Graph Generation and Control Systems

Abstract

One or more information maps are obtained by an agricultural machine. The one or more information maps map one or more agricultural characteristic values ​​at different geographic locations in a field. Field sensors on the agricultural machine sense the agricultural characteristics as the agricultural machine moves through the field. A prediction map generator generates a prediction map based on the relationship between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. The prediction map predicts the predicted agricultural characteristics 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 Art

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

[0003] The above discussion is provided for general background information only and is not intended to be used as an aid in determining the scope of the claimed subject matter. Summary of the Invention

[0004] One or more information maps are obtained by an agricultural machine. The one or more information maps map one or more agricultural characteristic values ​​at different geographic locations in a field. Field sensors on the agricultural machine sense the agricultural characteristics as the agricultural machine moves through the field. A prediction map generator generates a prediction map that predicts the predicted agricultural characteristics at different locations in the field based on the relationship between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. The prediction map can be output and used for automated machine control.

[0005] This summary is provided to introduce a selection of concepts in a simplified form that will be further described in the detailed description below. 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 examples that solve any or all of the shortcomings identified in the background. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 is a partially pictorial, partially schematic illustration of an example of a combine harvester.

[0007] Figure 2 is a block diagram illustrating some portions of an agricultural harvester in greater detail according to some examples of the present disclosure.

[0008] Figures 3A to 3B (collectively referred to herein as FIG. 3 ) shows a flow chart illustrating an example of the operation of an agricultural harvester when generating a map.

[0009] Figure 4A is a block diagram illustrating one example of a prediction model generator and a prediction map generator.

[0010] Figure 4Bis a block diagram illustrating one example of a predictive model generator in more detail.

[0011] Figure 5 is a flow chart illustrating an example of the operation of an agricultural harvester when receiving a priori information map, detecting characteristics with field sensors, and generating a functional prediction map for rendering the agricultural harvester or for controlling the agricultural harvester during a harvesting operation.

[0012] Figure 6 is a block diagram of an example of a control region generator.

[0013] Figure 7 is a block diagram illustrating one example of the operation of a control region generator.

[0014] Figure 8 is a flowchart of one example of an operation using a control area.

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

[0016] Figure 10 is a flow chart illustrating one example of the operation of the operator interface controller.

[0017] Figure 11 is an illustration of an example of a user interface display.

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

[0019] Figures 13 to 15 An example of a mobile device that may be used in an agricultural harvester is shown.

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

[0021] To promote an understanding of the principles of the present disclosure, reference will now be made to the examples described herein and illustrated in the accompanying drawings, and specific language will be used to describe the examples. However, it will be understood that this is not intended to limit the scope of the present disclosure. Any changes and further modifications to the described devices, systems, methods, and any further applications of the principles of the present disclosure are fully contemplated, as would be generally contemplated by one skilled in the art to which the present disclosure relates. In particular, it is fully contemplated that the features, components, and / or steps described with respect to one example may be combined with the features, components, and / or steps described with respect to other examples of the present disclosure.

[0022] This specification relates to using field data acquired concurrently with agricultural operations, combined with previous data, to generate predictive maps.

[0023] In some examples, the prediction map can be used to control agricultural machines, such as agricultural harvesters. As discussed above, under different conditions, the performance of agricultural harvesters may deteriorate or be affected in other ways. For example, the performance of a harvester (or other agricultural machine) may be adversely affected based on the size of the grain of the crop being harvested, or the characteristics (EHP characteristics) of the ear, head, or pod of the crop being harvested. As used herein, grain size can include various size characteristics of the grain, such as diameter (such as cross-sectional width), weight, length, mass, density, volume, and various other size characteristics or dimensions. EHP characteristics can include, but are not limited to, deformities of the grain, ear, cob, head, or pod; diseases in the grain, ear, cob, head, or pod; and damage to the grain, ear, cob, head, or pod, as well as size characteristics of the EHP, such as diameter (such as cross-sectional width), weight, length, mass, density, volume, and various other size characteristics or dimensions. The settings of the screens, chaff screens, cleaning fans, rotors and concave plates can be different based on kernel size or EHP characteristics.

[0024] In some examples, the predicted biomass map can be used to control agricultural operation machines, such as agricultural harvesters. As used in this article, biomass refers to the amount of vegetation material on the ground in a given area or position. Often the amount is measured according to weight, and the weight is, for example, the weight per given area (such as tons per acre). Various characteristics can indicate biomass (referring to biomass characteristics in this article) and can be used to predict the biomass on the field of interest. For example, the biomass characteristic can include various crop characteristics, such as crop height (the height of crop above the surface of the field), crop density (the amount of crop material at a given interval, which can be derived from crop quality and crop volume), crop quality (such as the weight of crop or the weight of crop components), or crop volume (how much in a given area or position is occupied by crops, i.e., the space occupied or contained by crops). In another example, the biomass characteristic can include various machine characteristics of an agricultural harvester, such as machine settings or operating characteristics. For example, the force (such as fluid pressure or torque) used to drive the threshing rotor of an agricultural harvester can indicate biomass.

[0025] When an agricultural harvester engages a field with an area of ​​biomass variation, the performance of the agricultural harvester may be affected. For example, if the machine settings of the agricultural harvester are set based on an expected or desired throughput, the biomass variation may cause the throughput to vary, and thus, the machine settings may not be optimal for the effective processing of vegetation (including crops). As mentioned above, the operator can try to predict the biomass in front of the machine. In addition, some systems (such as feedback control systems) reactively adjust the forward ground speed of the agricultural harvester in an attempt to maintain the desired throughput. This can be accomplished by trying to identify the biomass based on sensor input (such as from a sensor sensing a variable indicating biomass). However, such an arrangement may be prone to errors and may be too slow to react to the upcoming biomass change in order to effectively change the operation of the machine so as to control the throughput (such as by changing the forward speed of the harvester). For example, such a system is typically reactive because only after vegetation has been encountered by the machine, adjustments to the machine settings are made to try to reduce further errors (such as in the feedback control system).

