensor 204 identifying a geo-location of the agricultural harvester 100. As shown in block 700, the input from the geo-location sensor 204 can include a heading and a position of the agricultural harvester 100. Block 702 indicates an example in which the input from the geo-location sensor 204 includes a speed of the agricultural harvester 100, while block 704 indicates an example in which the input from the geo-location sensor 204 includes other items.

[0162] At block 706, the visual control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen in the operator interface mechanism 218 to generate a display showing all or a portion of the 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 identifying a next work unit (or area on the field) in which the agricultural harvester 100 is to operate. Block 712 indicates an example in which the displayed field includes a coming area display portion showing areas that have not yet been processed by the agricultural harvester 100 (e.g., unharvested areas), while block 714 indicates an example in which the displayed field includes a previously visited display portion representing areas of the field that have been processed by the agricultural harvester 100 (e.g., harvested areas). Block 716 indicates an example in which the displayed field shows various characteristics of the field with geo-referenced locations on the map. For example, if the received map is a weed map, the displayed field can show different weed types present in the geo-referenced field within the displayed field. The mapped characteristics can be shown in previously visited areas (as shown in block 714), coming areas (as shown in block 712), and next work units (as shown in block 710). Block 718 indicates examples in which the displayed field includes other items as well, e.g., the displayed field can include a control area or a status area, or both.

[0163] At block 760, the operator interface controller 231 detects an input that marks a region of the field (e.g., sets a flag corresponding to an intersection with a region or map boundary in the field), and controls the touch-sensitive user interface display to display the flag on the field display portion. The detected input can be an operator input (as indicated by 762), or an input from another controller (as indicated by 764). At block 766, the operator interface controller 231 detects a field sensor input from one of the field sensors 208 that indicates a measured characteristic of the field. At block 768, the visual control signal generator 684 generates a control signal to control the user interface display to display actuators for modifying the user interface display and for modifying machine control. For example, block 770 indicates that one or more of the actuators for setting or modifying values displayed on the user interface display can be displayed. Thus, the user can set the flags and modify characteristics of the flags. For example, the user can modify values corresponding to the flags, such as a speed or a speed range, or both. Block 772 indicates that action thresholds can also be displayed. Block 776 indicates that actions to be taken (e.g., increase the speed of the machine) can be displayed, and block 778 indicates that measured field data can also be displayed. Block 780 indicates that a wide variety of other information and actuators can also be displayed on the user interface display.

[0164] At block 782, the operator input command processing system 654 detects and processes operator inputs corresponding to interactions with the user interface display performed by the operator 260. Where the user interface mechanism on which the user interface display is displayed is a touch-sensitive display screen, the operator interactions with the touch-sensitive display screen can be touch gestures 784. In some cases, the operator interaction inputs can be inputs using a click device 786 or other operator interaction inputs 788.

[0165] At block 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, block 792 indicates that the signal can be received by the controller input processing system 668, the signal indicating that the detected value satisfies a threshold condition. As explained previously, the threshold condition can include a value below, at, or above a threshold value. Block 794 shows that the action signal generator 660 can generate a visual alarm by using the visual control signal generator 684, generate an audio alarm by using the audio control signal generator 686, generate a haptic alarm by using the haptic control signal generator 688, or warn the operator 260 by using any combination of these, in response to receiving the alarm condition. Similarly, as shown by block 796, the controller output generator 670 can generate outputs to other controllers in the control system 214, causing those controllers to perform corresponding actions displayed on the user interface display. Block 798 shows that the operator interface controller 231 can also detect and handle alarm conditions in other ways.

[0166] Block 900 shows that the speech handling system 662 can detect and handle inputs that invoke the speech processing system 658. Block 902 shows that performing speech processing can include using the dialog management system 680 to have a conversation with the operator 260. Block 904 shows that the speech processing can include providing signals to the controller output generator 670, causing control operations to be performed automatically based on the speech input.

[0167] Table 1 below shows an example of a conversation between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 invokes the speech processing system 658 using a trigger word or wake-up word that is detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is “Johnny”.

[0168] Table 1

[0169] Operator: “Johnny, tell me about current speed”

[0170] Operator interface controller: “Current speed is 3.0 mph, and target speed is 3.1 mph”.

[0171] Operator: “Johnny, what should I do to maximize feed rate?”

[0172] Operator interface controller: “Increase speed to 5.1 mph”.

