Machine control using prediction maps

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses an agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map based on a relationship between values in the one or more information maps and the agricultural property sensed by the on-board sensor, the prediction map predicting a predicted agricultural property at different locations in the field. The prediction map can be output and used for automated machine control.

Description

TECHNICAL FIELD

[0001] This description relates to agricultural machines, forestry machines, construction machines, and turf management 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 be fitted with different types of headers to harvest different types of crops.

[0003] Terrain characteristics can have many detrimental effects on a harvesting operation. For example, when a harvester is travelling over a sloped feature, the pitch or roll of the harvester can impede the performance of the harvester. As a result, when a slope is encountered during a harvesting operation, an operator can attempt to modify the control of the harvester.

[0004] The discussion above is provided merely for general background information and is not intended to aid in the determination of the scope of the subject matter claimed. SUMMARY

[0005] One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. A field sensor on the agricultural work machine senses an agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map that predicts a predicted agricultural property at different locations in the field based on a relationship between values in the one or more information maps and the agricultural property sensed by the field sensor. The prediction map can be output and used for automated machine control.

[0006] This summary is provided to introduce a selection of concepts 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 limit the scope of the claimed subject matter. The claimed subject matter is not limited to addressing any or all of the disadvantages mentioned in the background. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is a partially pictorial, partially schematic diagram of portions of a combine harvester, which is one example of an agricultural machine.

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

[0009] Figures 3A-3B (Figures 3, collectively referred to herein as Figure 3) illustrate a flowchart that illustrates an example of the operation of an agricultural harvester in generating a map.

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

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

[0012] Figure 6 is a block diagram illustrating one example of a control zone generator.

[0013] Figure 7 is a block diagram illustrating Figure 6 is a flowchart illustrating one example of operation of the control zone generator illustrated in

[0014] Figure 8 is a flowchart illustrating one example of operation of a control system in selecting a target setpoint value to control an agricultural harvester.

[0015] Figure 9 is a block diagram illustrating one example of an operator interface controller.

[0016] Figure 10 is a flowchart illustrating one example of an operator interface controller.

[0017] Figure 11 is an illustration showing one example of an operator interface display.

[0018] Figure 12 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment.

[0019] Figures 13-15 is an example of a mobile device that can be used in an agricultural harvester.

[0020] Figure 16 is a block diagram illustrating one example of a computing environment that can be used in an agricultural harvester. DETAILED DESCRIPTION

[0021] To facilitate an understanding of the principles of the present disclosure, reference is made to the examples described herein and illustrated in the drawings, and specific language will be used to describe the same. It will, however, be understood that no limitation of the scope of the disclosure is intended. Alterations and further modifications of the described devices, systems, methods, and any additional or additional applications of the principles of the present disclosure are fully contemplated as would be apparent to one of ordinary skill in the relevant art. 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.

[0022] This specification relates to using live data acquired contemporaneously with agricultural operations, in combination with prior data, to generate a prediction map, such as a prediction conveyor speed map. In some examples, the prediction map can be used to control the speed of one or more conveyors on an agricultural harvesting machine.

[0023] As discussed above, when an agricultural harvester engages a terrain feature, such as a side slope, the performance of the agricultural harvester can be degraded. For example, when an agricultural harvester is traveling on a side slope, one half of the header will be feeding cut material down the slope and the other half of the header will be feeding cut material up the slope. When both conveyors are kept at the same speed in this situation, the material being pushed down the slope can ride over the center belt that is directing the material into the feeder house. This can result in grain loss or even in some cases a plugged header.

[0024] Alternatively, for example, when an agricultural harvester is traveling up and down a slope, the ground travel speed of the harvester can change. This change in speed has an impact on the amount of vegetation encountered by the harvester in a given time. This increased amount of vegetation can require an increase in the conveyor speed to reduce the likelihood of material plugging the header.

[0025] A topography map illustratively maps the elevation of the ground across different geographic locations in a field of interest. Since ground slope indicates a change in elevation, two or more elevation values allow for the calculation of a slope between regions having known elevation values. Greater granularity of slope can be achieved by having more regions with known elevation values. When an agricultural harvester travels across a terrain in a known direction, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., the regions of changing elevation). When referred to hereinafter, topographical characteristics can include, but are not limited to, elevation, slope (e.g., including machine orientation relative to a slope), and ground profile (e.g., roughness).

[0026] Accordingly, the present discussion continues with a system that receives a map generated during a prior operation or prior information map of a field and also uses live sensors to detect variables indicative of one or more agricultural characteristics during a harvesting operation. The system generates a model that models the relationship between values of the prior information map and output values from the live sensors. The model is used to generate a functional prediction map that predicts, for example, a conveyor speed at different locations in a field. The functional prediction map generated during a harvesting operation can be presented to an operator or other user during the harvesting operation or used to automatically control the agricultural harvester, or both. The functional prediction map can be used to control the speed of one or more conveyors.

[0027] Figure 1 is a partially pictorial, partially schematic illustration of portions of a self-propelled agricultural harvester 100. In the illustrated example, the agricultural harvester 100 is a combine harvester. Additionally, while a combine harvester is provided as an example throughout this disclosure, it will be understood that the present description also applies to other types of harvesters, such as a cotton harvester, a sugarcane harvester, a self-propelled forage harvester, a swather, 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 a combine harvester. Furthermore, the present disclosure relates to other types of work machines that can be suitable for generating a prediction map, such as agricultural planters and sprayers, construction equipment, forestry equipment, and turf management equipment. Thus, the present disclosure is intended to encompass these various types of harvesters and other work machines, and is therefore not limited to a combine harvester.

[0028] As shown in Figure 1 The agricultural harvester 100 illustratively includes an operator cab 101 that can have various different operator interface mechanisms to control the agricultural harvester 100. The agricultural harvester 100 includes a set of front end equipment, such as a header 102 and a cutter 104. In the illustrated example, the cutter 104 is included on the header 102. The agricultural harvester 100 also includes a feeder housing 106, a feeder accelerator 108, and a threshing machine, generally indicated as 110. The feeder housing 106 and the feeder accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotably coupled to a frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive movement of the header 102 about the axis 105 in a direction generally indicated by arrow 109. Thus, a vertical position of the header 102 above the ground 111 (header height) on which the header 102 travels is controllable by actuation of 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 tilt angle, a roll angle, or both a tilt angle and a roll angle to the header 102 or portions of the header 102, although not shown in

[0029] 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 grain cleaning subsystem or cleaner (collectively referred to as the grain cleaning subsystem 118) that includes a grain cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes an unloading beater 126, a tailings elevator 128, a clean grain elevator 130, and an unloading auger 134 and spout 136. The clean grain elevator moves clean grain into a clean grain bin 132. The agricultural harvester 100 also includes a residue subsystem 138 that can include a chopper 140 and a spreader 142. The combine harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure can have more than one of any of the subsystems mentioned above. In some examples, the agricultural harvester 100 can have left and right grain cleaning subsystems, separators, etc., which are not shown in Figure 1 FIG. 1.

[0030] In operation, and by way of overview, 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. Operator commands are commands from the operator. The operator of the agricultural harvester 100 can determine one or more of a height setting, a tilt 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 greater detail below. The control system can also receive settings from the operator to establish the tilt angle and roll angle of the header 102 and implement the input settings by controlling the associated actuators (not shown) that change the tilt angle and roll angle 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 the desired tilt angle and roll angle. Each of the height setting, roll setting, and tilt setting can be implemented independently of the others. The control system responds to header errors (e.g., a difference between the height setting and a measured height of the header 102 above the ground 111, and, in some examples, tilt angle errors and roll angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set to a higher sensitivity level, the control system responds to smaller header position errors and attempts to reduce detected errors more quickly than when the sensitivity is at a lower sensitivity level.

[0031] 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 housing 106 by a conveyor toward the feed accelerator 108, which accelerates the crop material into the threshing machine 110. The crop material is threshed by the crop being tumbled against the concave 114 by the rotor 112. The threshed crop material moves through a separator rotor in the separator 116, with a portion of the residue moving through the unloading beater 126 toward the residue subsystem 138. The residue portion passed 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 discharged from the agricultural harvester 100 in a pile. In other examples, the residue subsystem 138 can include a weed seed eliminator (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0032] The grain falls to the clean grain subsystem 118. The chaffer 122 separates some of the larger pieces of material from the grain, and the screen 124 separates some of the finer pieces of material from the clean grain. The clean grain falls to a screw conveyor that moves the clean 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 clean grain subsystem 118 by the airflow generated by the clean grain fan 120. The clean grain fan 120 directs air up through the screen and the chaffer along an airflow path. The airflow carries residue back in the agricultural harvester 100 toward the residue handling subsystem 138.

[0033] The tailings elevator 128 returns the tailings to the threshing machine 110, where the tailings are re-threshed. Alternatively, the tailings can also be carried by the tailings elevator or another transport device to a separate re-threshing mechanism where the tailings are also re-threshed.

[0034] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a machine speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151 (which can be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the clean grain subsystem 118.

[0035] 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, the speed of travel can also be sensed using a positioning system such as a global positioning system (GPS), a dead reckoning system, a long-range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of the speed of travel.

[0036] The loss sensors 152 illustratively provide output signals indicative of the amount of grain loss occurring on the right and left sides of the clean grain subsystem 118. In some examples, the sensors 152 are knock sensors that count the number of grain knocks per unit of time or per unit of travel distance to provide an indication of the amount of grain loss occurring at the clean grain subsystem 118. The knock sensors on the right and left sides of the clean grain subsystem 118 can provide separate signals or a combined or aggregate signal. In some examples, the sensors 152 can include a single sensor, as opposed to providing a separate sensor for each clean grain subsystem 118.

[0037] The separator loss sensors 148 provide output signals indicative of the amount of grain loss occurring on the left and right sides of the threshing machine 110. In some examples, the sensors 148 are knock sensors that count the number of grain knocks per unit of time or per unit of travel distance to provide an indication of the amount of grain loss occurring at the threshing machine 110. The knock sensors on the right and left sides of the threshing machine 110 can provide separate signals or a combined or aggregate signal. In some examples, the sensors 148 can include a single sensor, as opposed to providing a separate sensor for each threshing machine 110.Figure 1 The separator loss sensors 148 can be associated with the left and right separators and can provide separate grain loss signals, or a combined or aggregate signal. In some cases, sensing grain loss in the separators can also be performed using a variety of different types of sensors as well.

[0038] The agricultural harvester 100 can also include other sensors and measurement mechanisms. For example, the agricultural harvester 100 can include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stabilization sensor that senses the oscillating or bouncing motion (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, create a windrow, etc.; a cleaner fan speed sensor that senses the speed of the fan 120; a concave gap sensor that senses the gap between the rotor 112 and the concave 114; a threshing rotor speed sensor that senses the rotor speed of the rotor 112; a chaffer screen gap sensor that senses the size of the openings in the chaffer screen 122; a screen mesh gap sensor that senses the size of the openings in the screen mesh 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the 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 the 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 is also configured to sense the properties of the severed crop material as it is being processed by the agricultural harvester 100. For example, in some cases, the crop property sensor can sense: grain quality, such as cracked grain, MOG levels; grain constituents, such as starch and protein; and grain feed rate as the grain travels through the feeder housing 106, the clean grain elevator 130, or other places in the agricultural harvester 100. The crop property sensor can also sense the feed rate of biomass through the feeder housing 106, through the separator 116, or other places in the agricultural harvester 100. The crop property sensor can also sense the mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvester 100, or provide other output signals indicative of other sensed variables.