[0026] The vegetation index map illustratively maps vegetation index values ​​(which can indicate plant growth) for different geographic locations across a field of interest. One example of a vegetation index includes the Normalized Difference Vegetation Index (NDVI). Many other vegetation indices exist, and these other vegetation indices are within the scope of the present disclosure. In some examples, the vegetation index can be derived based on sensor readings of one or more bands of electromagnetic radiation reflected by plants. Without limitation, these bands of electromagnetic radiation can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0027] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, vegetation index maps enable crops to be identified and georeferenced in the presence of bare soil, crop residue, or other vegetation (such as weeds). In other examples, vegetation index maps can detect various crop characteristics, such as crop growth and crop health or vigor, across different geographic locations in a field of interest.

[0028] A seed genotype map maps the specific genotypes of seeds planted at different locations in a field. A seed genotype map can be generated by a grower or by a machine that performs subsequent operations, such as a sprayer with an optical detector that detects the plant's genotype.

[0029] The forecast yield map includes geo-referenced forecast yield values.

[0030] The predicted weed map includes one or more of a geo-referenced predicted weed intensity value or a weed type value. The weed intensity value may include, but is not limited to, at least one of weed population, weed growth stage, weed size, weed biomass, weed moisture, or weed health.

[0031] The sowing diagram illustratively maps the sowing characteristics of different geographical locations across the field of interest. These sowing diagrams are typically collected from past seed planting operations on the field. In some examples, the sowing diagram can be derived from the control signal used by the sowing machine when planting seeds, or derived from a sensor, such as a topographic sensor that is used to confirm that the seed is transferred to the furrow generated by the sowing machine. The sowing machine can include a topographic sensor that geolocates the location where the seed is planted and generates topographic information of the field. For example, the topographic sensor can include GPS, a laser level, an inclinometer / odometer pair, local radio triangulation (meter) and various other systems for generating topographic information. The information generated during the previous seed planting operation can be used to determine various sowing characteristics, such as position (for example, the geographical location of the planted seed in the field), interval (for example, the interval between individual seeds, the interval between seed rows, or both), population (which can be derived from the interval characteristic), seed orientation (for example, the orientation of the seed in the furrow or the seed row), depth (for example, seed depth or furrow depth), size (such as seed size) or genotype (such as seed species, seed hybrids, seed cultivars, etc.). Various other seeding characteristics may also be determined.In some examples, the seeding map may include information related to the seed bed in which the seeds are placed, such as soil moisture, soil temperature, soil composition (such as soil organic matter).

[0032] In some embodiments, the present invention provides the seeding diagram of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention.In some embodiments, the seeding diagram of the present invention provides the seeding characteristics of the present invention. It should be noted that plant response data can include data indicating the resistance of the plant to various conditions and characteristics, such as the resistance of the plant to applied substances, the resistance of the plant to weather conditions, the resistance of the plant to pests, fungi, weeds, diseases, etc., as well as the resistance of the plant to various other conditions or characteristics, and the resistance of the plant to various other conditions or characteristics.

[0033] In lieu of, or in addition to, data from previous operations or from a third party, various seeding characteristics on a seeding map can be generated based on various user or operator input data (e.g., operator or user input data indicating various seeding characteristics), such as location, depth, orientation, spacing, size, genotype, and various other seeding characteristics.

[0034] In some examples, a seeding map can be derived based on sensor readings of one or more bands of electromagnetic radiation reflected by the seeds or seedbed. Without limitation, these bands of electromagnetic radiation can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0035] The present discussion continues with reference to a system that receives at least one or more of a sowing map, a vegetation index map, a yield map, a biomass map, or another map, and further uses field sensors to detect values ​​indicative of one or more EHP characteristics or grain size during a harvesting operation. The system generates a model that models one or more relationships between characteristics derived from the prior information map and output values ​​from the field sensors. One or more models are used to generate a functional prediction map based on the one or more prior information maps. The functional prediction map predicts characteristics, such as those sensed by the one or more field sensors or related characteristics, at different geographical locations in the field. The functional prediction map generated during the harvesting operation can be used to automatically control a harvester during the harvesting operation. For example, the functional prediction map can be used to control a screen, a chaff screen, a cleaning fan, a threshing rotor, and a concave plate. The functional prediction map can also be provided to an operator or another user.

[0036] Figure 1 is a partially painted, partially schematic illustration of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. In addition, although a combine harvester is provided as an example throughout this disclosure, it will be understood that this specification also applies to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvesters, reapers or other agricultural work machines. Therefore, the present disclosure is intended to cover the various types of harvesters described and is not therefore limited to combine harvesters. In addition, the present disclosure relates to other types of work machines (such as agricultural seeders and sprayers), construction equipment, forestry equipment and turf management equipment that may be applicable to generating prediction maps. Therefore, the present disclosure is intended to cover these various types of harvesters and other work machines and is not therefore limited to combine harvesters.