[0173] Table 2 illustrates an example in which the speech synthesis unit 676 provides output to the audio control signal generator 686 to provide auditory updates intermittently or periodically. The interval between updates can be based on time (such as every five minutes), or on coverage or distance (such as every five acres), or on anomalies (such as when a measured value is greater than a threshold).

[0174] Table 2

[0175] Operator interface controller: "The speed has reached 3.0 mph in the past 10 minutes."

[0176] Operator interface controller: "Approaching a new status area".

[0177] Operator interface controller: "Warning: Current speed is lower than the target speed setting".

[0178] Operator interface controller: "Attention: A new status area is coming soon. Select target speed setting."

[0179] The examples shown in Table 3 illustrate some actuators or user input mechanisms on the touch-sensitive display 720 that can be supplemented by voice dialogue. Figure 14 (As shown in Table 3). The example in Table 3 illustrates that the motion signal generator 660 can generate motion signals to automatically select new target settings.

[0180] Table 3

[0181] Human: "Johnny, select the target speed setting corresponding to the crop status."

[0182] Operator interface controller: "Target speed setting for crop condition has been selected. Target machine speed is now 3.1 mph."

[0183] The example shown in Table 4 illustrates that the motion signal generator 660 can communicate with the operator 260 to start and end the selection of target machine settings.

[0184] Table 4

[0185] Human: "Johnny, select the speed setting that corresponds to the crop status."

[0186] Operator interface controller: "Speed ​​setting corresponds to the selected crop condition".

[0187] Human: "Johnny, end the selection of the speed setting corresponding to the crop status, and select the speed setting corresponding to the feeding rate."

[0188] Operator interface controller: "Crop status speed setting stop. Select speed setting corresponding to feeding rate."

[0189] The example shown in Table 5 shows that the action signal generator 160 can generate signals selecting machine settings in a manner different from that shown in Tables 3 and 4.

[0190] Table 5

[0191] Human: "Johnny, select speed setting corresponding to crop status for next 100 feet."

[0192] Operator interface controller: "Select crop status speed setting for next 100 feet."

[0193] Returning again Figure 13 to FIG. 9, block 906 shows that the operator interface controller 231 can also detect and handle situations for outputting messages or other information in other ways. For example, the other controller interaction system 656 can detect input from other controllers indicating that an alert or output message should be presented to the operator 260. Block 908 shows that the output can be an audio message. Block 910 shows that the output can be a visual message, and block 912 shows that the output can be a haptic message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in block 914), processing returns to block 698, where the geographic location of the harvester 100 is updated, and processing continues as described above to update the user interface display.

[0194] Once the operation is complete, any desired values displayed or already displayed on the user interface display can be saved. These values can also be used in machine learning to improve different parts of the prediction model generator 210, the prediction map generator 212, the control region generator 213, the control algorithm, or other items. Saving the desired values is indicated by block 916. These values can be saved locally on the agricultural harvester 100, or the values can be saved at a remote server location or sent to another remote system.

[0195] Figure 14 is a diagram showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in an operator's cab of the agricultural harvester 100 or provided on a mobile device or elsewhere.

[0196] In Figure 14In the illustrated example, the user interface display 720 includes a display feature for operating the microphone 722 and the speaker 724. Thus, the touch- sensitive display can be communicatively coupled to the microphone 722 and the speaker 724. The box 726 indicates that the touch-sensitive display can include a variety of user interface control actuators, such as buttons, keyboards, soft keyboards, links, icons, switches, and the like. The operator 260 can actuate the user interface control actuators to perform various functions.

[0197] In Figure 14 In the illustrated example, the user interface display 720 includes a field display portion 728 that displays at least a portion of a field in which the agricultural harvester 100 is operating. In Figure 14 In the illustrated example, the field display portion 728 corresponds to a functional forecast map having a control area and a status area. The field display portion 728 is displayed with a current position marker 708 that corresponds to a current position of the agricultural harvester 100 in the portion of the field shown in the field display portion 728. In one example, the operator can control the touch-sensitive display to zoom in on portions of the field display portion 728, or pan or scroll the field display portion 728 to display different portions of the field. The current position marker 708 can also be configured to identify a direction of travel of the agricultural harvester 100, a speed of travel of the agricultural harvester 100, or both. In Figure 14 In the illustrated example, a shape of the current position marker 708 provides an indication of an orientation of the agricultural harvester 100 within the field that can be used as an indication of a direction of travel of the agricultural harvester 100.