[0039] Before describing how the agricultural harvester 100 generates a functional predicted conveyor speed map and uses the functional predicted conveyor speed map for control, a brief description of some of the items on the agricultural harvester 100 and their operation will first be described. Figure 2The plot of FIG. 3 describes receiving a previous information map of a general type and combining information from the previous information map with georeferenced sensor signals generated by on-site sensors, where the sensor signals are indicative of characteristics in the field, such as characteristics of crops or weeds present in the field. 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; characteristics of crop properties, such as crop height, crop moisture, crop density, crop status; characteristics of grain properties, such as grain moisture, grain size, grain test weight; and characteristics of machine performance, such as loss level, work quality, fuel consumption, and power utilization. Relationships between characteristic values obtained from the on-site sensor signals and the previous information map values are identified, and the relationships are used to generate a new functional prediction map. The functional prediction map predicts values at different geographic locations in the field, and one or more of these values can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, the functional prediction map can be presented to a user, such as an operator of an agricultural work machine, which can be an agricultural harvester. The functional prediction map can be presented to the user visually, such as by a display, haptically, or aurally. The user can interact with the functional prediction map to perform editing operations and other user interface operations. In some cases, the functional prediction map can be used to control an agricultural work machine, such as an agricultural harvester, presented to an operator or other user, and presented to an operator or user to facilitate one or more of operator or user interaction.

[0040] In reference to the overall method described in reference to Figure 2 and FIG. 3, reference is made to Figure 4 and Figure 5 A more specific method for generating a functional prediction conveyor speed map is described, which can be presented to an operator or user, or used to control an agricultural harvester 100, or both. Furthermore, while the present discussion continues to be directed to agricultural harvesters (and particularly combine harvesters), the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

[0041] Figure 2 is a block diagram showing some portions of an example agricultural harvester 100. Figure 2It is shown that the agricultural harvester 100 illustratively includes one or more processors or servers 201, a data store 202, a geo-location sensor 204, a communication system 206, and one or more in-situ sensors 208 that sense one or more agricultural properties of the field contemporaneously with the harvesting operation. Agricultural properties can include any property that can have an influence on the harvesting operation. Some examples of agricultural properties include properties of the harvester, the field, the plants on the field, the weather. Other types of agricultural properties are included. The in-situ sensors 208 generate values corresponding to the sensed properties. 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 subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 can also include a wide variety of other agricultural harvester functionality 220. For example, the in-situ sensors 208 include on-board sensors 222, remote sensors 224, and other sensors 226 that sense properties of the field during the course of the agricultural operation. The predictive model generator 210 illustratively includes a prior information variable-to-in-situ 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 conveyor belt controller 240, a table deck position controller 242, a residue system controller 244, a machine cleanout controller 245, a zone controller 247, and the system 214 can include other items 246. The controllable subsystems 216 include machine and header actuators 248, a propulsion subsystem 250, a steering subsystem 252, a residue subsystem 138, a machine cleanout subsystem 254, and the subsystems 216 can include a wide variety of other subsystems 256.

[0042] Figure 2 It is also shown that the agricultural harvester 100 can receive a prior information map 258. As described below, the prior information map 258 includes, for example, a topographical map. However, the prior information map 258 can also encompass other types of data obtained prior to the harvesting operation, or maps from prior operations. Figure 2It is also shown that an operator 260 can operate the agricultural harvester 100. The operator 260 interacts with the operator interface mechanism 218. In some examples, the operator interface mechanism 218 can include joysticks, levers, steering wheels, linkage mechanisms, pedals, buttons, dials, keyboards, user-actuatable elements on user interface display devices such as icons, buttons, etc., microphones and speakers with voice recognition and speech synthesis provided, and a wide variety of other types of control devices. Where a touch-sensitive display system is provided, the operator 260 can interact with the operator interface mechanism 218 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Thus, other types of operator interface mechanisms 218 can be used and are within the scope of the present disclosure.

[0043] The prior information map 258 can be downloaded using the communication system 206 or otherwise onto the agricultural harvester 100 and stored in the data storage 202. In some examples, the communication system 206 can be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, a communication system configured to communicate over any of a variety of other networks or a combination of networks. The communication system 206 can also include a system that facilitates downloading or transferring information to and from a secure digital (SD) card or a universal serial bus (USB) card or both a secure digital (SD) card and a universal serial bus (USB) card.

[0044] The geo-location sensor 204 illustratively senses or detects a geo-location or position of the agricultural harvester 100. The geo-location sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geo-location sensor 204 can also include a real-time kinematic (RTK) component configured to improve the accuracy of position data derived from GNSS signals. The geo-location sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geo-location sensors.

[0045] The field sensors 208 can be any of the field sensors described above with reference to Figure 1Any of the described sensors. The field sensors 208 include on-board sensors 222 that are mounted on-board the agricultural harvester 100. Such sensors can include, for example, optical sensors such as cameras. The field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the harvester or data acquired by any sensor that detects data during a harvesting operation.

[0046] The prediction model generator 210 generates a model that indicates a relationship between values sensed by the field sensors 208 and values mapped to the field by the prior information map 258. For example, if the prior information map 258 maps terrain characteristic values to different locations in the field, and the field sensors 208 are sensing values indicative of a conveyor speed, the model of prior information variables to field variables generator 228 generates a predictive conveyor speed model that models a relationship between the terrain characteristic and the conveyor speed. The predictive conveyor speed model can also be generated based on terrain characteristic values from the prior information map 258 and a plurality of field data values generated by the field sensors 208. The prediction map generator 212 uses the predictive conveyor speed model generated by the prediction model generator 210 to generate a functional predictive conveyor speed map that predicts values of the conveyor speed at different locations in the field based on the prior information map 258.

[0047] In some examples, the type of values ​​in Functional Prediction Chart 263 may be the same as the field data type sensed by Field Sensor 208. In some cases, the type of values ​​in Functional Prediction Chart 263 may have different units than the data sensed by Field Sensor 208. In some examples, the type of values ​​in Functional Prediction Chart 263 may be different from the data type sensed by Field Sensor 208, but related to the type of data sensed by Field Sensor 208. For example, in some examples, the data type sensed by Field Sensor 208 may indicate the type of values ​​in Functional Prediction Chart 263. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in the previous Infographic 258. In some cases, the data type in Functional Prediction Chart 263 may have different units than the data in Infographic 258. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in the previous Infographic 258, but related to the data type in the previous Infographic 258. For example, in some examples, the data type in the previous Infographic 258 may indicate the data type in Functional Prediction Chart 263. In some examples, the data type in the function prediction graph 263 is different from one or both of the field data type sensed by the field sensor 208 and the data type in the previous infographic 258. In some examples, the data type in the function prediction graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the previous infographic 258. In some examples, the data type in the function prediction graph 263 is the same as one of the field data type sensed by the field sensor 208 or the data type in the previous infographic 258, but different from the other.

[0048] like Figure 2 As shown, prediction map 264 is based on previous information values ​​at multiple locations in previous information map 258 and uses a prediction model to predict values ​​of sensed characteristics (sensed by field sensor 208) or characteristics related to said sensed characteristics at those locations across the field. For example, if prediction model generator 210 has already generated a prediction model indicating the relationship between terrain characteristics and conveyor belt speed, then given terrain characteristic values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting the values ​​of conveyor belt speed at those different locations across the field. The terrain characteristic values ​​at those locations obtained from the terrain map, and the relationship between terrain characteristics and conveyor belt speed obtained from the prediction model, are used to generate prediction map 264.

[0049] The following will now describe some changes in the data types mapped in the previous Infographic 258, the data types sensed by the field sensor 208, and the data types predicted in the prediction graph 264.

[0050] In some examples, the data type in the previous information map 258 is different from the data type sensed by the in-field sensor 208, and the data type in the prediction map 264 is the same as the data type sensed by the in-field sensor 208. For example, the previous information map 258 can be a vegetation index map, and the variable sensed by the in-field sensor 208 can be yield. Then, the prediction map 264 can be a predicted yield map that maps predicted yield values to different geographic locations in the field. In another example, the previous information map 258 can be a vegetation index map, and the variable sensed by the in-field sensor 208 can be crop height. Then, the prediction map 264 can be a predicted crop height map that maps predicted crop height values to different geographic locations in the field.

[0051] Further, in some examples, the data type in the previous information map 258 is different from the data type sensed by the in-field sensor 208, and the data type in the prediction map 264 is different from both the data type in the previous information map 258 and the data type sensed by the in-field sensor 208. For example, the previous information map 258 can be a vegetation index map, and the variable sensed by the in-field sensor 208 can be crop height. Then, the prediction map 264 can be a predicted biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the previous information map 258 can be a vegetation index map, and the variable sensed by the in-field sensor 208 can be yield. Then, the prediction map 264 can be a predicted speed map that maps predicted harvester speed values to different geographic locations in the field.

[0052] In some examples, the previous information map 258 is generated from a previous pass through the field during a previous operation, and the data type is different from the data type sensed by the in-field sensor 208, and the data type in the prediction map 264 is the same as the data type sensed by the in-field sensor 208. For example, the previous information map 258 can be a seed population map generated during planting, and the variable sensed by the in-field sensor 208 can be stalk size. Then, the prediction map 264 can be a predicted stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, the previous information map 258 can be a seed mix map, and the variable sensed by the in-field sensor 208 can be crop status, such as standing crop or fallen crop. Then, the prediction map 264 can be a predicted crop status map that maps predicted crop status values to different geographic locations in the field.

[0053] In some examples, the previous information map 258 is generated from previous travel through the field during a previous 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 previous 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 mapping predicted yield values to different geographic locations in the field. In such an example, the georeferenced previous information map 258 and the relative yield differences from the previous year can be used by the prediction model generator 210 to generate a prediction model modeling the relationship between the relative yield differences on the previous information map 258 and the 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.

[0054] In some examples, the prediction map 264 can be provided to a control zone generator 213. The control zone generator 213 groups adjacent portions of the field into one or more control zones based on the data values of the prediction map 264 associated with those adjacent portions. A control zone can include two or more contiguous portions of a field, such as a field, for which a control parameter corresponding to a control zone for controlling a controllable subsystem is constant. For example, the response time for changing a setting of a controllable subsystem 216 can not be sufficient to respond satisfactorily to changes in values contained in a map, such as the prediction map 264. In that 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, a control zone can be sized to reduce wear and tear due to excessive actuator movement from continuous adjustments. In some examples, there can be different sets of control zones for each controllable subsystem 216 or for a group of controllable subsystems 216. The control zones can be added to the prediction map 264 to obtain a prediction control zone map 265. Thus, the prediction control zone map 265 can be similar to the prediction map 264 except that the prediction control zone map 265 includes control zone information defining control zones. Thus, a functional prediction map 263 as described herein 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 includes control zones, such as the prediction control zone map 265. In some examples, if an intercrop production system is implemented, multiple crops can be present in the field at the same time. In that case, the prediction map generator 212 and the control zone generator 213 can identify the locations and characteristics of two or more crops and then generate the prediction control zone map 265 and the prediction map 264 accordingly.

[0055] It will 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 and / or calibrate the agricultural harvester 100. 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.

[0056] 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 transmit the prediction map 264, the prediction control zone map 265, or both to other remote systems.

[0057] The operator interface controller 231 is operable to generate control signals for controlling 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 that are displayed and can be actuated by the operator to interact with the displayed maps. For example, the operator can edit the displayed map by correcting the conveyer belt speed displayed on the map based on the operator's observations. The settings controller 232 can generate control signals based on the prediction map 264, the prediction control zone map 265, or both, to control various settings of the agricultural harvester 100. For example, the settings controller 232 can generate control signals for controlling the machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave gap, rotor settings, clean grain fan speed settings, header height, header functions, reel speed, reel position, conveyor functions (where the agricultural harvester 100 is coupled to a conveyor header), corn header functions, in-crop distribution control, and other actuators 248 that affect other functions of the agricultural harvester 100. 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 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, when the agricultural harvester 100 approaches a weed patch having an intensity value above a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant feed rate of the biomass through the machine. The header and reel controller 238 can generate control signals to control the header or the reel or other header functions. The conveyer belt controller 240 can generate control signals to control the conveyer belt or other conveyor functions based on the prediction map 264, the prediction control zone map 265, or both. The table deck position controller 242 can generate control signals to control the position of a table deck included on the header based on the prediction map 264 or the prediction control zone map 265 or both.The residue system controller 244 can control the residue subsystem 138 based on the prediction map 264 or the prediction control zone map 265 or both. The machine cleanout controller 245 can generate control signals for controlling the machine cleanout subsystem 254. 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.

[0058] Figure 3A and Figure 3B FIG. 3 (collectively referred to herein as FIG. 3) shows a flowchart illustrating one example of operations 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.