[0037] like Figure 1As shown in FIG, the agricultural harvester 100 illustratively includes an operator cab 101 that can have a variety of 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, generally indicated as 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher, generally indicated as 110. The feeder housing 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to a frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive movement of the header 102 about the axis 105 in a direction generally indicated by arrow 109. Thus, the vertical position of the header 102 above the ground 111 on which the header 102 travels (header height) is controllable by actuating the actuator 107. Figure 1 Although not shown in the drawings, the agricultural harvester 100 may also include one or more actuators that operate to apply a pitch angle, a roll angle, or both a pitch angle and a roll angle to the header 102 or portions of the header 102. Pitch refers to the angle at which the cutterheads 104 engage the crop. For example, the pitch angle can be increased by controlling the header 102 so that the distal edges 113 of the cutterheads 104 point more toward the ground. The pitch angle can be decreased by controlling the header 102 so that the distal edges 113 of the cutterheads 104 point further away from the ground. The roll angle refers to the orientation of the header 102 about the front-to-back longitudinal axis of the agricultural harvester 100.

[0038] The thresher 110 illustratively includes a threshing rotor 112 and a set of concave plates 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a grain cleaning subsystem or cleaner (collectively referred to as a grain cleaning subsystem 118), which includes a grain cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a tailings elevator 128, a clean grain elevator 130, and a discharge auger 134 and a 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, which may include a chopper 140 and a spreader 142. The combine harvester 100 also includes a propulsion subsystem, which includes an engine that drives ground-engaging components 144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, the agricultural harvester 100 may have left and right grain cleaning subsystems, separators, etc. Figure 1 Not shown in the figure.

[0039] 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 collects the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the agricultural harvester 100 can determine one or more of a height setting, a pitch setting, or a roll setting for the header 102. For example, the operator inputs one or more settings to a control system that controls the actuators 107, which are described in more detail below. The control system can also receive settings from the operator to establish the pitch and roll angles of the header 102, and implement the input settings by controlling associated actuators (not shown) that operate to change the pitch and roll angles of the header 102. Actuator 107 maintains header 102 at a height above ground 111 based on the height setting and, where applicable, at a desired pitch and roll angle. Each of the height, roll, and pitch settings can be implemented independently of the other settings. The control system responds to header errors (e.g., the difference between the height setting and the measured height of header 102 above ground 111, and in some examples, pitch and roll angle errors) with a responsiveness determined based on the 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 the detected errors more quickly than if the sensitivity level is set to a lower sensitivity level.

[0040] Returning to the description of the operation of agricultural harvester 100, after the crop is cut by cutter 104, the cut crop material is moved within feeder housing 106 via a conveyor toward feed accelerator 108, which accelerates the crop material into thresher 110. The crop material is threshed by rotating the crop against concave plate 114 via rotor 112. The threshed crop material is moved by separator rotors in separator 116, where a portion of the residue is moved through discharge agitator 126 toward residue subsystem 138. The residue portion delivered to residue subsystem 138 is chopped by residue chopper 140 and spread over the field by spreader 142. In other configurations, the residue is discharged from agricultural harvester 100 in a pile. In other examples, residue subsystem 138 may include a weed seed eliminator (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0041] The grain falls to the cleaning subsystem 118. A chaff screen 122 separates some larger grain pieces, and a screen 124 separates some finer grain pieces from the clean grain. The clean grain falls to an auger that moves the grain to the inlet end of a clean grain elevator 130. Clean grain elevator 130 moves the clean grain upward, depositing it in a clean grain bin 132. Residue is removed from the cleaning subsystem 118 by an airflow generated by a cleaning fan 120. The cleaning fan 120 directs air upward along an airflow path through the screen and chaff screen. This airflow transports the residue backward within the agricultural harvester 100 toward a residue handling subsystem 138.

[0042] The tailings elevator 128 returns the tailings to the thresher 110 where they are re-threshed. Alternatively, the tailings may be transported by the tailings elevator or another transport device to a separate re-threshing mechanism where they are also re-threshed.

[0043] Figure 1 Also shown, in one example, the agricultural harvester 100 includes a ground 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 grain cleaning subsystem 118.

[0044] The ground speed sensor 146 senses the speed of travel of the agricultural harvester 100 on the ground. The ground 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, travel speed 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, a Doppler effect speed sensor or a variety of other systems or sensors that provide an indication of travel speed. The ground speed sensor 146 can also include a direction sensor, such as a compass, a magnetometer, a gravity sensor, a gyroscope, a GPS derivation, to determine a two-dimensional or three-dimensional direction of travel in combination with speed. In this way, when the agricultural harvester 100 is located on a slope, the orientation of the agricultural harvester 100 relative to the slope is known. For example, the orientation of the agricultural harvester 100 can include rising, falling or traveling laterally along the slope. When referred to in this disclosure, machine or ground speed may also include two-dimensional or three-dimensional direction of travel.

[0045] Loss sensors 152 illustratively provide output signals indicating the amount of grain loss occurring on the left and right sides of cleaning subsystem 118. In some examples, sensors 152 are strike sensors that count grain strikes per unit time or per unit distance traveled to provide an indication of grain loss occurring at cleaning subsystem 118. The strike sensors on the right and left sides of cleaning subsystem 118 can provide separate signals or a combined or aggregated signal. In some examples, rather than providing separate sensors for each cleaning subsystem 118, sensor 152 can comprise a single sensor.

[0046] The splitter loss sensor 148 provides an indication of the left and right splitters (at Figure 1 The separator loss sensor 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregated signal. In some cases, sensing grain loss in the separators may also be performed using a variety of different types of sensors.