[0198] The field display portion 728 also includes status areas 732A-D, unharvested areas 712A-D, and harvested areas 714A-D. The harvested areas 714A-D represent areas of the field that have been harvested (e.g., by the agricultural harvester 100 or another machine), while the unharvested areas 712A-D represent areas of the field that have not yet been harvested. Each status area 732 can include an unharvested area 712 and a harvested area 714. As shown, the status area 732A includes the unharvested area 712A and the harvested area 714A, the status area 732B includes the unharvested area 712B and the harvested area 714B, the status area 732C includes the unharvested area 712C and the harvested area 714C, and the status area 732D includes the unharvested area 712D and the harvested area 714D. The different status areas 732, unharvested areas 712, and harvested areas 714 can be displayed in a variety of ways, including renderings having a variety of display features (e.g., colors, patterns, shading, symbols, and numerous other display features).

[0199] exist Figure 14 In the example, the user interface display 720 also includes a current setting resolver indicator 721 displaying the current setting resolver of the machine settings, a current speed indicator 723 displaying the current speed of the agricultural harvester 100, a user-actuable display marker 733, and a control display section 738. In one example, the user-actuable display marker 733 is configured to receive user input for adjusting the current speed of the agricultural harvester 100. The control display section 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0200] The actuators and display markers on the user interface display 720 can be displayed as, for example, individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. Furthermore, the actuators and display markers can be touch-sensitive, allowing the operator 260 to activate the corresponding touch-sensitive actuator or display marker by touching it, for example, with a finger or device.

[0201] exist Figure 14In the illustrated example, the control display portion 738 includes a selection display column 746 and a control zone display column 748. The selection display column 746 displays selectable or otherwise variable target machine settings corresponding to one or more WMAs of the agricultural harvester 100. In the illustrated example, the selection display column 746 includes target speed settings 736A-736C and user-actuatable selection display indicia 737A-737C. The control zone display column 748 displays various control zones corresponding to the field or a location on the field. In the illustrated example, the control zone display column 748 includes control zone display indicia 739A-739C. In the illustrated example, each control zone display indicium 739 has a corresponding target speed setting 736 for an actuator in the propulsion subsystem 250, and each target speed setting 736 is selectable by the operator 260 by actuation of the selection display indicium 737. Thus, in the illustrated example, the target speed setting 736A corresponds to the feed rate control zone display indicium 739A, the target speed setting 736B corresponds to the labor and grain loss control zone display indicium 739B, and the target speed setting 736C corresponds to the crop condition control zone display indicium 739C. Thus, in the illustrated example, the feed rate control zone recommends a target speed setting of 5.1 miles per hour (mph) or approximately 8.21 kilometers per hour (kph) in the feed rate control zone in which the agricultural harvester is controlled based on a feed rate (e.g., a desired or selected feed rate level or value). The target speed setting for the labor and grain loss control zone is 4.7 mph or approximately 7.56 kph in the labor and grain loss control zone in which the agricultural harvester is controlled based on a labor cost and a grain loss (e.g., a desired or selected labor cost and grain loss level or value). The target speed setting for the crop condition control zone is 3.1 mph or approximately 4.99 kph in the crop condition control zone in which the agricultural harvester 100 is controlled based on a crop condition (e.g., a predicted or measured crop condition level or value).

[0202] As previously discussed herein, there can be multiple control zones for a given location of the field, and multiple target settings for the WMA at the given location, such as multiple target speed settings for the actuators in the propulsion subsystem 250 at the given location on the field. In the illustrated example, the agricultural harvester enters the competing setting zone 733 in which there are multiple target speed settings for the WMA or group of WMAs in the propulsion subsystem 250, and each target speed setting corresponds to a respective control zone. In the presence of competing target settings for the WMA or group of WMAs, the state zone generation system 490 generates a state zone to resolve the multiple different competing target settings for the WMA or group of WMAs. Each state zone can have a unique setting resolver for resolving the competing target settings for the given WMA or group of WMAs. As Figure 14 illustrated, the agricultural harvester and the competing setting zone 733 are located in the state zone 732C. The state zone 732C has a human setting resolver, as indicated by the setting resolver indicator 721. Thus, the operator of the agricultural harvester can resolve the competing target speed setting in the competing setting zone 733 by selecting one of the target speed settings displayed in the selection display column 746, such as by actuating one of the user-actuatable selection display indicia 737. In the illustrated example, the operator of the agricultural harvester has selected, such as by user input, such as a touch input, and the target speed setting of 3.1 mph corresponds to the target speed setting for the crop state control zone. Thus, the WMA or group of WMAs in the propulsion subsystem 250 will be controlled to propel the agricultural harvester at 3.1 mph in the competing setting zone 733 based on the operator’s selection.