[0059] At 280, the agricultural harvester 100 receives a previous information map 258. Examples of the previous information map 258 or receiving the previous information map 258 are discussed with reference to blocks 281, 282, 284, and 286. As discussed above, the previous information map 258 maps values of a variable corresponding to a first characteristic to different locations in the field, as indicated by block 282. As indicated by block 281, receiving the previous information map 258 can include selecting one or more of a plurality of available available previous information maps that are available. For example, one previous information map can be a vegetation index map generated from aerial imaging. Another previous information map can be a map generated during a previous pass through the field that can be performed by a different machine performing a previous operation in the field, such as a sprayer or other machine. The process of selecting one or more previous information maps can be manual, semi-automated, or automated. The previous 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 the previous year or early in the current growing season or during another time. The data can be based on data detected other than using aerial images. For example, the agricultural harvester 100 can be equipped with sensors, such as internal optical sensors, for identifying weed seeds exiting the agricultural harvester 100. The weed seed data detected by the sensors during harvesting the previous year can be used as data for generating the previous information map 258. The sensed weed data can be combined with other data to generate the previous information map 258. For example, based on the amount of weed seeds exiting the agricultural harvester 100 at different locations and based on other factors, such as whether the seeds are being spread by a spreader or falling into a pile; weather conditions, such as wind blowing when the seeds are falling or being spread; drainage conditions that can move the seeds around the field; or other information, the locations of those weed seeds can be predicted so that the previous information map 258 maps the predicted seed locations in the field. The data for the previous information map 258 can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data storage 202. The data for the previous information map 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, and this is indicated by block 286 in the flowchart of FIG. 3. In some examples, the previous information map 258 can be received by the communication system 206.

[0060] At the start of the harvesting operation, the field sensors 208 generate sensor signals indicative of one or more field data values indicative of a characteristic (e.g., a speed of a conveyor belt), as indicated by block 288. Examples of field sensors are discussed with reference to blocks 222, 290, and 226. As explained above, the field sensors 208 include on-board sensors 222, such as a camera, remote field sensors 224, such as a UAV-based sensor that collects field data on a single flight, shown in block 290, or other types of field sensors specified by field sensors 226. In some examples, data from on-board sensors is georeferenced using location, heading, or speed data from the geolocation sensors 204.

[0061] The predictive model generator 210 controls the model generator 228 of the previous information variables to field variables to generate a model that models the relationship between the mapped values contained in the previous information map 258 and the field values sensed by the field sensors 208, as indicated by block 292. The characteristic or data type represented by the mapped values in the previous 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.

[0062] 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 previous information map 258 to generate a predictive map 264 that predicts the values of the characteristic sensed by the field sensors 208 at different geographic locations in the field being harvested, or values of different characteristics related to the characteristic sensed by the field sensors 208, as indicated by block 294.

[0063] It is noted that in some examples, the previous 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, or the layers can have the same data type obtained at different times. Each of the two or more different maps, or each 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 in-field 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 in-field sensors 208 can include two or more sensors, each of which senses a different type of variable. As such, the prediction model generator 210 generates a prediction model that models relationships between each type of variable mapped by the previous information map 258 and each type of variable sensed by the in-field sensors 208. The prediction map generator 212 can generate a functional prediction map 263 that uses the prediction model and each of the maps or layers of the previous information map 258 to predict values of each sensed characteristic (or characteristics related to the sensed characteristic) sensed by the in-field sensors 208 at different locations in the field being harvested.

[0064] The prediction map generator 212 configures the prediction map 264 such that the prediction map 264 is executable (or usable) by the control system 214. The prediction map generator 212 can provide the prediction map 264 to the control system 214 or the control zone generator 213, or both. Some examples of different ways in which the prediction map 264 can be configured or output are described with reference 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 different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0065] The control zone generator 213 can divide the prediction map 264 into control zones based on the values on the prediction map 264. Successive geolocated values that are within a threshold of each other can be grouped into a control zone. 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 zones can be based on the response of the control system 214, controllable subsystem 216, based on wear considerations, or based on other criteria, as indicated by block 295. The control zone generator 213 can configure a prediction control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the prediction map 264 or the prediction control zone map 265 or both can include one or more of the predicted values on the prediction map 264 related to geographic location, the control zones on the prediction control zone map 265 related to geographic location, and the set values or control parameters used based on the predicted values on the map 264 or zones on the prediction control zone map 265. In another example, the presentation includes more summary information or more detailed information. The presentation can also include a confidence level that indicates an accuracy of the predicted values on the prediction map 264 or the zones on the prediction control zone map 265 in agreement with measured values that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. Additionally, where the information is presented in more than one location, an authentication and authorization system can be provided to implement an authentication and authorization process. For example, there can be levels of individuals authorized to view and change the maps and other presented information. By way of example, an onboard display device can show the maps locally on the machine in near real-time, or the maps can also be generated at one or more remote locations, or both. In some examples, each entity display device at each location can be associated with a personnel or user permission level. The user permission level can be used to determine which display indicia are visible on the entity display device and which values the respective personnel can change. As an 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 monitor, such as a monitor at a remote location, can be able to see the prediction map 264 on the display but prevented from making any changes. A manager, possibly 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 accessible and changeable by the remotely located manager can be used for machine control. This is one example of an authorization level that can be implemented. The prediction map 264 or the prediction control zone map 265 or both can also be configured in other ways, as indicated by block 297.

[0066] 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 inputs from the geo-location sensor 204 that identify the geo-location of the agricultural harvester 100. Block 302 represents sensor inputs indicative of 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 from the various field sensors 208 being received by the control system 214.

[0067] 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, and 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 understood that the particular control signals generated and the particular controllable subsystems 216 being controlled can vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 being controlled can vary based on the type of prediction map 264 or prediction control zone map 265 or both that is being used. Similarly, the control signals generated, the controllable subsystems 216 being controlled, and the timing of the control signals can vary based on various delays in the flow of the crop through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0068] By way of example, the generated prediction map 264 in the form of a prediction conveyor speed map can be used to control one or more subsystems 216. For example, the prediction conveyor speed map can include conveyor control setting values that are geographically referenced to locations within the field being harvested. The conveyor speed values from the prediction conveyor speed map can be extracted and used to control the conveyor speed of the header. Thus, values obtained from a prediction conveyor speed map or other types of prediction maps can be used to generate a wide variety of other control signals to control one or more of the controllable subsystems 216.

[0069] At block 312, it is determined whether the harvesting operation has been completed. If harvesting is not complete, the process proceeds to block 314 where the reading of field sensor data from the geo-location sensor 204 and the field sensors 208 (and possibly other sensors) continues.

[0070] 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 executed by the controllers in the control system 214, and other triggered learning.

[0071] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting the trigger criterion are discussed with reference to blocks 318, 320, 321, 322, and 324. For example, in some examples, when a threshold amount of field sensor data is obtained from the field sensors 208, the triggered learning can involve recreating the relationships used to generate the prediction models. In such examples, an amount of field sensor data received from the field sensors 208 exceeding 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, a threshold amount of field sensor data received from the field sensors 208 triggers the creation of new relationships represented by the prediction models generated by the prediction model generator 210. In addition, a new prediction map 264, a prediction control zone map 265, or both can be regenerated using the new prediction models. Block 318 represents detecting a threshold amount of field sensor data for triggering the creation of a new prediction model.

[0072] In other examples, the learning trigger criterion can be based on how much the field sensor data from the field sensors 208 changes (such as changes over time or changes compared to previous values). For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the previous information map 258) is within a selected range or less than a defined amount or a threshold below, the prediction model generator 210 does not generate a new prediction model. 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, for example, if the change in the field sensor data is outside of the selected range, greater than the defined amount, or above the threshold, the prediction model generator 210 generates a new prediction model with the new received field sensor data all or a portion of which is used by the prediction map generator 212 to generate a new prediction map 264. At block 320, the change in the field sensor data (such as a magnitude of the amount of data that exceeds the selected range, or a magnitude of the change in the relationship between the field sensor data and the information in the previous information map 258) can be used as a trigger for causing the generation of a new prediction model and prediction map. Consistent with the examples described above, the threshold, range, and defined amount can be set to a default value; set by an operator or user via user interface interaction; set by an automated system; or otherwise set.

[0073] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different previous infographic (different from the initially selected previous infographic 258), the switch to a different previous infographic can trigger relearning by the predictive model generator 210, the predictive map generator 212, the control area generator 213, the control system 214, or other objects. In another example, the agricultural harvester 100 switching to a different terrain or a different control area can also be used as a learning trigger criterion.

[0074] In some cases, operator 260 can also edit prediction graph 264 or prediction control area graph 265, or both. The editing can change the values ​​on prediction graph 264; change the size, shape, position, or presence of control areas on prediction control area graph 265; or change both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0075] In some cases, operator 260 may also observe that the automated control of the controllable subsystem is not as the operator expects. In these cases, operator 260 may provide manual adjustments to the controllable subsystem reflecting the operator's expectation that the controllable subsystem will operate in a different manner than what is being commanded by control system 214. Thus, manual changes to settings made by operator 260 may, based on operator 260's adjustments, cause predictive model generator 210 to relearn the model, cause predictive graph generator 212 to regenerate graph 264, cause control area generator 213 to regenerate one or more control areas on predictive control area graph 265, and cause control system 214 to relearn the control algorithm, or perform machine learning on one or more controller components 232 to 246 in control system 214, as shown in block 322. Block 324 indicates the use of other triggered learning criteria.

[0076] In other examples, relearning can be performed periodically or intermittently, for example, based on selected time intervals (such as discrete or variable time intervals), as indicated in box 326.

[0077] As indicated by box 326, if relearning is triggered (whether based on a learning trigger criterion or on the elapsed time interval), one or more of the predictive model generator 210, predictive map generator 212, control region generator 213, and control system 214 perform machine learning based on the learning trigger criterion to generate a new predictive model, a new predictive map, a new control region, and a new control algorithm, respectively. Any additional data collected since the last learning operation is performed is used to generate the new predictive model, the new predictive map, and the new control algorithm. Relearning is instructed to be performed by box 328.

[0078] If the harvesting operation has been completed, the operation moves from block 312 to block 330, where one or more of the prediction map 264, the prediction control zone map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control zone map 265, and the prediction model can be stored locally on the data storage 202, or transmitted to a remote system using the communication system 206 for later use.

[0079] It will be noted that while some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving a prior information map when generating a prediction model and a functional prediction map, respectively, in other examples, the prediction model generator 210 and the prediction map generator 212 can receive other types of maps when generating a prediction model and a functional prediction map, respectively, including a prediction map, such as a functional prediction map generated during a harvesting operation.

[0080] Figure 4 is Figure 1 A block diagram of a portion of the agricultural harvester 100 shown in FIG. 1 is shown. In particular, among other things, Figure 4 An example of the prediction model generator 210 and the prediction map generator 212 is shown in more detail. Figure 4 Information flow between the various components shown is also shown. The prediction model generator 210 receives one or more of the terrain maps 332 as a prior information map. 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 field sensors 208 illustratively include a conveyor belt speed sensor, such as the conveyor belt sensor 336, a material flow sensor 337, and a processing system 338. In some cases, the conveyor belt sensor 336 or the material flow sensor 337 can be located on the agricultural harvester 100. In other examples, the conveyor belt sensor 336 or the material flow sensor 337 can be remote from the agricultural harvester 100. The processing system 338 processes sensor data generated from the conveyor belt sensor 336 or the material flow sensor 337 to generate processed data indicative of a conveyor belt speed or a material flow characteristic, respectively.

[0081] In some examples, the conveyor belt sensor 336 can be a rotational sensor coupled to a conveyor belt roller drive system component. For example, a tone wheel / Hall effect type combination sensor. In another example, the conveyor belt sensor 336 senses user input from an operator indicative of a commanded conveyor belt speed. In other examples, the conveyor belt sensor 336 can be a different type of sensor. The processing system 338 processes one or more sensor signals from the conveyor belt sensor 336 to generate processed sensor data identifying a conveyor belt speed.