[0047] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may 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 stability sensor that senses the oscillatory or jerky motion (and amplitude) of the agricultural harvester 100; a residue setting sensor that is configured to sense whether the agricultural harvester 100 is configured to chop residue, generate a windrow, etc.; a grain cleaner fan speed sensor that senses the speed of the fan 120; a concave plate gap sensor that senses the gap between the rotor 112 and the concave plate 114; a threshing rotor speed sensor that senses the rotation speed of the rotor 112; and a threshing rotor speed sensor that senses the rotation speed of the rotor 112. The agricultural harvester 100 may include a chaff screen gap sensor that senses the size of the opening in the chaff screen 122; a screen gap sensor that senses the size of the opening in the screen 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 that are 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 types of crop properties, such as crop type, crop moisture, and other crop properties. The crop property sensor is also configured to sense characteristics of the cut crop material while the agricultural harvester 100 is processing the crop material. For example, in some cases, the crop property sensor can sense: grain quality, such as cracked grain, MOG level; grain composition, such as starch and protein; and grain feed rate as the grain travels through the feeder housing 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensor may also sense the feed rate of biomass through the feeder housing 106, through the separator 116, or elsewhere in the agricultural harvester 100. The crop property sensor may also sense the feed rate as a 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. An internal material distribution sensor may sense material distribution within the agricultural harvester 100.

[0048] Examples of sensors for detecting or sensing power characteristics include, but are not limited to, voltage sensors, current sensors, torque sensors, hydraulic pressure sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors. Power characteristics can be measured at varying levels of granularity. For example, power usage can be sensed within the machine being sensed, within a subsystem, or by individual parts of a subsystem.

[0049] Examples of sensors for detecting one or more of ear, head, or pod characteristics (EHP characteristics) or kernel size include, but are not limited to, one or more of a camera, a capacitive sensor, a piezoelectric patch, an electromagnetic or ultrasonic time-of-flight reflectance sensor, a signal attenuation sensor, a weight or mass sensor, a material flow sensor, etc. These sensors may be placed at one or more locations in the agricultural harvester 100 to sense the distribution of material in the agricultural harvester 100 during operation of the agricultural harvester 100.

[0050] Examples of sensors for detecting or sensing the pitch or roll of the agricultural harvester 100 include accelerometers, gyroscopes, inertial measurement units, gravity sensors, magnetometers, etc. These sensors can also indicate the slope of the terrain on which the agricultural harvester 100 is currently located.

[0051] The crop handling system includes systems for handling crops and their components, and can vary depending on whether the agricultural harvester is a combine, a self-propelled forage harvester, a sugarcane harvester, a cotton harvester, a hay harvester, a timber harvester, or other harvester. Functions performed by the crop handling system include one or more of separating plant material from roots, separating plant material from stems, separating desired material from undesirable material, cutting material to size, and aggregating material. In a combine harvester, in some examples, the crop handling system may include, without limitation, one or more of a threshing rotor 112, a concave plate 114, a screen 124, a chaff screen 122, and a cleaning fan 120.

[0052] Before describing how the agricultural harvester 100 generates a functional prediction map and uses the functional prediction map for control, a brief description of some items on the agricultural harvester 100 and their operation will first be described. Figure 2The description of FIG3 and FIG4 describes receiving a priori information graph of a general type and combining information from the priori information graph with georeferenced sensor signals generated by field sensors, where the sensor signals may indicate characteristics of harvested material. The harvested material may include kernels, pods, heads, and ears. The sensor signals may indicate agricultural characteristics, such as one or more of field characteristics, crop characteristics, grain characteristics, or characteristics of the agricultural harvester 100. "Field" characteristics may include, but are not limited to, field characteristics such as slope, weed intensity, weed type, soil moisture, and surface quality. Crop properties may include, but are not limited to, kernel size, EHP characteristics, crop height, crop moisture, grain quality, crop density, and crop state. Kernel characteristics may include, but are not limited to, grain moisture, grain size, and grain test weight; and agricultural harvester 100 characteristics may include, but are not limited to, orientation, loss level, operating quality, fuel consumption, internal distribution, tailings characteristics, and power utilization. Relationships between characteristic values ​​obtained from the field sensor signals and the priori information graph values ​​are identified, and these relationships are used to generate new functional prediction graphs. The function prediction map predicts the values ​​at different geographical locations in the field, and one or more of those values ​​can be used to control the machine. In some cases, the function prediction map can be presented to a user, such as an operator of an agricultural machine, which can be an agricultural harvester. The function prediction map can be presented to the user visually (such as through a display), tactilely, or auditorily. The user can interact with the function prediction map to perform editing operations and other user interface operations. In some cases, the function prediction map can be used to control an agricultural machine (such as an agricultural harvester), presented to an operator or other user, or used to be presented to an operator or user for operator or user interaction.

[0053] In reference Figure 2 After describing the overall method with FIG. 3, referring to FIG. 4 and Figure 5 A more specific method is described for generating a functional predictive speed map that can be presented to an operator or user, used to control the agricultural harvester 100, or both. Furthermore, while the present discussion continues with respect to agricultural harvesters (and, in particular, combine harvesters), the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural working machines.

[0054] Figure 2 is a block diagram illustrating portions of an example agricultural harvester 100 . Figure 2As shown, the agricultural harvester 100 exemplarily includes one or more processors or servers 201, a data storage device 202, a geographic location sensor 204, a communication system 206, and one or more field sensors 208, which sense one or more agricultural characteristics simultaneously with the harvesting operation. Agricultural characteristics can include any characteristics that may affect the harvesting operation. Some examples of agricultural characteristics include characteristics of the agricultural harvester, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a prediction model or relationship generator (hereinafter collectively referred to as a "prediction model generator 210"), a prediction 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 may also include a variety of other agricultural harvester functions 220. For example, field sensors 208 include onboard sensors 222, remote sensors 224, and other sensors 226 that sense characteristics during the course of agricultural operations. Predictive model generator 210 illustratively includes a prior information variable-to-in-situ variable model generator 228, and predictive model generator 210 may include other objects 230. 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 controller 240, a deck position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and system 214 may include other objects 246. Controllable subsystems 216 include machine and header actuators 248 , a propulsion subsystem 250 , a steering subsystem 252 , a residue subsystem 138 , a machine cleanup subsystem 254 , and subsystems 216 may include a variety of other subsystems 256 .