[0203] As previously mentioned, various other setting resolvers can be used or displayed, or both, such as artificial intelligence setting resolvers, machine learning setting resolvers, setting resolvers based on predictive or historical quality of the competing target settings, rule-based resolvers, performance criteria-based resolvers, and various other resolvers. It should be understood that machine speed is merely one example of a machine setting that can be recommended by different state zones and selected by setting resolvers for controlling operation of the agricultural harvester 100. Various other machine settings corresponding to various other actuators of the agricultural machine 100 can also be recommended by various state zones and selected by various setting resolvers.

[0204] Furthermore, while the state zones are illustrated as being displayed in the illustrated example, it should be understood that control zones can also be displayed in lieu of or in addition to the state zones. Furthermore, while the state zones are illustrated as being displayed in the illustrated example, it should be understood that control zones can also be displayed in lieu of or in addition to the state zones. Figure 14 Figure 15 ​The state regions shown in the figures have a particular number, shape, and size, but it should be understood that any number of state regions can be displayed having various shapes and sizes. Further, as previously described herein, the state regions can overlap at one or more locations on the field.

[0205] As such, a figure is obtained by an agricultural harvester and the figure shows machine speeds at different geographic locations of a field being harvested. On-board sensors sense characteristics as the agricultural harvester moves through the field. A prediction figure generator generates a prediction figure that predicts control values for different locations in the field based on values for machine speeds in the figure and the characteristics sensed by the on-board sensors. A control system controls controllable subsystems based on the control values in the prediction figure.

[0206] A control value is a value on which an action can be based. As described herein, a control value can include any value (or a characteristic indicated by or derived from the value) that can be used to control the agricultural harvester 100. A control value can be any value that is indicative of an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value can include any value provided by a figure, such as any one of the figures described herein. For example, a control value can be a value provided by an information figure, a value provided by a priori information figure, or a value provided by a prediction figure, such as a functional prediction figure. A control value can also include any one of the characteristics indicated by or derived from a value detected by any one 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.

[0207] The current discussion has mentioned processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not separately shown). Processors and servers are functional parts of systems or devices to which they belong, and their functions are activated and facilitated by other components or items in those systems.

[0208] Moreover, a number of user interface displays have been discussed. The displays can take a variety of different forms, and can have a variety of different user-actuatable operator interface structures disposed thereon. For example, the user-actuatable operator interface mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-actuatable operator interface mechanisms can also be actuated in a variety of different ways. For example, the user-actuatable operator interface mechanisms can be actuated using an operator interface mechanism such as a pointing device, (such as a trackball or mouse, a hardware button, a switch, a joystick or keypad, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuators. Moreover, 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, the user-actuatable operator interface mechanisms can be actuated using voice commands that utilize voice recognition functionality. Voice recognition can be implemented using a voice detection device such as a microphone and software for recognizing detected voice and executing commands based on received voice.

[0209] A number of data stores have also been discussed. It should be noted that the data stores can each be divided into a number of data stores. In some examples, one or more of the data stores can be local to the system accessing the data store, all of the data stores can be located remotely from the system utilizing the data stores, or one or more of the data stores can be local while others are remote. All of these configurations are contemplated by the present disclosure.

[0210] Furthermore, the drawings illustrate a number of blocks that can be represented by a number of different components. It will be understood that the functions attributed to these different blocks can be performed by fewer components, or by more components, in different examples. In some examples, some of the functions can be added, and in other examples, some of the functions can be removed.