[0082] Material flow sensor 337 can sense one or more characteristics of a material flow. Some characteristics of a material flow include volumetric flow rate, mass flow rate, material composition, flow uniformity, material aggregation, material stall, underfeeding, and grain loss due to underfeeding. In some examples, material flow sensor 337 includes an optical sensor (such as a camera) that can sense the material flow as it moves through the field of view of the optical sensor. In another example, material flow sensor 337 includes an ultrasonic sensor, a lidar sensor, or a radar sensor. In other examples, material flow sensor 337 may also include other sensors.

[0083] like Figure 4 As shown, the exemplary prediction model generator 210 includes one or more of a model generator 342 for conveyor belt speed versus terrain characteristics and a model generator 343 for material flow versus terrain characteristics. In other examples, the prediction model generator 210 may include... Figure 4 The examples shown are compared to those with additional, fewer, or different components. Therefore, in some examples, the prediction model generator 210 may also include other objects 348, which may include other types of prediction model generators for generating other types of prediction models.

[0084] Model generator 342 identifies the relationship between one or more conveyor belt speed values ​​detected in sensor data 340 at a geographic location corresponding to sensor data 340 and topographic feature values ​​from topographic map 332 corresponding to the same geographic location where the conveyor belt speed values ​​are geolocated in the field. Based on this relationship established by model generator 342, model generator 342 generates prediction model 350. Prediction model 350 is used by conveyor belt map generator 352 to predict conveyor belt speed values ​​at different locations in the field based on georeferenced topographic feature values ​​at the same location in the field included in topographic map 332.

[0085] Model generator 343 identifies the relationship between one or more material flow characteristic values ​​detected in sensor data 340 at a geographic location corresponding to sensor data 340 and topographic characteristic values ​​from topographic map 332 corresponding to the geographic locations where the material flow characteristic values ​​are geolocated in the field. Based on this relationship established by model generator 343, model generator 343 generates prediction model 350. Prediction model 350 is used by material flow map generator 353 to predict material flow characteristics at different locations in the field based on georeferenced topographic characteristic values ​​at the same location in the field included in topographic map 332.

[0086] According to the foregoing, the prediction model generator 210 is operable to generate a plurality of prediction models, such as one or more of the prediction models generated by the model generators 342, 343, and 348. In another example, two or more of the prediction models described in the foregoing can be combined into a single prediction model that can be used to predict two or more of the conveyor belt speeds or the material flow characteristics based on the terrain values at different locations in the field. Any of these prediction models, or combinations thereof, are collectively represented in Figure 4 as the prediction model 350.

[0087] The prediction model 350 is provided to the prediction map generator 212. In Figure 4 the example, the prediction map generator 212 includes a conveyor belt map generator 352 and a material flow map generator 353. In other examples, the prediction map generator 212 can include additional or different map generators. As such, in some examples, the prediction map generator 212 can include other items 358, which can include other types of map generators for generating maps for other characteristic types. The conveyor belt map generator 352 receives the prediction model 350 and generates a prediction map that predicts one or more of the conveyor belt speeds at different locations in the field based on values from the terrain map 332 and the prediction model 350. The material flow map generator 353 receives the prediction model 350 and generates a prediction map that predicts one or more of the material flow characteristics at different locations in the field based on values from the terrain map 332 and the prediction model 350.

[0088] The prediction map generator 212 outputs prediction maps 360 that predict one or more of the conveyor belt speeds, one or more of the material flow characteristics, or some combination thereof. The generated prediction 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 those control zones into the prediction maps 360. One or more of the prediction maps 360 can be provided to the control system 214, which generates control signals to control one or more of the controllable subsystems 216 based on the one or more prediction maps 360.

[0089] Figure 5is a flowchart of an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating the prediction model 350 and the prediction map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive the previous topography map 332. At block 364, the processing system 338 receives one or more sensor signals from the conveyor belt sensor 336, the material flow sensor 337, or both. As discussed above, the sensor can be a conveyor belt speed sensor 336 that senses a conveyor belt speed 366; an operator input sensor that senses an operator input 367 indicative of a conveyor speed; a material flow sensor that senses a material flow characteristic 368; or another type of sensor that senses some other characteristic 370.

[0090] At block 372, the processing system 338 processes the one or more received sensor signals to generate data indicative of one or more conveyor speeds, one or more material flow characteristics, or some combination thereof. At block 382, the prediction model generator 210 also obtains a geographic location corresponding to the sensor data. For example, the prediction model generator 210 can obtain a geographic location from the geographic location sensor 204 and determine the precise geographic location to which the sensor data corresponds based on machine delays, machine speeds, sensor calibrations, and the like.

[0091] At block 384, the prediction model generator 210 generates one or more prediction models, such as the prediction model 350, that model a relationship between values obtained from the previous information map, such as the topography map 332, and the agricultural characteristic or related characteristic sensed by the field sensor 208. For example, the prediction model generator 210 can generate a prediction conveyor model that models a relationship between topography characteristics and sensed conveyor speeds obtained from the field sensor 208 indicated by the sensor data. Or, for example, the prediction model generator 210 can generate a prediction material flow model that models a relationship between topography characteristics and sensed material flow characteristics obtained from the field sensor 208 indicated by the sensor data.

[0092] At block 386, the prediction model, such as prediction model 350, is provided to the prediction map generator 212, which generates a prediction map 360 mapping a predicted characteristic based on the prediction model 350 and the terrain map 332. For example, the prediction map generator 212 can generate a prediction map 360 mapping a predicted expected conveyor belt speed based on the prediction model 350 and the terrain map 332. Or, for example, the prediction map generator 212 can generate a prediction map 360 mapping a predicted material flow characteristic based on the prediction model 350 and the terrain map 332. The prediction map 360 can be generated during the course of the agricultural operation. Thus, as the agricultural harvester moves through the field performing the agricultural operation, the prediction map 360 is generated as the agricultural operation is being performed.

[0093] At block 394, the prediction map generator 212 outputs the prediction map 360. At block 391, the prediction map generator 212 outputs the prediction map for presentation to the operator 260 and possible interaction by the operator 260. At block 393, the prediction map generator 212 can configure the map for use 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 to generate and incorporate control zones. At block 397, the prediction map generator 212 can also otherwise configure the prediction map 360. The prediction map 360, with or without control zones, is provided to the control system 214. At block 396, the control system 214 generates control signals for controlling the controllable subsystems 216 based on the prediction map 360. As indicated by block 400, the conveyor belt speed can be increased. For example, the conveyor belt speed can be increased when there is too much material bunching up.

[0094] As indicated by block 401, the conveyor belt speed can be decreased. In some cases, it is desirable to decrease the conveyor belt speed when the ground is sloped downward in the direction of travel of the conveyor belt. For example, the conveyor belt speed can be decreased when there is an overfeed of material from the feeder bin conveyor mechanism (e.g., the center belt of the header conveyor) that is perpendicular to the header conveyor.

[0095] As indicated by block 402, individual conveyor belt speeds can be controlled independently of one another. For example, when the header is oriented to roll over, the conveyor belts on each side can be independently controlled to account for the uneven effect of gravity on the material moving on the belts.

[0096] The controllable subsystems 216 can also be configured in other ways, as indicated by block 403.

[0097] Figure 6A block diagram showing 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 status 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 status zone generation system 490 includes a status zone criteria identification component 522, a status 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 corresponding operations will first be provided.

[0098] The agricultural harvester 100 or other work machine can have a wide variety of 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 independently controllable based on values on the function prediction map, or the WMAs can be controlled as a group 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 a coordinated manner with each other.

[0099] The WMA selector 486 selects a WMA or a group of WMAs for which a corresponding control zone is to be generated. The control zone generation system 488 then generates the 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, the 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 out as it moves) 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 is used to define the control zone for the selected WMA or group of WMAs. The control zone boundary definition component 496 processes the values on the function prediction map being analyzed to define the boundaries of the control zone on the function prediction map based on the values on the function prediction map being analyzed and based on the control zone criteria for the selected WMA or group of WMAs.

[0100] The target setting identifier component 498 sets the values of the target settings that will be used to control the WMA or set of WMAs in different control zones. For example, if the selected WMA is the propulsion system 250 and the function prediction graph being analyzed is the function prediction speed graph 438, the target speed setting in each control zone can be a target speed setting based on the speed values in the function prediction speed graph 238 contained within the identified control zone.

[0101] 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 for the WMA at a given location can be possible. In that case, the target settings can have different values and can be mutually contradictory. As such, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, in the case where the WMA is an actuator in the propulsion system 250 that is being controlled in order to control the speed of the agricultural harvester 100, in identifying the control zones and the target settings for the selected WMA in the control zones, the control zone generation system 488 takes into account that there can be multiple different mutually contradictory sets of criteria. For example, different target settings for controlling the machine speed can be generated based on, for example, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations thereof. However, at any given time, the agricultural harvester 100 cannot travel on the ground at multiple speeds simultaneously. Rather, at any given time, the agricultural harvester 100 travels at a single speed. As such, one of the mutually contradictory target settings is selected for use in controlling the speed of the agricultural harvester 100.

[0102] Accordingly, in some examples, the state zone generation system 490 generates state zones to resolve multiple different mutually contradictory target settings. The state zone criteria identification component 522 identifies criteria for establishing a state zone for the selected WMA or set of WMAs on the function prediction graph being analyzed. Some criteria that can be used to identify or define a state zone include, for example, the crop type or crop variety based on the planting map, or another source of crop type or crop variety, weed type, weed intensity, or crop state such as whether the crop is down, partially down, or standing. Just as each WMA or set of WMAs can have a corresponding control zone, different WMAs or sets of WMAs can have a corresponding state zone. The state zone boundary definition component 524 identifies the boundaries of the state zone on the function prediction graph being analyzed based on the state zone criteria identified by the state zone criteria identification component 522.

[0103] In some examples, the status zones can overlap one another. For example, the crop variety status zone can overlap some or all of the crop status status zone. In such examples, the different status zones can be assigned a priority ranking such that, where two or more status zones overlap, the status zone assigned a greater position or importance in the priority ranking is prioritized over the status zone with a lesser position or importance in the priority ranking. The priority ranking of the status zones can be set manually using a rules-based system, a model-based system, or another system, or automatically. As one example, where the down crop status zone overlaps the crop variety status zone, the down crop status zone can be assigned a greater importance in the priority ranking than the crop variety status zone such that the down crop status zone is prioritized.

[0104] Additionally, each status zone can have a unique settings resolver for a given WMA or set of WMAs. The settings resolver identifier component 526 identifies the particular settings resolver for each status zone identified on the functional prediction graph being analyzed, and the particular settings resolver for the selected WMA or set of WMAs.

[0105] Once the settings resolver for a particular status zone is identified, the settings resolver can be used to resolve the conflicting target settings, where more than one target setting is identified based on the control zone. Different types of settings resolvers can have different forms. For example, the settings resolver identified for each status zone can include a human-selected resolver, where the conflicting target settings are 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 instances, the settings resolver can resolve the conflicting target settings based on predicted or historical quality metrics corresponding to each of the different target settings. As an example, an increase in vehicle speed setting can decrease the time to harvest a field and decrease labor and equipment costs based on the corresponding time but can increase grain loss. A decrease in vehicle speed setting can increase the time to harvest a field and increase labor and equipment costs based on the corresponding time but can decrease grain loss. When grain loss or time to harvest is selected as a quality metric, the predicted or historical value for the selected quality metric can be used to resolve the speed setting given two conflicting vehicle speed settings. In some cases, the settings resolver can be a set of threshold rules that can be used in place of or in addition to the status zones. An example of a threshold rule can be expressed as follows:

[0106] If the predicted biomass value within 20 feet of the header of the agricultural harvester 100 is greater than x kilograms (where x is a selected or predetermined value), then the target setting value selected based on the feed rate is used instead of other mutually contradictory target settings, otherwise the target setting value based on grain loss is used instead of other mutually contradictory target setting values.

[0107] The setting resolver can be a logical component that performs logical rules in identifying the target setting. For example, the setting resolver can resolve the target setting while trying to minimize the harvesting time, or minimize the total harvesting cost, or maximize the harvested grain, or based on other variables calculated according to 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 where the total harvesting cost is reduced to or below a selected threshold. The harvested grain can be maximized where the amount of harvested grain is increased to or above a selected threshold.