[0055] Figure 2 Also shown is that the agricultural harvester 100 can receive prior information maps 258. As described below, the prior information maps 258 include, for example, maps from previous operations in the field, such as an unmanned or manned aircraft or another land vehicle. The prior information maps 258 can include one or more of a sowing map, a vegetation index (VI) map, a yield map, a biomass map, a weed map, or another map. However, the prior map information can also include other types of data obtained before the harvesting operation or maps from previous operations. Figure 2Also shown is that operator 260 can operate agricultural harvester 100.Operator 260 interacts with operator interface mechanism 218.In some examples, operator interface mechanism 218 can comprise user-actuated elements (such as icons, buttons, etc.) on joystick, lever, steering wheel, linkage, pedal, button, dial, keyboard, user interface display device, microphone and loudspeaker (wherein provided with speech recognition and speech synthesis) and various other types of control devices.When a touch-sensitive display system is provided, operator 260 can use touch gestures to interact with operator interface mechanism 218.These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure.Therefore, other types of operator interface mechanism 218 can be used and are within the scope of the present disclosure.However, previous graph information can also cover other types of data obtained before the harvesting operation or the graph from the previous operation. Figure 2 Also shown is that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include user-actuated elements (such as icons, buttons, etc.) on a joystick, lever, steering wheel, linkage, pedals, buttons, dials, keyboards, user interface display devices, microphones and speakers (with voice recognition and voice synthesis), and various other types of control devices. In the case of a touch-sensitive display system, operator 260 can interact with 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 this disclosure. Therefore, other types of operator interface mechanisms 218 can be used and are within the scope of this disclosure.

[0056] The prior information map 258 can be downloaded to the agricultural harvester 100 using the communication system 206 or in other ways and stored in the data storage 202. In some examples, the communication system 206 can be a cellular communication system, a system for communicating on a wide area network or a local area network, a system for communicating on a near field communication network, a communication system configured to communicate on 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.

[0057] The geographic location sensor 204 illustratively senses or detects the geographic location or orientation of the agricultural harvester 100. The geographic location sensor 204 may include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from GNSS satellite transmitters. The geographic location sensor 204 may also include a real-time kinematic (RTK) component configured to improve the accuracy of position data derived from the GNSS signals. The geographic location sensor 204 may include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic location sensors.

[0058] The field sensor 208 may be the one referenced above. Figure 1 Any of the sensors described herein. The field sensors 208 include onboard sensors 222 that are mounted onboard the agricultural harvester 100. Such sensors may include, for example, a speed sensor (e.g., a GPS, a speedometer, or a compass), an image sensor located within the agricultural harvester 100 (such as one or more clean grain cameras mounted to identify one or more of material distribution, kernel size, or EHP characteristics in the agricultural harvester 100 (e.g., in a residue subsystem or in a clean grain system), or other kernel size sensors or EHP characteristic sensors. 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 harvesting operations.

[0059] Predictive model generator 210 generates a model that indicates the relationship between the values ​​sensed by field sensors 208 and the characteristics mapped to the field by prior information map 258. For example, if prior information map 258 maps a yield characteristic to different locations in the field, and field sensors 208 sense values ​​indicating kernel size, prior information variable to field variable model generator 228 generates a predictive model that models the relationship between the yield characteristic and kernel size. Predictive machine models can also be generated based on characteristics from one or more prior information maps 258 and one or more field data values ​​generated by field sensors 208. Predictive map generator 212 then uses the predictive model generated by predictive model generator 210 to generate a functional prediction map 263 based on prior information map 258. The functional prediction map 263 predicts the value of the characteristic (such as kernel size or EHP characteristic) sensed by field sensors 208 at different locations in the field.

[0060] In some examples, the type of values ​​in the function prediction graph 263 may be the same as the type of field data sensed by the field sensor 208. In some cases, the type of values ​​in the function prediction graph 263 may have different units than the data sensed by the field sensor 208. In some examples, the type of values ​​in the function prediction graph 263 may be different from the type of data sensed by the field sensor 208, but may be related to the type of data sensed by the field sensor 208. For example, in some examples, the type of data sensed by the field sensor 208 may indicate the type of values ​​in the function prediction graph 263. In some examples, the type of data in the function prediction graph 263 may be different from the type of data in the prior information graph 258. In some cases, the type of data in the function prediction graph 263 may have different units than the data in the prior information graph 258. In some examples, the type of data in the function prediction graph 263 may be different from the type of data in the prior information graph 258, but may be related to the type of data in the prior information graph 258. For example, in some examples, the type of data in the prior information graph 258 may indicate the type of data in the function prediction graph 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 prior information graph 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 prior information graph 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 prior information graph 258 and different from the other.

[0061] The prediction map generator 212 can use the characteristics in the prior information map 258 and the model generated by the prediction model generator 210 to generate a functional prediction map 263 that predicts the characteristics at different locations in the field. Thus, the prediction map generator 212 outputs a prediction map 264.