[0211] It should be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware items, such as processors, memories, or other processing components, including but not limited to artificial intelligence components (such as neural networks, some of which are described below) that perform functions associated with those systems, components, logic, or interactions. Moreover, any or all of the systems, components, logic, and interactions can be implemented by software that is loaded into memory and subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions can 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 can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

[0212] Figure 2 is a block diagram of an agricultural harvester 600, which can be similar to the agricultural harvester 100 shown in Figure 2 The agricultural harvester 600 communicates with elements in the remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services that do not require end users to be aware of the physical location or configuration of the system that is delivering the services. In various examples, the remote server can deliver services over a wide area network, such as the Internet, using appropriate protocols. For example, the remote server can deliver an application over a wide area network, and the application can be accessed through a web browser or any other computing component. Figure 15 The software or components shown in and the data associated therewith can be stored on servers at remote locations. Computing resources in the remote server environment can be consolidated at a remote data center location, or the computing resources can be dispersed to multiple remote data centers. The remote server infrastructure can deliver services through a shared data center, even though the services appear as a single access point to the user. Thus, the components and functionality described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functionality can be provided from a server, or the components and functionality can be installed directly or otherwise on a client device.

[0213] Figure 2 In the example shown in Figure 15 some items are similar to the items shown in Figure 15 and are similarly numbered. Figure 15In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0214] Figure 15 Another example of a remote server architecture is also described. Figure 2 It shows Figure 2 Some components can be located at remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be placed at a location separate from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the combine harvester 600 via a network (such as a wide area network or local area network); these components can be hosted on a remote site, provided as a service, or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to or by an operator, user, or system. For example, a physical carrier can be used in addition to a physical carrier. In some examples, where wireless telecommunications service coverage is poor or absent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches the machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. Then, when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available, the collected information can be forwarded to another network. For example, when a fuel truck travels to a location to refuel other machines or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, information can be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can transmit the information to another network.

[0215] It will also be noted that Figure 16 The components or parts thereof can be located on a variety of different devices. One or more of these devices may include an airborne computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

[0216] In some examples, the remote server architecture 500 can include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and use of ledgers for recording metadata, data, data transfers, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as blockchains).

[0217] Figures 17-18 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a handheld device 16 of a user or customer in which the present system (or a portion thereof) can be deployed. For example, the mobile device can be deployed in an operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the graphs discussed above. Figure 16 is an example of a handheld or mobile device.

[0218] Figure 2 A general block diagram of components of a client device 16 is provided that can operate Figure 17 Some of the components shown in FIG. 1, interact 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 in some examples the communications link 13 provides a channel for automatically receiving information (e.g., by scanning). Examples of the communications link 13 include allowing communication over one or more communication protocols, such as wireless services for providing cellular access to a network, and a contract for providing local wireless connectivity to a network.

[0219] In other examples, the application can be received on a removable Secure Digital (SD) card that connects to an interface 15. The interface 15 and the communications link 13 communicate with a processor 17 (which can also implement the processor or server from other figures) along a bus 19 that is also connected to memory 21 and input / output (I / O) components 23, as well as a clock 25 and a location system 27.

[0220] In one example, the I / O components 23 are provided to facilitate input and output operations. The I / O components 23 of 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 display devices, speakers, and / or printer ports. Other I / O components 23 can also be used.

[0221] The clock 25 illustratively includes a real-time clock component that outputs time and date. Illustratively, it can also provide timing functions for the processor 17.

[0222] The location system 27 illustratively includes components that output a current geographic location of the device 16. This can include, for example, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. The location system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigational routes, and other geographic functions.

[0223] The memory 21 stores an operating system 29, network settings 31, applications 33, application configuration settings 35, data stores 37, communication drivers 39, and communication configuration settings 41. The memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. The memory 21 can also include computer storage media (described below). The memory 21 stores computer readable instructions that, when executed by the processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. The processor 17 can also be activated by other components to facilitate their functions.

[0224] Figure 17 One example is shown in which the device 16 is a tablet computer 600. In Figure 18 The computer 601 is shown with a user interface display screen 602. The screen 602 can be a touch screen that receives input from a pen or a touch pen, or a pen-enabled interface. The tablet computer 600 can also use an on-screen virtual keyboard. Of course, the computer 601 can also be attached to a keyboard or other user input device, e.g., by a suitable attachment structure such as a wireless link or a USB port. The computer 601 can also illustratively receive voice input.

[0225] Figure 17 Similar to Figure 19 , except that the device is a smart phone 71. The smart phone 71 has a touch sensitive display 73 that displays icons or widgets or other user input mechanisms 75. The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. Generally, the smart phone 71 is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone.

[0226] Note that other forms of the device 16 are possible.