[0108] Figure 7 is a flowchart showing one example of the operation of the control zone generator 213 in generating a graph (e.g., for the distance from the analyzed graph) for a control zone and a state zone received by the control zone generator 213 for zone processing.

[0109] At block 530, the control zone generator 213 receives a graph being analyzed for processing. In one example, as shown at block 532, the graph being analyzed is a functional prediction graph. For example, the graph being analyzed can be one of the functional prediction graphs 436, 437, 438, or 440. Block 534 indicates that the graph being analyzed can also be other graphs.

[0110] At block 536, the WMA selector 486 selects a WMA or a set of WMAs for which a control zone is to be generated on the map being analyzed. At block 538, the control zone criteria identification component 494 obtains control zone definition criteria for the selected WMA or set of WMAs. Block 540 indicates an example in which the control zone criteria are or include an example of a wear characteristic of the selected WMA or set of WMAs. Block 542 indicates an example in which the control zone definition criteria are or include a magnitude and a variation of input source data, such as a magnitude or variation on the map being analyzed or a magnitude or variation of input from individual field sensors 208. Block 544 indicates an example in which the control zone definition criteria are or include a physical machine characteristic, such as a physical dimension of the machine, a speed at which different subsystems operate, or other physical machine characteristic. Block 546 indicates an example in which the control zone definition criteria are or include a response of the selected WMA or set of WMAs at a set value of a new command. Block 548 indicates an example in which the control zone definition criteria are or include a machine performance indicator. Block 550 indicates an example in which the control zone definition criteria are or include an operator preference. Block 552 also indicates an example in which the control zone definition criteria are 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 a boundary of a control zone until a selected amount of time has elapsed after the agricultural harvester 100 enters a particular control zone. In some cases, the selected amount of time can be a minimum amount of time. As such, in some cases, the control zone definition criteria can prevent the agricultural harvester 100 from crossing a boundary of a control zone until at least the selected amount of time has elapsed. Block 551 indicates an example in which the control zone definition criteria are based on a selected size value. For example, control zone definition criteria based on a selected size value can disqualify the definition of a control zone that is less than the selected size. In some cases, the selected size can be a minimum size.

[0111] At block 554, the state zone criteria identification component 522 obtains state zone definition criteria for the selected WMA or set of WMAs. Block 556 indicates an example in which the state zone definition criteria are based on manual input from the operator 260 or another user. Block 558 illustrates an example in which the state zone definition criteria are based on a crop type or a crop variety. Block 560 illustrates an example in which the state zone definition criteria are based on a weed type or both. Block 562 illustrates an example in which the state zone definition criteria are based on or include a crop state. Block 564 also indicates an example in which the state zone definition criteria are or include other criteria. For example, the state zone definition criteria can be based on a terrain characteristic.

[0112] At block 566, control zone boundary definition component 496 generates boundaries of control zones on the map being analyzed based on control zone criteria. State zone boundary definition component 524 generates boundaries of state zones on the map being analyzed based on state zone criteria. Block 568 indicates an example of identifying zone boundaries for control zones and state zones. Block 570 shows that target setting identifier component 498 identifies target settings for each control zone. Control zones and state zones can also be generated in other ways, and this is indicated by block 572.

[0113] At block 574, setting resolver identifier component 526 identifies a setting resolver for the selected WMA in each state zone defined by state zone boundary definition component 524. As discussed above, the state zone resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver based on a prediction or historical quality of each mutually contradictory target setting 580, a rule-based resolver 582, a performance criteria-based resolver 584, or other resolver 586.

[0114] At block 588, WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If there are additional WMAs or sets of WMAs to process, processing returns to block 436 where the next WMA or set of WMAs for which control zones and state zones are to be defined is selected. When there are no additional WMAs or sets of WMAs for which control zones or state zones are to be generated remaining, processing moves to block 590 where control zone generator 213 outputs a map with control zones, target settings, state zones, and setting resolvers for each WMA or set of WMAs. As discussed above, the output map can be presented to operator 260 or another user; the output map can be provided to control system 214; or the output map can be output in other ways.

[0115] Figure 8 Fig. 6 illustrates one example of operation of control system 214 in controlling agricultural harvester 100 based on a map output by control zone generator 213. Thus, at block 592, control system 214 receives a map of a work site. In some cases, the map can be a functional prediction map that can include control zones and state zones, as represented by block 594. In some cases, the received map can be a functional prediction map that excludes control zones and state zones. Block 596 indicates an example of the received map indicating a work site can be a prior information map with control zones and state zones identified thereon. Block 598 indicates an example of the received map can include multiple different maps or multiple different map layers. Block 610 indicates an example of the received map can also take other forms.

[0116] 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 direction of travel 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 set of WMAs to be controlled. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings obtained for the selected WMA or set of WMAs can come from a variety of different sources. For example, block 630 illustrates an example where one or more of the target settings for the selected WMA or set of WMAs are based on input from the control zone on the map for the job site. Block 632 illustrates an example where one or more of the target settings are obtained from manual input from the operator 260 or another user. Block 634 illustrates an example where the target settings are obtained from the field sensors 208. Block 636 illustrates an example where one or more of the target settings are obtained from one or more sensors on other machines simultaneously working in the same field as the agricultural harvester 100, or from one or more sensors on machines that have worked in the same field in the past. Block 638 also illustrates an example where the target settings are obtained from other sources.

[0117] At block 640, the zone controller 247 accesses a settings resolver for the selected state zone and controls the settings resolver to resolve the conflicting target settings into resolved target settings. As discussed above, in some cases, the settings resolver can be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the conflicting target settings to the operator 260 or another user for resolution. In some cases, the settings resolver can be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the conflicting target settings to the neural network, artificial intelligence, or machine learning system for selection. In some cases, the settings resolver can be based on predictive or historical quality metrics, threshold rules, or logical components. In any of these latter examples, the zone controller 247 executes the settings resolver based on the predictive or historical quality metrics, based on the threshold rules, or by using the logical components to obtain the resolved target settings.

[0118] 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, resulting in control signals being generated based on the resolved target setting and applied to the selected WMA or set of WMAs. For example, with the selected WMA being a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the setpoint controller 232 or the header / true controller 238 or both the setpoint controller 232 and the header / true controller 238 to generate control signals based on the resolved target setting, and those generated control signals are applied to the machine or header actuator 248. At block 644, if additional WMAs or sets 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 set of WMAs is selected. The process represented by blocks 626 through 644 continues until all WMAs or sets of WMAs to be controlled at the current geographic location of the agricultural harvester 100 have been addressed. If there are no additional WMAs or sets of WMAs 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 are deemed to exist in the selected state zone. If additional control zones are deemed to exist, the process returns to block 624 where the next control zone is selected. If no additional control zones are deemed to exist, the process proceeds to block 648 where a determination is made as to whether additional state zones are deemed to exist. The zone controller 247 determines whether additional state zones are deemed to exist. If additional state zones are deemed to exist, the process returns to block 622 where the next state zone is selected.

[0119] 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 the control zone criteria have been met to continue processing, as indicated by block 652. For example, as mentioned above, the control zone definition criteria can include criteria defining when the control zone boundary can be crossed by the agricultural harvester 100. For example, whether the control zone boundary can be crossed by the agricultural harvester 100 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 that case, at block 652, the zone controller 247 determines that the selected time period has elapsed. In addition, the zone controller 247 can continually 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 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 will also be appreciated that the zone controller 247 can control the WMA and the set of WMAs using a multiple-input, multiple-output regulator simultaneously instead of controlling the WMA and the set of WMAs sequentially.

[0120] Figure 9 FIG. 6 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 processing system 662, a touch gesture processing system 664, and other artifacts 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 artifacts 682. The action signal generator 660 includes a visual control signal generator 684, an auditory control signal generator 686, a haptic control signal generator 688, and other artifacts 690. Before describing the operation of the example operator interface controller 231 illustrated in FIG. 6 in the context of processing various operator interface actions Figure 9 Before describing the operation of the example operator interface controller 231 illustrated in FIG. 6 in the context of processing various operator interface actions

[0121] The operator input command processing system 654 detects operator inputs on the operator interface mechanisms 218 and processes those inputs for commands. The speech processing system 662 detects speech inputs and processes interactions with the speech processing system 658 to process speech inputs for commands. The touch gesture processing system 664 detects touch gestures on touch sensitive elements in the operator interface mechanisms 218 and processes those inputs for commands.

[0122] The other controller interaction system 656 processes 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 and provides outputs to other controllers in the control system 214. The speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides outputs indicating the meaning of the spoken inputs. For example, when the operator 260 is commanding the control system 214 to change a setting for a controllable subsystem 216, the speech processing system 658 can recognize the speech input from the operator 260. In such an example, the speech processing system 658 recognizes the content of the spoken command, recognizes the meaning of the command as a setting change command, and provides the meaning of the input back to the speech processing system 662. The speech processing system 662 in turn interacts with the controller output generator 670 to provide command outputs to the appropriate controller in the control system 214 to implement the spoken setting change command.

[0123] The speech processing system 658 can be invoked in a variety of different ways. For example, in one example, the speech processing system 662 provides input from a microphone (as one of the operator interface mechanisms 218) to the speech processing system 658 continuously. The microphone detects speech from the operator 260 and the speech processing system 662 provides the detected speech to the speech processing system 658. The trigger detector 672 detects a trigger that indicates that the speech processing system 658 is invoked. In some cases, while the speech processing system 658 is receiving continuous speech input from the speech processing system 662, the speech recognition component 674 performs continuous speech recognition on all speech spoken by the operator 260. In some cases, the speech processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the speech processing system 658 can be initiated based on recognition of a selected spoken word, referred to as a wake-up word. In such examples, where the recognition component 674 recognizes the wake-up word, the recognition component 674 provides an indication that the wake-up word has been recognized to the trigger detector 672. The trigger detector 672 detects that the speech processing system 658 has been invoked or triggered by the wake-up word. In another example, the speech processing system 658 can be invoked by the operator 260 activating 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 speech processing system 658 has been invoked when the trigger input is detected by the user interface mechanism. The trigger detector 672 can also detect that the speech processing system 658 has been invoked in other ways.

[0124] Once the speech processing system 658 is invoked, speech input from the operator 260 is provided to the speech recognition component 674. The speech recognition component 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. The natural language understanding system 678 recognizes the meaning of the recognized speech. The meaning can be a natural language output, a command output recognizing a command reflected in the recognized speech, a value output recognizing a value in the recognized speech, or any of a wide variety of other outputs reflecting an understanding of the recognized speech. For example, more generally, the natural language understanding system 678 and the speech processing system 568 can understand the meaning of the recognized speech in the context of the agricultural harvester 100.

[0125] 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 conversation with the user to identify what the user wants to do. The conversation can disambiguate the user's command; identify one or more particular values that are needed to perform the user's 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 aural operator interface mechanism, such as a speaker. Thus, the conversation managed by the dialog management system 680 can be entirely a spoken conversation, or a combination of a visual conversation and a spoken conversation.

[0126] The action signal generator 660 generates action signals 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 to control the operator interface mechanisms 218. 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 aural control signal generator 686 generates output that controls aural elements of the operator interface mechanisms 218. The aural elements include speakers, aural alarm mechanisms, horns, or other aural elements. The haptic control signal generator 688 generates control signals as output to control haptic elements of the operator interface mechanisms 218. The haptic elements include vibration elements that can be used to, for example, vibrate the operator's seat, steering wheel, pedals, or joystick used by the operator. The haptic elements can include haptic feedback or force feedback elements that provide haptic feedback or force feedback to the operator through the operator interface mechanisms. The haptic elements can also include a wide variety of other haptic elements.

[0127] Figure 10 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 10 One example of how the operator interface controller 231 can detect and process operator interaction with a touch-sensitive display screen is also shown.