[0062] like Figure 2As shown in FIG, prediction map 264 is based on the prior information values ​​at various locations across the field in prior information map 258 and uses a prediction model to predict the value of a sensed characteristic (sensed by field sensor 208) at those locations, or a characteristic related to the sensed characteristic. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between a yield characteristic and kernel size, then, given the yield characteristics at different locations across the field, prediction map generator 212 generates prediction map 264 that predicts kernel size values ​​at different locations across the field. The yield characteristics at those locations obtained from the yield map, and the relationship between the yield characteristics and kernel size obtained from the prediction model, are used to generate prediction map 264. The predicted kernel size can be used by the control system to adjust, for example, one or more of the screen and chaff screen openings, rotor operation, concave plate gap, or cleaning fan speed.

[0063] We will now describe some of the variations in the types of data mapped in the prior information graph 258, the types of data sensed by the field sensors 208, and the types of data predicted on the prediction graph 264. These are merely examples illustrating that the types of data may be the same or different.

[0064] In some examples, the data type in the prior information map 258 is different from the data type sensed by the field sensors 208, while the data type in the prediction map 264 is the same as the data type sensed by the field sensors 208. For example, the prior information map 258 may be a topographic map, and the variables sensed by the field sensors 208 may be grain quality characteristics. The prediction map 264 may then be a predicted machine map that maps predicted machine characteristic values ​​to different geographic locations in the field.

[0065] Furthermore, in some examples, the data type in prior information map 258 is different from the data type sensed by field sensors 208, and the data type in prediction map 264 is different from both the data type in prior information map 258 and the data type sensed by field sensors 208. For example, prior information map 258 may be a topographic map, and the variable sensed by field sensors 208 may be machine pitch / roll. Prediction map 264 may then be a predicted internal distribution map that maps predicted internal distribution values ​​to different geographic locations in the field.

[0066] In some examples, prior information map 258 is generated based on previous operations on the field, and the data type is different from the data type sensed by field sensors 208, while the data type in prediction map 264 is the same as the data type sensed by field sensors 208. For example, prior information map 258 may be a seed genotype map generated during planting, and the variable sensed by field sensors 208 may be loss. Prediction map 264 may then be a predicted loss map that maps predicted grain loss values ​​to different geographic locations in the field. In another example, prior information map 258 may be a seed genotype map, and the variable sensed by field sensors 208 may be crop state, such as standing or fallen crops. Prediction map 264 may then be a predicted crop state map that maps predicted crop state values ​​to different geographic locations in the field.

[0067] In some examples, prior information map 258 is generated based on previous operations on the field and the data type is the same as the data type sensed by field sensors 208, and the data type in prediction map 264 is also the same as the data type sensed by field sensors 208. For example, prior information map 258 may be a yield map generated during the previous year, and the variable sensed by field sensors 208 may be yield. Prediction map 264 may then be a predicted yield map that maps predicted yield values ​​to different geographic locations within the field. In such an example, the relative yield differences between the georeferenced prior information map 258 and the previous year can be used by prediction model generator 210 to generate a prediction model that models the relationship between the relative yield differences on prior information map 258 and the yield values ​​sensed by field sensors 208 during the current harvesting operation. The prediction model is then used by prediction map generator 210 to generate a predicted yield map.

[0068] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups consecutive individual point data values ​​on prediction map 264 into control zones. A control zone may include two or more consecutive portions of an area (such as a field) for which the control parameters corresponding to the control zone used to control the controllable subsystems are constant. For example, the response time for changing the settings of controllable subsystem 216 may be insufficient to satisfactorily respond to changes in the values ​​contained in a map (such as prediction map 264). In that case, control zone generator 213 parses the map and identifies control zones with a defined size to accommodate the response time of controllable subsystem 216. In another example, the control zones may be sized to reduce wear caused by excessive actuator movement due to continuous adjustments. In some examples, different groups of control zones may exist for each controllable subsystem 216 or for groups of controllable subsystems 216. Control zones may be added to prediction map 264 to obtain predicted control zone map 265. Thus, the predicted control area map 265 can be similar to the predicted map 264, except that the predicted control area map 265 includes control area information that defines the control area. Thus, the functional predicted map 263 as described herein may include or exclude control areas. Both the predicted map 264 and the predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include control areas, such as the predicted map 264. In another example, the functional predicted map 263 includes control areas, such as the predicted control area map 265. In some examples, if an intercropping production system is implemented, multiple crops can be present in the field at the same time. In that case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops and then generate the predicted control area map 265 and the predicted map 264 accordingly.

[0069] It will also be understood that the control zone generator 213 can cluster the values ​​to generate the control zones, and the control zones can be added to the predicted control zone graph 265 or a separate graph showing only the generated control zones. In some examples, the control zones can be used only 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 only to the operator 260 or another user, or stored for later use.

[0070] The prediction map 264 or the prediction control area map 265, or both, is provided to the control system 214, which generates control signals based on the prediction map 264 or the prediction control area 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 area map 265, or control signals based on the prediction map 264 or the prediction control area map 265, to other agricultural harvesters 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 area map 265, or both to other remote systems.

[0071] In some examples, the prediction map 264 can be provided to a route / task generator 267. The route / task generator 267 plots a path for the agricultural harvester 100 to travel during a harvesting operation based on the prediction map 264. The path of travel can also include machine control settings corresponding to locations along the path of travel. For example, if the path of travel rises along a hill, then at a point before rising along the hill, the path of travel can include controls for directing power to the propulsion system to maintain the speed or feed rate of the agricultural harvester 100. In some examples, the route / task generator 267 analyzes different orientations and predicted machine characteristics of the agricultural harvester 100 for multiple different routes of travel (i.e., predicting the orientation based on the prediction map 264 to predict the machine characteristics that will be generated) and selects a route with a desired result (such as a fast harvesting time or a desired power utilization or material distribution uniformity).