[0227] Figure 2 is one example of a computing environment in which Figure 19 elements can be deployed. Reference is made to Figure 2An example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. The components of computer 810 can include, but are not limited to, a processing unit 820 (which can include a processor or server from the previous figures), a system memory 830, and a system bus 821 to couple the various system components including the system memory to the processing unit 820. The system bus 821 can 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. Regarding Figure 19 The described memory and programs can be deployed in Figure 19 corresponding portions of

[0228] The computer 810 typically includes a variety of computer readable media. Computer readable media can 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 can comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It 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 can embody computer readable instructions, data structures, program modules or other data in a modulated data signal or carrier wave. 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.

[0229] The system memory 830 includes computer storage media in the form of volatile and / or nonvolatile memory 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 the computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data and / or program modules that are immediately accessible to and / or presently being operated on by processing unit 820. By way of example, and not limitation, as Figure 19 Operating system 834, application programs 835, other program modules 836, and program data 837 are shown.

[0230] The computer 810 can also include other removable / non-removable volatile / nonvolatile computer storage media. By way of example, and not limitation, Figure 19 Hard disk drive 841 is shown as storing at least one program of instruction and / or data 844 from non-removable, non-volatile magnetic media, flash drive 855 and volatile memory 856. Hard disk drive 841 is typically connected to system bus 821 via non-removable memory interface, such as interface 840, and flash drive 855 is typically connected to system bus 821 via removable memory interface, such as interface 850.

[0231] 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 (ASICs), Application-specific Standard Products (such as ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0232] The drives and their associated computer storage media discussed above and illustrated in Figure 19 provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. In this regard, the ​In the diagram, hard disk drive 841 is shown storing operating system 844, application program 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application program 835, other program modules 836, and program data 837.

[0233] Users can input commands and information to computer 810 through input devices such as keyboard 862, microphone 863, and pointing devices 861 (such as mouse, trackball, or touchpad). Other input devices (not shown) may include joysticks, game keyboards, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860 coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0234] Computer 810 operates in a networked environment using logical connections (such as Controller Area Network (CAN), Local Area Network (LAN), or Wide Area Network (WAN)) to one or more remote computers (such as remote computer 880).

[0235] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other means for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 19 shows that a remote application 885 can reside on a remote computer 880.

[0236] It should also be noted that the different examples described in this article 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 considered in this article.

[0237] Example 1 is a method for controlling agricultural machinery, including:

[0238] Access graph, the graph having a set of state regions defined on the graph, each state region having a corresponding setting parser;

[0239] Detecting the geographical location of agricultural machinery in the field;

[0240] identifying a plurality of different target actuator settings corresponding to a geographic location of the agricultural work machine;

[0241] identifying a state region based on the geographic location of the agricultural work machine;

[0242] selecting one of the plurality of different target actuator settings as a resolved target actuator setting based on a settings resolver corresponding to the identified state region; and

[0243] controlling an actuator of the agricultural work machine based on the resolved target actuator setting.

[0244] Example 2 is the method of any or all preceding examples and further comprising:

[0245] identifying a settings resolver corresponding to the identified state region.

[0246] Example 3 is the method of any or all preceding examples, wherein identifying the settings resolver comprises:

[0247] identifying a human settings resolver as the settings resolver corresponding to the state region; and

[0248] displaying the plurality of different target actuator settings on a user interface display.

[0249] Example 4 is the method of any or all preceding examples, wherein selecting one of the plurality of different target actuator settings comprises:

[0250] detecting, via the user interface display, an operator input selecting one of the displayed plurality of different target actuator settings as the resolved target actuator setting.

[0251] Example 5 is the method of any or all preceding examples, wherein identifying the settings resolver comprises:

[0252] executing one of an artificial intelligence component, a machine learning component, and an artificial neural network component to identify the resolved target actuator setting.

[0253] Example 6 is the method of any or all preceding examples, wherein identifying the settings resolver comprises:

[0254] executing a rule-based settings resolver to identify the resolved target speed setting.

[0255] Example 7 is the method of any or all preceding examples, wherein identifying the state region based on the geographic location of the agricultural work machine comprises:

[0256] identifying a plurality of different overlapping state regions.

[0257] Example 8 is the method of any or all preceding examples and further comprising:

[0258] accessing a priority level of the state region; and

[0259] selecting one of the plurality of different overlapping state regions as the identified state region based on a position in the priority level of the state region of each of the plurality of different overlapping state regions.