[0128] At block 692, the operator interface controller 231 receives a map. Block 694 indicates an example where the map is a function prediction map, and block 696 indicates an example where 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 indicated in block 700, the input from the geo-location sensor 204 can include a heading of the agricultural harvester 100 as well as a location. Block 702 indicates an example where the input from the geo-location sensor 204 includes a speed of the agricultural harvester 100, and block 704 indicates an example where the input from the geo-location sensor 204 includes other items.

[0129] 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 where 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 will operate. Block 712 indicates an example where the displayed field includes a coming area display portion showing an area that is about to be processed by the agricultural harvester 100, and block 714 indicates an example where the displayed field includes a previously visited display portion representing an area of the field that has already been processed by the agricultural harvester 100. Block 716 indicates an example where the displayed field displays various characteristics of the field having geo-referenced locations on the map. For example, if the received map is a conveyor speed map, the displayed field can show different conveyor speeds present in the field georeferenced within the displayed field. Or, for example, if the received map is a material flow map, the displayed field can show grain loss resulting from underfeeding. The mapped characteristics can be shown in previously visited areas (as shown in block 714) and in coming areas (as shown in block 712). Block 718 also indicates examples where the displayed field includes other items.

[0130] As Figure 11As shown in FIG. 7, the display portion 738 includes an interactive flag display portion, generally indicated as 741. The interactive flag display portion 741 includes a flag bar 739 that shows flags that have been set automatically or manually. A flag actuator 740 allows the operator 260 to mark a location (such as the current location of the agricultural harvester) or another location on the field designated by the operator and add information indicating the level of loss (e.g., due to underfeeding) found at the current location. For example, when the operator 260 activates the flag actuator 740 by touching the flag actuator 740, the touch gesture processing system 664 in the operator interface controller 231 identifies the current location as a location where the agricultural harvester 100 encountered a high level of loss. When the operator 260 touches the button 742, the touch gesture processing system 664 identifies the current location as a location where the agricultural harvester 100 encountered a medium level of loss. When the operator 260 touches the button 744, the touch gesture processing system 664 identifies the current location as a location where the agricultural harvester 100 encountered a low level of loss. When one of the flag actuators 740, 742, or 744 is activated, the touch gesture processing system 664 can control the visual control signal generator 684 to add a symbol corresponding to the level of loss identified on the field display portion 728 at the location identified by the user. In this way, areas in the field where the predicted values do not accurately represent the actual values can be marked for later analysis and can also be used for machine learning. In other examples, the operator can specify an area in front of or around the agricultural harvester 100 by activating one of the flag actuators 740, 742, or 744 so that control of the agricultural harvester 100 can be made based on the value specified by the operator 260.

[0131] The display portion 738 also includes an interactive marker display portion, generally indicated as 743. The interactive marker display portion 743 includes a symbol bar 746 that displays the symbol corresponding to each class of value or characteristic (in this case, level of grain loss due to underfeeding) being tracked on the field display portion 728. The display portion 738 also includes an interactive indicator display portion, generally indicated as 745. The interactive indicator display portion 745 includes an indicator bar 748 that shows the class of value or characteristic (in this case, level of grain loss due to underfeeding) identified on the field display portion 728. Figure 11 Figure 11 ​For example, the level of grain loss due to insufficient feed supply is indicated by a text indicator or other indicator. Without limitation, the symbols in the symbol bar 746 and the indicators in the indicator bar 748 may include any display features, such as different colors, shapes, patterns, intensity, text, icons, or other display features, and can be customized through the interaction of the operator of the agricultural harvester 100.

[0132] Display section 738 also includes an interactive value display section, typically indicated as 747. Interactive value display section 747 includes a value display bar 750 that displays the selected value. The selected value corresponds to a characteristic or value that is being tracked or displayed, or both tracked and displayed, on field display section 728. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in value display bar 750 defines a range of values, or other values ​​(such as predicted values), based on their categorized values. Thus, in Figure 11 In the examples, a predicted or measured grain loss level of 1.5 bushels per acre or greater is classified as "high loss level," a predicted or measured loss level of 1 bushel per acre or greater (but less than 1.5 bushels per acre) is classified as "medium loss level," and a predicted or measured loss level of 0.5 bushels per acre or less is classified as "low loss level." In some examples, the selected values ​​include a range, such that predicted or measured values ​​within the selected range will be classified under the corresponding indicator. For example, where the selected values ​​include a range, a predicted or measured loss level of 0.8 bushels per acre can be specified as "medium loss level" instead of "low loss level," even if the predicted or measured loss level of 0.8 bushels per acre exceeds the "low loss level" value but is less than the "medium loss level" value. The selected values ​​in value display bar 750 can be adjusted by the operator of the agricultural harvester 100. In one example, operator 260 can select a specific section of the field display section 728, and the value in column 750 for that specific section will be displayed. Thus, the value in column 750 can correspond to the value in display sections 712, 714, or 730.

[0133] Display section 738 also includes an interactive threshold display section, typically indicated as 749. Interactive threshold display section 749 includes a threshold display bar 752 that displays action thresholds. The action threshold in bar 752 can be a threshold corresponding to a selected value in value display bar 750. If a predicted or measured value of a characteristic being tracked or displayed, or both tracked and displayed, meets the corresponding action threshold in threshold display bar 752, control system 214 takes the action identified in bar 754. In some cases, a measured or predicted value can meet a corresponding action threshold by being equal to or exceeding it. In one example, operator 260 can select a threshold by touching a threshold in threshold display bar 752, for example, to change the threshold. Once selected, operator 260 can change the threshold. The threshold in bar 752 can be configured such that a specified action is performed when the measured or predicted value of a characteristic exceeds, is equal to, or is less than the threshold. In some cases, the threshold may represent a range or deviation of values ​​from selected values ​​in the value display bar 750, such that the predicted or measured characteristic value is equal to or falls within the range that satisfies the threshold. For example, in Figure 11 In one example, a predicted value falling within 10% of 1.5 bushels / acre will satisfy the corresponding action threshold (within 10% of 1.5 bushels / acre), and the control system 214 will take action (such as reducing the conveyor belt speed). In other examples, the threshold in the threshold display bar 752 is separated from the selected value in the value display bar 750, such that the value in the value display bar 750 defines the classification and display of the predicted or measured value, while the action threshold defines when an action will be taken based on the measured or predicted value. For example, when a predicted or measured loss value of 1.0 bushels / acre can be designated as "medium loss level" for classification and display purposes, the action threshold could be 1.2 bushels / acre, such that no action will be taken until the loss value meets the threshold. In other examples, the threshold in the threshold display bar 752 can include distance or time. For example, in the example of distance, the threshold could be a threshold distance from the field to an area where the measured or predicted value is georeferenced, an area that the harvester 100 must be located in before taking action. For example, a 10-foot threshold distance would mean taking action when the harvester is 10 feet or more within a georeferenced area of ​​the field where the measured or predicted values ​​are located. In an example where the threshold is time, the threshold could be the threshold time it takes for the harvester 100 to reach a georeferenced area of ​​the field where the measured or predicted values ​​are located. For example, a 5-second threshold would mean taking action when the harvester 100 is 5 seconds away from that georeferenced area. In such an example, the harvester's current position and speed of travel can be considered.

[0134] The display portion 738 also includes an interaction action display portion, generally indicated as 751. The interaction action display portion 751 includes an action display bar 754 that displays an action indicator that indicates an action that will be taken when a predicted or measured value meets an action threshold in the threshold display bar 752. The operator 260 can touch the action indicator 754 in the bar 754 to change the action to be taken. In some examples, multiple actions can be taken when the threshold is met. For example, one conveyor speed can increase while a second conveyor speed can decrease in response to the threshold being met.

[0135] The actions that can be set in the bar 754 can be any of a variety of different types of actions. For example, the actions can include a setting change action to change a setting of an internal actuator or another WMA or set of WMAs or to perform a setting change action to change a setting that changes a threshing rotor speed, a grain cleanout fan speed, a position of a header (e.g., tilt, height, roll, etc.), and a variety of other settings. These are provided by way of example only, and a variety of other actions are contemplated herein.

[0136] The items shown on the user interface display 720 can be visually controlled. The interface display 720 can be visually controlled to obtain the attention of the operator 260. For example, a display marker can be controlled to modify the intensity, color, or pattern in which the display marker is displayed. In addition, the display marker can be controlled to flash. The described alterations to the visual appearance of the display marker are provided by way of example. Thus, other aspects of the visual appearance of the display marker can be altered. Thus, the display marker can be modified in a desired manner in a variety of circumstances, such as to obtain the attention of the operator 260. In addition, while a particular number of items are shown on the user interface display 720, this need not be the case. In other examples, more or fewer items, including more or fewer items than the particular items, can be included on the user interface display 720.

[0137] Now returning to Figure 10FIG. 7 is a flowchart of the process of FIG. 6, continuing the description of the operation of operator interface controller 231. At block 760, operator interface controller 231 detects an input setting a flag and controls touch-sensitive user interface display 720 to display the flag on field display portion 728. The detected input can be an operator input (as indicated at 762) or an input from another controller (as indicated at 764). At block 766, operator interface controller 231 detects a field sensor input indicative of a characteristic of the field measured from one of field sensors 208. At block 768, visual control signal generator 684 generates a control signal to control user interface display 720 to display actuators for modifying user interface display 720 and for modifying machine control. For example, block 770 indicates that one or more of the actuators for setting or modifying the values in bars 739, 746, and 748 can be displayed. Thus, the user can set the flags and modify the characteristics of those flags. For example, the user can modify the wear level loss and wear level indicators corresponding to the flags. Block 772 indicates that the action threshold in bar 752 is displayed. Block 776 indicates that the action in bar 754 is displayed, and block 778 indicates that the measured field data in bar 750 is displayed. Block 780 indicates a variety of other information, and actuators can also be displayed on user interface display 720.

[0138] At block 782, operator input command processing system 654 detects and processes operator input corresponding to the interaction with user interface display 720 performed by operator 260. In the case where the user interface mechanism on which user interface display 720 is displayed is a touch-sensitive display screen, the interaction input by operator 260 with the touch-sensitive display screen can be a touch gesture 784. In some cases, the operator interaction input can be an input using a click device 786 or other operator interaction input 788.

[0139] 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 indicating that the detected value in column 750 satisfies the threshold condition presented in column 752. As previously explained, the threshold condition can include a value that is below a threshold, at a threshold, or above a threshold. Block 794 shows that the action signal generator 660 can alert the operator 260 in response to receiving the alarm condition by generating a visual alert using the visual control signal generator 684, by generating an audible alert using the audible control signal generator 686, by generating a tactile alert using the tactile control signal generator 688, or by using any combination of these. Similarly, as indicated by block 796, the controller output generator 670 can generate outputs to other controllers in the control system 214 so that those controllers perform the corresponding actions identified in column 754. Block 798 shows that the operator interface controller 231 can also detect and process alarm conditions in other ways.

[0140] Block 900 shows that the speech processing system 662 can detect and process inputs that invoke the speech processing system 658. Block 902 shows that performing speech processing can include using the dialog management system 680 to carry on a conversation with the operator 260. Block 904 shows that the speech processing can include providing signals to the controller output generator 670 to automatically perform control operations based on the speech input.

[0141] Hereinafter, Table 1 shows an example of a conversation between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 uses a trigger word or wake word that is detected by the trigger detector 672 to invoke the speech processing system 658. In the example shown in Table 1, the wake word is “Johnny”.

[0142] Table 1

[0143] Operator: “Johnny, tell me the current conveyor speed.”

[0144] Operator interface controller: “The current conveyor speed is 180 rpm.”

[0145] Operator: “Johnny, is there a feed shortage?”

[0146] Operator interface controller: “There is currently an undetectable amount of feed shortage.”

[0147] Table 2 shows an example of the speech synthesis component 676 providing output to the auditory control signal generator 686 to provide auditory updates on an intermittent or periodic basis. The interval between updates can be time-based (such as every five minutes), or area or distance-based (such as every five acres), or event-based (such as when a measured value is greater than a threshold).

[0148] Table 2

[0149] Operator interface controller: "In the last 10 minutes, the left conveyor belt speed averaged 190 rpm and the right conveyor belt speed averaged 150 rpm."