[0072] The operator interface controller 231 is operable to generate control signals for controlling the operator interface mechanism 218. The operator interface controller 231 is also operable to present a prediction map 264 or a 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 may be a local operator or a remote operator. For example, the controller 231 generates control signals to control the display mechanism to display one or both of the prediction map 264 and the prediction control zone map 265 for the operator 260. The controller 231 may generate operator-actuable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. For example, the operator can edit the map by correcting the power utilization displayed on the map based on the operator's observations. The setting controller 232 may 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 setting 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, screen and chaff screen settings, concave plate gap, threshing rotor settings, cleaning fan speed settings, header height, header function, reel speed, reel position, conveyor function (where the agricultural harvester 100 is coupled to a conveyor header), corn header function, internal distribution control, and one or more of 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 path 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 path. The feed rate controller 236 can control various subsystems (such as the propulsion subsystem 250 and the machine actuator 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 downhill terrain with an estimated rotational speed 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 biomass through the agricultural harvester 100. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The conveyor belt controller 240 can generate control signals to control the conveyor belt or other conveyor functions based on the prediction map 264, the prediction control zone map 265, or both. For example, when the agricultural harvester 100 approaches a downhill terrain with an estimated rotational speed value above a selected threshold, the conveyor belt controller 240 can increase the speed of the conveyor belt to prevent material from accumulating on the belt.The deck position controller 242 can generate control signals to control the position of a deck included on the harvesting deck based on the prediction map 264 or the prediction control zone map 265, or both, and 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 clearing controller 245 can generate control signals for controlling the machine clearing subsystem 254. For example, if the agricultural harvester 100 is about to travel laterally on a slope where internal material is estimated to be disproportionately distributed on one side of the clearing subsystem 254, the machine clearing controller 245 can adjust the clearing subsystem 254 to account for or correct for the disproportionate material. 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.

[0073] Figure 3A and Figure 3B (collectively referred to herein as FIG. 3 ) shows a flow chart illustrating one example of the operation of the agricultural harvester 100 when generating a prediction map 264 and a prediction control zone map 265 based on the prior information map 258 .

[0074] At 280, the agricultural harvester 100 receives a priori information map 258. The priori information map 258, or an example of receiving the priori information map 258, is discussed with reference to blocks 281, 282, 284, and 286. As discussed above, the priori information map 258 maps the 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 priori information map 258 may include selecting one or more of a plurality of possible information maps available. For example, one priori information map may be a relief contour map generated using an aerial phase contour imaging method. Another priori information map may be a map generated during a previous pass through the field, which may have been performed by a different machine, such as a sprayer or other machine, performing a previous operation in the field. The process of selecting one or more priori information maps may be manual, semi-automated, or automated. The priori 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 by a GPS receiver installed on a piece of equipment during previous field operations. For example, the data can be collected in a lidar range scanning operation in the previous year or early in the current growing season or at other times. The data can be based on data detected or received in a manner other than using a lidar range scanning. For example, a drone equipped with a fringe projection profilometry system can detect the contour or altitude of the terrain. Or, for example, some terrain features can be estimated based on weather patterns, such as the formation of ruts caused by erosion or the splitting of clumps in freeze-thaw cycles. In some examples, the prior information map 258 can be generated by combining data from many sources (such as those listed above). Or, for example, data for the prior information map 258 (such as a topographic map) can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data memory 202. Data for the a priori information map 258 may also be provided to the agricultural harvester 100 in other ways using the communication system 206 , and this is indicated in the flowchart of FIG 3 by block 286 . In some examples, the a priori information map 258 may be received by the communication system 206 .

[0075] At the start of a harvesting operation, field sensors 208 generate sensor signals indicative of one or more field data values ​​indicative of machine characteristics (e.g., kernel size and EHP characteristics). Examples of field sensors 208 are discussed with reference to blocks 222, 290, and 226. As explained above, field sensors 208 include: onboard sensors 222; remote field sensors 224, such as UAV-based sensors that collect field data during a flight, as shown in block 290; or other types of field sensors designated by field sensors 226. In some examples, data from the onboard sensors is georeferenced using position, heading, or speed data from geolocation sensor 204.

[0076] The predictive model generator 210 controls the prior information variable to field variable model generator 228 to generate a model that models the relationship between the mapped values ​​contained in the prior information map 258 and the field values ​​sensed by the field sensors 208, as indicated by block 292. The characteristics of the data types represented by the mapped values ​​in the prior information map 258 and the field values ​​sensed by the field sensors 208 may be the same characteristics or data types or different characteristics or data types.

[0077] The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212. The predictive map generator 212 generates a predictive map 264 that uses the predictive model and the prior information map 258 to predict the value of the characteristic sensed by the field sensor 208, or the value of different characteristics related to the characteristic sensed by the field sensor 208, at different geographic locations in the field being harvested, as indicated by box 294.

[0078] It should be noted that in some examples, the prior information map 258 may include two or more different maps, or two or more different layers of a single map. Each of the two or more different maps, or each layer of the two or more different layers of a single map, maps a different type of variable to a geographic location in the field. In such an example, the prediction model generator 210 generates a prediction model that models the relationship between the field data and each of the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensor 208 may include two or more sensors, each of which senses a different type of variable. Thus, the prediction model generator 210 generates a prediction model that models the relationship between each type of variable mapped by the prior information map 258 and each type of variable sensed by the field sensor 208. The prediction map generator 212 can generate a functional prediction map 263, which uses the prediction model and each of the maps or layers in the prior information map 258 to predict the value of each sensed characteristic (or characteristic related to the sensed characteristic) sensed by the field sensor 208 at different locations in the field being harvested.