[0260] Example 9 is the method of any or all preceding examples, wherein the map includes a set of control regions defined on the map, each control region including a corresponding target actuator setting, and

[0261] wherein identifying the plurality of different target actuator settings includes:

[0262] identifying a plurality of different control regions based on a geographic location of the agricultural work machine; and

[0263] identifying the target actuator settings corresponding to the identified plurality of different control regions as the plurality of different target actuator settings.

[0264] Example 10 is the method of any or all preceding examples, wherein identifying the plurality of different target actuator settings includes:

[0265] identifying the target actuator settings from a plurality of different sources as the plurality of different target actuator settings, the plurality of different sources including at least one of a field sensor, an operator input, and an input received from a different agricultural work machine.

[0266] Example 11 is an agricultural work machine comprising:

[0267] a controllable actuator subsystem;

[0268] a geographic location sensor that detects a geographic location of the agricultural work machine in a field;

[0269] a region controller that accesses a map including a set of state regions defined on the map, each state region including a corresponding setting resolver, the region controller identifying a plurality of different target actuator settings corresponding to the geographic location of the agricultural work machine, identifying a state region based on the geographic location of the agricultural work machine, and selecting one of the plurality of different target actuator settings as a resolved target setting based on the setting resolver corresponding to the identified state region; and

[0270] a setting controller that controls the actuator subsystem of the agricultural work machine based on the resolved target actuator setting.

[0271] Example 12 is the agricultural work machine of any or all preceding examples, wherein the zone controller is configured to, prior to selecting one of the plurality of different target actuator settings as the resolved target actuator setting, identify a setting resolver corresponding to the identified state zone.

[0272] Example 13 is the agricultural work machine of any or all preceding examples, and further comprising:

[0273] a user interface display, wherein the zone controller is configured to identify the setting resolver by identifying a human setting resolver as the setting resolver corresponding to the state zone, and to display the plurality of different target actuator settings on the user interface display.

[0274] Example 14 is the agricultural work machine of any or all preceding examples, and further comprising:

[0275] an operator interface controller configured to detect an operator input via the user interface display, the operator input selecting one of the displayed plurality of different target actuator settings as the resolved target actuator setting.

[0276] Example 15 is the agricultural work machine of any or all preceding examples, wherein the setting resolver comprises at least one of an artificial intelligence component, a machine learning component, and an artificial neural network component.

[0277] Example 16 is the agricultural work machine of any or all preceding examples, wherein the setting resolver comprises a rules-based setting resolver.

[0278] Example 17 is the agricultural work machine of any or all preceding examples, wherein the map comprises a plurality of different overlapping state zones, and wherein the zone controller is configured to access a priority ranking of the state zones and select one of the plurality of different overlapping state zones as the identified state zone based on a position in the priority ranking of the state zone of each of the plurality of different overlapping state zones.

[0279] Example 18 is the agricultural work machine of any or all preceding examples, wherein the map comprises a set of control zones defined on the map, each control zone comprising a corresponding target actuator setting, and wherein the zone controller identifies a plurality of different control zones based on a geographic location of the agricultural work machine and identifies the target actuator settings corresponding to the identified plurality of different control zones as the plurality of different target actuator settings.

[0280] Example 19 is the agricultural work machine of any or all preceding examples, and further comprising:

[0281] a field sensor that generates a sensor signal indicative of a sensed characteristic;

[0282] an operator input mechanism that generates an operator input signal based on operator input; and

[0283] a communication system that receives communications from a remote work machine, and

[0284] wherein the zone controller identifies a plurality of different target actuator settings based on at least one of the sensor signal, the operator input signal, and the communications received from the remote work machine.

[0285] Example 20 is a method of controlling an agricultural work machine, comprising:

[0286] accessing a graph that includes a set of state zones defined on the graph, each state zone including a corresponding settings resolver;

[0287] detecting a geographic location of the agricultural work machine in a field;

[0288] identifying a plurality of different target settings corresponding to the geographic location of the agricultural work machine;

[0289] identifying a state zone based on the geographic location of the agricultural work machine;

[0290] selecting one of the plurality of different target settings as a resolved target setting based on the settings resolver corresponding to the identified state zone; and

[0291] controlling a controllable subsystem of the agricultural work machine based on the resolved target setting.

[0292] 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 implementing the claims.