[0150] Operator interface controller: "The next acre includes a predicted average left conveyor belt speed of 210 rpm and a predicted average right conveyor belt speed of 130 rpm."

[0151] The example shown in Figure 3 shows that some actuators or user input mechanisms on the touch-sensitive display 720 can be complementary to the voice dialog. The example in Table 3 shows that the action signal generator 660 can generate action signals to automatically change the belt speed in the field being harvested.

[0152] Table 3

[0153] Person: "Johnny, change the belt speed to 150 rpm."

[0154] Operator interface controller: "Belt speed set to 150 rpm."

[0155] The example shown in Table 4 shows that the action signal generator 660 can have a dialog with the operator 260 to start and end control of the belt speed.

[0156] Table 4

[0157] Person: "Johnny, start the marker slow feed."

[0158] Operator interface controller: "Marker slow feed started."

[0159] ...

[0160] Person: "Johnny, slowly increase the belt speed to 250 rpm."

[0161] Operator interface controller: "Belt speed slowly increased to 250 rpm."

[0162] ...

[0163] Person: "Johnny, stop the marker slow feed."

[0164] Operator interface controller: "Mark slow down feed stop. Current belt speed is 195 rpm."

[0165] The example shown in Table 5 shows that the action signal generator 160 can generate signals to mark the weed patch in a different manner than shown in Tables 3 and 4.

[0166] Table 5

[0167] Person: "Johnny, increase the belt speed to 200 rpm for the next 100 feet."

[0168] Operator interface controller: "The belt speed will be increased to 200 rpm for the next 100 feet." Return to Figure 10 , block 906 illustrates that the operator interface controller 231 can also detect and handle conditions for outputting messages and 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 audible message. Block 910 shows that the output can be a visual message, and block 912 shows that the output can be a tactile message. Until the operator interface controller 231 determines that the current harvesting operation is complete, processing resumes at block 698 where the geographic location of the harvester 100 is updated and processing continues as described above to update the user interface display 720.

[0169] Once the operation is complete, any desired values displayed or already displayed on the user interface display 720 can be saved. Those values can also be used for machine learning to improve different parts of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control algorithm, or other artifacts. Saving the desired values is indicated by block 916. The 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.

[0170] It can thus be seen that a previous information index map is obtained by the agricultural harvester and shows values of terrain characteristics at different geographic locations of a field being harvested. On-board sensors on the harvester sense the belt speed as the agricultural harvester moves through the field. A prediction map is generated by the prediction map generator based on the values of terrain characteristics in the previous information map and the belt speed sensed by the on-board sensors, the prediction map predicting control values at different locations in the field. The control system controls the controllable subsystems based on the control values in the prediction map.

[0171] A control value is a value that an action can be based on. As described herein, a control value can include any value (or a property indicated by or derived from the value) that can be used in controlling an agricultural harvester 100. A control value can be any value indicative of an agricultural property. A control value can be a predicted value, a measured value, or a detected value. A control value can include any of the values provided by a graph, such as any of the graphs described herein, for example, a control value can be a value provided by an information graph, a value provided by a previous information graph, or a value provided by a prediction graph, such as a function prediction graph. A control value can also include any of the properties indicated by or derived from a value detected by any of the sensors described herein. In other examples, a control value can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

[0172] The present 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 apparatuses into which they are incorporated and activated by, and facilitate the functionality of, other components or articles of the systems.

[0173] Also, a number of user interface displays have been discussed. The displays can take a variety of different forms and can have a wide variety of different user-activatable operator interface mechanisms disposed thereon. For example, the user-activatable operator interface mechanisms can include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user-activatable operator interface mechanisms can also be actuated in a wide variety of different ways. For example, the user-activatable operator interface mechanisms can be actuated using operator interface mechanisms such as a pointing device (such as a trackball or mouse, a hardware button, a switch, a joystick or keyboard, a thumb switch or thumb pad, etc.), a virtual keyboard or other virtual actuator. Additionally, where the screen displaying the user-activatable operator interface mechanisms is a touch-sensitive screen, the user-activatable operator interface mechanisms can be activated using touch gestures. Furthermore, the user-activatable operator interface mechanisms can be activated using voice commands that utilize voice recognition functionality. Voice recognition can be implemented using voice detection devices such as microphones and software for recognizing the detected voice and performing commands based on the received voice.

[0174] Many data stores have also been discussed. Note that each data store can be divided into multiple data stores. In some examples, one or more of the data stores can be local to the system accessing the data store, one or more of the data stores can all be located remotely from the system utilizing the data store, or one or more data stores can be local while others are remote. All of these configurations are contemplated by the present disclosure.

[0175] Likewise, the figures show many blocks and assign a function to each block. Note that fewer blocks can be used to illustrate, functions attributed to multiple different blocks can be performed by fewer components. More blocks can also be used to illustrate, and the functions can be distributed among more components. In different examples, some functions can be added and some can be removed.

[0176] Note that the above discussion has described various different systems, components, logic, and interactions. It will be understood that any or all of such systems, components, logic, or interactions can be implemented by hardware items that perform the functions associated with those systems, components, logic, or interactions, 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. In addition, any or all of the systems, components, logic, and interactions can be implemented by software that is loaded into memory and then executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions are 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.

[0177] Figure 12 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 provides computing, software, data access, and storage services that do not require end users to know the physical location or configuration of the system 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 2The software or components and data associated therewith shown in FIG. 6 can all be stored on a server at a remote location. The computing resources in the remote server environment can be consolidated at a remote data center location, or the computing resources can be distributed 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 point of access for the user. Thus, the components and functionality described herein can be provided using a remote server architecture from a remote server located at a remote location. Alternatively, the components and functionality can be provided from a server, or the components and functionality can be installed on a client device directly or otherwise.

[0178] In Figure 12 the example shown in FIG. 6, some of the items are similar to the items shown in Figure 2 FIG. 5 and those items are similarly numbered. Figure 12 It is specifically shown that the prediction model generator 210 or the prediction map generator 212 or both can be located at a server location remote from the agricultural harvester 600. Thus, in the example shown in Figure 12 FIG. 6, the agricultural harvester 600 accesses the system through a remote server location 502.

[0179] Figure 12 Another example of a remote server architecture is also depicted. Figure 12 It is shown that, Figure 2Some of the elements of the system 500 can be located at a remote server location 502, while other elements can be located elsewhere. By way of example, the data store 202 can be located at a location separate from the location 502 and accessed via a remote server at the location 502. Regardless of where the elements are located, the elements can be accessed directly by the agricultural harvester 600 over a network, such as a wide area network or a local area network; the elements can be hosted by a server at a remote site; or the elements can be provided as a server or accessed by a connecting server located at a remote location. Further, data can be stored at any location and the stored data can be accessed or forwarded to an operator, user, or system by the operator, user, or system. For example, physical carriers can be used instead of or in addition to electromagnetic wave carriers. In some examples, where wireless telecommunication service coverage is crossed or absent, another machine, such as a fueling truck or other mobile machine or vehicle, can have an automated, semi-automated, or manual information collection system. When the combine harvester 600 approaches the machine containing the information collection system, such as a fueling truck before fueling, the information collection system uses any type of ad-hoc wireless connection to collect information from the combine harvester 600. The collected information can then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication coverage or other wireless coverage is available. For example, a fueling truck can enter an area with wireless communication coverage as it travels to a location to fuel other machines or as it is at a primary fuel storage location. All of these architectures are contemplated herein. Additionally, information can be stored onto the combine harvester 600 until the combine harvester 600 enters an area with wireless communication coverage. The combine harvester 600 itself can send the information to another network.

[0180] It will also be noted that, Figure 2 The elements of the system 500, or portions thereof, can be provided on a variety of different devices. One or more of these devices can include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palmtop computer, a cell phone, a smart phone, a multimedia player, a personal digital assistant, and the like.

[0181] In some examples, the remote server architecture 500 can include network security measures. Without limitation, these measures can include data encryption on storage devices, data encryption sent between network nodes, authentication of personnel or processes accessing data, and the use of ledgers to record metadata, data, data transmissions, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as a blockchain).

[0182] Figure 13is a simplified block diagram of one illustrative embodiment of a handheld or mobile computing device that can be used as a user's or client's handheld device 16, in which the present system (or a portion thereof) can be deployed. For example, a mobile device can be deployed in the cab of an agricultural harvester 100 for generating, processing, or displaying the maps discussed above. Figures 14-15 is an example of a handheld or mobile device.

[0183] Figure 13 An overall block diagram of components of a client device 16 is provided, which can run some of the components shown in Figure 2 interact with some of the components shown in Figure 2 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, automatically provide a channel or conduit for receiving information such as through scanning. Examples of the communications link 13 include allowing communication through one or more communication protocols, such as a cellular wireless service for providing access to a network, and protocols that provide local wireless connections to a network.

[0184] In other examples, the applications can be received on a removable secure digital (SD) card that is connected to an interface 15. The interface 15 and the communications link 13 communicate with a processor 17 (which can also be embodied as a processor or server according to other figures) along a bus 19 that is also connected to a memory 21 and input / output (I / O) components 23, as well as a clock 25 and a positioning system 27.

[0185] In one example, the I / O components 23 are provided to facilitate input and output operations. The I / O components 23 for various examples of the device 16 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors, and output components such as displays, speakers, and / or printer ports. Other I / O components 23 can also be used.

[0186] The clock 25 illustratively includes a real-time clock component that outputs the time and date. The clock 25 can also illustratively provide timing functions to the processor 17.

[0187] The positioning system 27 illustratively includes a component that outputs the current geographic position of the device 16. For example, this can include a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. The positioning system 27 can also include mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions, for example.

[0188] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include, and can be included within, computer storage media (described below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause processor to perform computer-implemented steps or functions according to the instructions. Processor 17 can also be activated by other components to facilitate its functions.

[0189] Figure 14 One example is shown in which device 16 is a tablet computer 600. In Figure 14 , computer 601 is shown with a user interface display screen 602. Screen 602 can be a touch screen or a pen-activated interface that receives input from a pen or stylus. Tablet computer 600 can also use an on-screen virtual keyboard. Of course, computer 601 can also be attached to a keyboard or other user input device by a suitable attachment mechanism, such as a wireless link or USB port, for example. Computer 601 can also exemplarily receive voice input.

[0190] Figure 15 Similarly Figure 14 , except that the device is a smart phone 71. Smart phone 71 has a touch-sensitive display 73 that displays icons or tiles or other user input mechanisms 75. A user can use mechanisms 75 to run applications, make phone calls, perform data transfer operations, etc. Typically, smart phone 71 is built on a mobile operating system and provides more advanced computing capability and connectivity than a feature phone.

[0191] Note that other forms of device 16 are possible.

[0192] Figure 16 is one example of a computing environment in which elements of Figure 2 may be deployed. Referring to Figure 16 , an example system for implementing some embodiments includes a computing device in the form of a computer 810, programmed to operate as discussed above. The components of computer 810 can include, but are not limited to, a processing unit 820 (which can include a processor or server according to the previous figures), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. The system bus 821 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. With Figure 2The described memory and programs can be deployed in Figure 16 corresponding portions of the respective sections.

[0193] 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 include computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0194] 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 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, Figure 8 illustrates operating system 834, application programs 835, other program modules 836, and program data 837. Figure 16 are shown in the drawing from which Figure 8 was created.

[0195] The computer 810 can also include other removable / non-removable volatile / nonvolatile computer storage media. By way of example, and not limitation, Figure 8 illustrates a hard disk drive 840 that reads from or writes to nonremovable, nonvolatile magnetic media, a magnetic disk drive 851 that reads from or writes to a removable, nonvolatile magnetic media such as a floppy disk, and an optical disk drive 855 that reads from or writes to a removable, nonvolatile optical disk 856 such as a CD ROM or other optical media. Figure 16A hard disk drive 841, an optical disk drive 855, and a non-volatile optical disk 856 are shown for reading from or writing to non-removable non-volatile media. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable disk storage interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable storage interface (such as interface 850).

[0196] Alternatively, or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), application-specific standard products (e.g., ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and the like.

[0197] The above discussion and Figure 16 The driver and its associated computer storage media shown provide storage for computer-readable instructions, data structures, program modules, and other data for the computer 810. Figure 16 For example, hard disk drive 841 is shown storing operating system 844, application program 845, other program modules 846, and program data 847. It should be noted 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.

[0198] Users can input commands and information into computer 810 using input devices such as keyboard 862, microphone 863, and clicking or pointing devices 861, such as mouse, trackball, or touchpad. Other input devices (not shown) may include joysticks, gamepads, satellite dish antennas, 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 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 a monitor, the computer may also include other peripheral output devices, such as speakers 897 and printer 896, which may be connected via output peripheral interface 895.

[0199] Computer 810 operates in a network 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).

[0200] When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873, such as the Internet. In a networked environment, program modules can be stored in the remote memory storage device. For example, Figure 16 It is shown that remote applications 885 can remain on the remote computer 880.

[0201] It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All these possibilities are to be considered as being contemplated in this document.

[0202] Example 1 is an agricultural work machine comprising:

[0203] a communication system that receives a previous information map, the previous information map comprising values of a terrain characteristic corresponding to different geographical locations in a field;

[0204] a geographical location sensor that detects a geographical location of the agricultural work machine;

[0205] a field sensor that detects a first agricultural characteristic corresponding to the geographical location;

[0206] a prediction map generator that generates a functional predicted agricultural map of the field based on the values of the terrain characteristic in the previous information map and based on the value of the first agricultural characteristic, the functional predicted agricultural map mapping predicted values of a second agricultural characteristic to different geographical locations in the field;

[0207] a controllable subsystem; and

[0208] a control system that generates a control signal to control a conveyor belt based on the geographical location of the agricultural work machine and based on the predicted values of the second agricultural characteristic in the functional predicted agricultural map.

[0209] Example 2 is the agricultural work machine of any or all preceding examples, wherein the control system controls a conveyor belt as the controllable subsystem.

[0210] Example 3 is the agricultural work machine of any or all preceding examples, wherein the control system controls a second conveyor belt independently of a first conveyor belt.

[0211] Example 4 is the agricultural work machine of any or all previous examples, wherein the field sensor comprises a conveyor speed sensor that detects a conveyor speed as the first agricultural characteristic.

[0212] Example 5 is the agricultural work machine of any or all previous examples, wherein the field sensor comprises an operator input sensor that detects an operator input indicative of an operator command to set the conveyor speed as the first agricultural characteristic.

[0213] Example 6 is the agricultural work machine of any or all previous examples, wherein the field sensor comprises a material flow sensor that detects a material flow characteristic as the first agricultural characteristic.

[0214] Example 7 is the agricultural work machine of any or all previous examples, wherein the prior information map comprises a topography map that maps a slope value as the topography characteristic to different geographic locations in the field.

[0215] Example 8 is the agricultural work machine of any or all previous examples, further comprising:

[0216] a prediction model generator that generates a predictive conveyor model that models a relationship between the topography characteristic and a conveyor speed based on values of the topography characteristic in the prior information map at the geographic locations and values of the first agricultural characteristic detected by the field sensor at the geographic locations, wherein the predictive map generator generates a functional predictive agricultural map based on the values of the topography characteristic in the prior information map and based on the predictive conveyor model.

[0217] Example 9 is the agricultural work machine of any or all previous examples, an operator interface controller that generates a user interface graphical representation of the functional predictive agricultural map, the user interface graphical representation comprising a field portion having one or more indicia indicative of values of the second agricultural characteristic at one or more geographic locations on the field portion.

[0218] Example 10 is the agricultural work machine of any or all previous examples, wherein the operator interface controller generates the user interface graphic to include an interaction display portion, an interaction threshold display portion, and an interaction action display portion, the interaction display portion displaying a value display portion indicative of a selected value, the interaction threshold display portion indicating an action threshold, the interaction action display portion indicating a control action to be taken when one of the predicted values of the second agricultural characteristic meets the action threshold related to the selected value, the control system generating the control signals to control the controllable subsystem based on the control action.

[0219] Example 11 is a computer-implemented method of controlling an agricultural work machine, comprising:

[0220] obtaining a prior information map, the prior information map including values of a terrain characteristic corresponding to different geographic locations in a field;

[0221] detecting a geographic location of the agricultural work machine;

[0222] detecting, with an on-site sensor, a value of a conveyor speed corresponding to the geographic location;

[0223] generating a functional predictive agricultural map of the field based on the values of the terrain characteristic in the prior information map and based on the value of the conveyor speed corresponding to the geographic location, the functional predictive agricultural map mapping predicted control values to different geographic locations in the field; and

[0224] controlling a controllable subsystem based on the geographic location of the agricultural work machine and based on control values in the functional predictive agricultural map.

[0225] Example 12 is the computer-implemented method of any or all previous examples, wherein generating a functional predictive map comprises:

[0226] generating a functional predictive conveyor speed map, the functional predictive conveyor speed map mapping predicted conveyor speed values as control values to different geographic locations in the field.

[0227] Example 13 is the computer-implemented method of any or all previous examples, wherein controlling a controllable subsystem comprises:

[0228] generating a conveyor speed control signal based on the detected geographic location and the functional predictive conveyor speed map; and

[0229] controlling the controllable subsystem based on the conveyor speed control signal to control a speed of a conveyor of the agricultural work machine.

[0230] Example 14 is the computer-implemented method of any or all previous examples, wherein controlling the controllable subsystem includes:

[0231] controlling the controllable subsystem based on the conveyor speed control signal to control a second speed of a second conveyor of the agricultural work machine.

[0232] Example 15 is the computer-implemented method of any or all previous examples, wherein generating a function prediction map includes:

[0233] generating a function prediction operator command map that maps predicted operator commands to different geographic locations in the field.

[0234] Example 16 is the computer-implemented method of any or all previous examples, wherein controlling the controllable subsystem includes:

[0235] generating an operator command control signal based on the detected geographic location and the function prediction operator command map, the operator command control signal indicating an operator command; and

[0236] controlling the controllable subsystem based on the operator command control signal to execute the operator command.

[0237] Example 17 is the computer-implemented method of any or all previous examples, further comprising:

[0238] generating a predicted conveyor model based on a value of the terrain characteristic in the previous information map at the geographic location and a value of a conveyor speed detected by the field sensor at the geographic location, the predicted conveyor model modeling a relationship between the terrain characteristic and the conveyor speed, wherein generating a function prediction agricultural map includes generating the function prediction agricultural map based on the value of the terrain characteristic in the previous information map and based on the predicted conveyor model.

[0239] Example 18 is the computer-implemented method of any or all previous examples, further comprising:

[0240] detecting, with a second field sensor, a value of a second conveyor speed corresponding to the geographic location; and

[0241] generate a second predictive belt model based on the values of the terrain characteristic at the geographic locations in the previous information map and the values of the second belt speed at the geographic locations detected by the field sensor, the second predictive belt model modeling a relationship between the terrain characteristic and the second belt speed, wherein generating the functional predictive agricultural map includes generating the functional predictive agricultural map based on the second predictive belt model.

[0242] Example 19 is an agricultural work machine, comprising:

[0243] a communication system that receives a previous information map, the previous information map including values of a terrain characteristic corresponding to different geographic locations in a field;

[0244] a geographic location sensor that detects a geographic location of the agricultural work machine;

[0245] a field sensor that detects values of a belt speed corresponding to geographic locations;

[0246] a predictive model generator that generates a predictive agricultural model based on the values of the terrain characteristic at the geographic locations in the previous information map and the values of the belt speed at the geographic locations detected by the field sensor, the predictive agricultural model modeling a relationship between the terrain characteristic and the belt speed;

[0247] a predictive map generator that generates a functional predictive agricultural map of the field based on the values of the terrain characteristic in the previous information map and based on the predictive agricultural model, the functional predictive agricultural map mapping predictive control values to different geographic locations in the field;

[0248] a controllable subsystem; and

[0249] a control system that generates control signals based on the geographic location of the agricultural work machine and based on control values in the functional predictive agricultural map to control the controllable subsystem.

[0250] Example 20 is the agricultural work machine of any or all preceding examples, wherein the predictive control values include belt speed settings for one or more belts.

[0251] While the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. An agricultural work machine (100), comprising: a communication system (206) that receives a prior information map (258) that includes values of a terrain characteristic corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine (100); a field sensor (208) that detects a first agricultural characteristic corresponding to the geographic location; a prediction map generator (212) that generates a functional predicted agricultural map of the field based on the values of the terrain characteristic in the prior information map (258) and based on the value of the first agricultural characteristic, the functional predicted agricultural map mapping predicted values of a second agricultural characteristic to different geographic locations in the field; a controllable subsystem (216); and a control system (214) that generates a control signal based on the geographic location of the agricultural work machine (100) and based on the predicted values of the second agricultural characteristic in the functional predicted agricultural map to control a conveyor belt. the control system controls the conveyor belt as the controllable subsystem.

2. The agricultural work machine of claim 1, wherein, the control system controls a second conveyor belt independently of a first conveyor belt.

3. The agricultural work machine of claim 2, wherein, the field sensor includes a conveyor belt speed sensor that detects a conveyor belt speed as the first agricultural characteristic.

4. The agricultural work machine of claim 1, wherein, the field sensor includes an operator input sensor that detects an operator input that indicates an operator command to set the conveyor belt speed as the first agricultural characteristic.

5. The agricultural work machine of claim 1, wherein, the field sensor includes a material flow sensor that detects a material flow characteristic as the first agricultural characteristic.

6. The agricultural work machine of claim 1, wherein, the prior information map includes a terrain map that maps slope values as the terrain characteristic to different geographic locations in the field.

7. The agricultural work machine of claim 1, wherein, 8. The agricultural work machine of claim 1, further comprising: a prediction model generator that generates a predictive conveyor belt model based on the value of the terrain characteristic at the geographic location in the prior information map and the value of the first agricultural characteristic at the geographic location detected by the field sensor, the predictive conveyor belt model modeling a relationship between the terrain characteristic and a conveyor belt speed, wherein the prediction map generator generates a functional predicted agricultural map based on the values of the terrain characteristic in the prior information map and based on the predictive conveyor belt model.

9. A computer-implemented method of controlling an agricultural work machine (100), comprising: obtaining a prior information map (258) that includes values of a terrain characteristic corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine (100); detecting a value of a conveyor belt speed corresponding to the geographic location with a field sensor (208); ​ generate a functional predictive agronomic map of the field that maps predictive control values to different geographic locations in the field based on the values of the terrain characteristic in the previous information map (258) and on the values of the conveyor speed corresponding to the geographic locations; and control a controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and on the control values in the functional predictive agronomic map.

10. An agricultural work machine (100) comprising: a communication system (206) that receives a previous information map (258) that includes values of a terrain characteristic corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects values of a conveyor speed corresponding to geographic locations; a predictive model generator (210) that generates a predictive agricultural model based on the values of the terrain characteristic at the geographic locations in the previous information map (258) and the values of the conveyor speed at the geographic locations detected by the field sensor (208), the predictive agricultural model modeling a relationship between the terrain characteristic and the conveyor speed; a predictive map generator (212) that generates a functional predictive agronomic map of the field that maps predictive control values to different geographic locations in the field based on the values of the terrain characteristic in the previous information map (258) and on the predictive agricultural model; a controllable subsystem (216); and a control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and on the control values in the functional predictive agronomic map.

10. An agricultural work machine (100) comprising: a communication system (206) that receives a previous information map (258) that includes values of a terrain characteristic corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of the agricultural work machine; a field sensor (208) that detects values of a conveyor speed corresponding to geographic locations; a predictive model generator (210) that generates a predictive agricultural model based on the values of the terrain characteristic at the geographic locations in the previous information map (258) and the values of the conveyor speed at the geographic locations detected by the field sensor (208), the predictive agricultural model modeling a relationship between the terrain characteristic and the conveyor speed; a predictive map generator (212) that generates a functional predictive agronomic map of the field that maps predictive control values to different geographic locations in the field based on the values of the terrain characteristic in the previous information map (258) and on the predictive agricultural model; a controllable subsystem (216); and a control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and on the control values in the functional predictive agronomic map.

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