Claims

1. A method of controlling an agricultural work machine, comprising: accessing a map having a set of state regions defined thereon, each state region having a corresponding settings resolver; detecting a geographic position of the agricultural work machine in a field; identifying a first target actuator setting and a second target actuator setting for an actuator corresponding to the geographic position of the agricultural work machine, wherein the first target actuator setting and the second target actuator setting conflict; identifying a state region based on the geographic position of the agricultural work machine; selecting one of the first target actuator setting and the second target actuator setting for the actuator as a resolved target actuator setting based on the settings resolver corresponding to the identified state region; and controlling the actuator of the agricultural work machine based on the resolved target actuator setting.

2. The method of claim 1, further comprising: identifying the settings resolver corresponding to the identified state region, wherein identifying the settings resolver comprises: identifying a human settings resolver as the settings resolver corresponding to the state region; and displaying the first target actuator setting and the second target actuator setting on a user interface display. selecting one of the first target actuator setting and the second target actuator setting comprises:

3. The method of claim 2, wherein, detecting an operator input via the user interface display, the operator input selecting one of the first target actuator setting and the second target actuator setting as the resolved target actuator setting.

4. The method of claim 1, further comprising: identifying a plurality of different overlapping state regions; accessing a priority level of a state region; and selecting one of the plurality of different overlapping state regions as the identified state region based on a position in the priority level of a state region in each of the plurality of different overlapping state regions. the map includes a first control region and a second control region defined thereon, the first control region defining the first target actuator setting and the second control region defining the second target actuator setting, and 5. The method of claim 1, wherein, wherein identifying the first target actuator setting and the second target actuator setting comprises: identifying that the first control region and the second control region correspond to the geographic position of the agricultural work machine; and identifying the first target actuator setting and the second target actuator setting based on the first control region and the second control region corresponding to the geographic position of the agricultural work machine.

6. An agricultural work machine, comprising: a controllable actuator subsystem; a geographic position sensor that detects a geographic position of the agricultural work machine in a field; ​ a zone controller that accesses a map, the map including a set of state zones defined on the map, each state zone including a corresponding settings resolver, the zone controller identifying a plurality of different conflicting target actuator settings for the controllable actuator subsystem corresponding to the geographic location of the agricultural work machine, identifying a state zone based on the geographic location of the agricultural work machine, and selecting one of the plurality of different conflicting target actuator settings for the controllable actuator subsystem as a resolved target actuator setting based on the settings resolver corresponding to the identified state zone; and a settings controller that controls the controllable actuator subsystem of the agricultural work machine based on the resolved target actuator setting. The zone controller is configured to, prior to selecting one of the plurality of different conflicting target actuator settings as a resolved target actuator setting, identify a settings resolver corresponding to the identified state zone, and the agricultural work machine further includes:

7. The agricultural work machine of claim 6, wherein, a user interface display, wherein the zone controller is configured to identify the settings resolver by identifying a human settings resolver as the settings resolver corresponding to the state zone, and to display the plurality of different conflicting target actuator settings on the user interface display; and an operator interface controller configured to detect an operator input via the user interface display, the operator input selecting one of the displayed plurality of different conflicting target actuator settings as the resolved target actuator setting. The map includes a plurality of different overlapping state zones, and wherein the zone controller is configured to access a priority level of a state zone, and to select one of the plurality of different overlapping state zones as the identified state zone based on a position in the priority level of a state zone of each of the plurality of different overlapping state zones.

8. The agricultural work machine of claim 6, wherein, The map includes a set of control zones defined on the map, each control zone including a corresponding target actuator setting, and wherein the zone controller identifies a plurality of different control zones of the set of control zones corresponding to the geographic location of the agricultural work machine, and identifies the target actuator settings corresponding to the plurality of different control zones identified as corresponding to the geographic location of the agricultural work machine as the plurality of different conflicting target actuator settings.

9. The agricultural work machine of claim 6, wherein, 10. A method of controlling an agricultural work machine, comprising: accessing a map, the map including a set of state zones defined on the map, each state zone including a corresponding settings resolver; detecting a geographic location of the agricultural work machine in a field; identifying a plurality of different target settings for a controllable subsystem corresponding to the geographic location of the agricultural work machine, wherein the plurality of different target settings for the controllable subsystem conflict; identifying a state zone based on the geographic location of the agricultural work machine; ​ selecting, based on the setting resolver corresponding to the identified state region, one of the plurality of different target settings for the controllable subsystem as a resolved target setting; and controlling the controllable subsystem of the agricultural work machine based on the resolved target setting.

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