[0079] The predictive map generator 212 configures the predictive map 264 so that the predictive map 264 can be executed (or used) by the control system 214. The predictive map generator 212 can provide the predictive map 264 to the control system 214 or the control zone generator 213, or both. Some examples of different ways in which the predictive map 264 can be configured or output are described with reference to blocks 296, 293, 295, 299, and 297. For example, the predictive map generator 212 configures the predictive map 264 so that the predictive map 264 includes values ​​that can be read by the control system 214 and used as a basis for generating control signals for one or more of the different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

[0080] The route / task generator 267 plots the path of travel for the agricultural harvester 100 during harvesting operations based on the prediction map 204, as indicated by box 293. The control zone generator 213 can divide the prediction map 264 into control zones based on the values ​​on the prediction map 264. Consecutive geolocation values ​​within a threshold of each other can be grouped into control zones. The threshold can be a default threshold, or the threshold can be set based on operator input, based on input from an automated system, or based on other criteria. The size of each zone can be based on the response of the control system 214, the controllable subsystem 216, or based on wear considerations, or based on other criteria, as indicated by box 295. The control zone generator 213 can configure the predicted control zone map 265 for presentation to the operator or other user. This is indicated by box 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 ​​associated with the geographic location on the prediction map 264, the control zones associated with the geographic location on the prediction control zone map 265, and settings 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 concise information or more detailed information. The presentation can also include a confidence level indicating the accuracy of the degree of agreement between the predicted values ​​on the prediction map 264 or zones on the prediction control zone map 265 and the measured values ​​that can be measured by sensors on the agricultural harvester 100 as the agricultural harvester 100 moves through the field. In addition, where information is presented in more than one location, an authentication or authorization system can be provided to implement the authentication and authorization process. For example, there can be a level of individuals who are authorized to view and change the various maps and other presented information. By way of example, the onboard display device may only show the various graphs locally on the machine in near real-time, or the graphs may also be generated at one or more remote locations. In some examples, each physical display device at each location may be associated with a personnel or user permission level. User permission levels may be used to determine which display indicators are visible on the physical display device and which values ​​the corresponding personnel can change. As an example, a local operator of machine 100 may not be able to see information corresponding to predictive graph 264 or make any changes to machine operation. However, a monitor at a remote location may be able to see predictive graph 264 on the display but not make any changes. A manager, perhaps at a separate remote location, may be able to see all elements of predictive graph 264 and may also change predictive graph 264 used in machine control. This is one example of possible authorization levels. Predictive graph 264, predictive control zone graph 265, or both may also be configured in other ways, as indicated by box 297.

[0081] At block 298, input from the geolocation sensor 204 and other field sensors 208 is received by the control system. Block 300 represents the control system 214 receiving input from the geolocation sensor 204 identifying the geographic location of the agricultural harvester 100. Block 302 represents the control system 214 receiving sensor input indicating the track or heading of the agricultural harvester 100, and block 304 represents the control system 214 receiving the speed of the agricultural harvester 100. Block 306 represents the control system 214 receiving other information from various field sensors 208.

[0082] At block 308, the control system 214 generates control signals to control the controllable subsystems 216 based on the forecast map 264 or the forecast control zone map 265, or both, and inputs from the geolocation sensor 204 and any other field sensors 208. At block 310, the control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the specific control signals generated and the specific controllable subsystems 216 controlled can vary based on one or more different factors. For example, the control signals generated and the controllable subsystems 216 controlled can be based on the type of forecast map 264 or the forecast control zone map 265, or both, being used. Similarly, the control signals generated and the controllable subsystems 216 controlled, as well as the timing of the control signals, can be based on various delays in the crop flow through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[0083] By way of example, the generated prediction map 264 in the form of a predicted grain size map can be used to control one or more subsystems 216. For example, the functional predicted grain size map can include grain size values ​​geo-referenced to locations in a field being harvested. The grain size values ​​from the functional predicted grain size map can be extracted and used to control the fan speed to ensure that the cleaning fan 120 minimizes crop loss through the cleaning subsystem 118 as the agricultural harvester 100 moves through the field. For example, the cleaning fan 120 can be controlled by reducing the fan speed to avoid blowing small grains out of the agricultural harvester 100. In another example, the prediction map 264 is an EHP characteristic map that includes EHP characteristic values ​​indicating diseased crops geo-referenced to different locations in the field being harvested. The EHP characteristic values ​​can be extracted and used to control the speed of the cleaning fan 120 by increasing the fan speed to blow the diseased crops out of the agricultural harvester 100. The aforementioned examples involving the use of predicted grain size maps and predicted EHP characteristic maps are provided by way of example only. Thus, a variety of other control signals may be generated using values ​​obtained from a predictive machine map or other types of predictive maps to control one or more of the controllable subsystems 216 .

[0084] At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not complete, processing proceeds to block 314 where field sensor data from the geolocation sensor 204 and the field sensor 208 (and possibly other sensors) continues to be read.

[0085] In some examples, at box 316, the agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the prediction map 264, the prediction control zone map 265, the model generated by the prediction model generator 210, the zone generated by the control zone generator 213, one or more control algorithms executed by the controller in the control system 214, and other triggered learning.

[0086] The learning trigger criteria can include any of a variety of different criteria. Some examples of detecting trigger criteria 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 sensor 208, the triggered learning can involve recreating the relationships used to generate the prediction model. In such an example, the amount of field sensor data received from the field sensor 208 exceeding a threshold value 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, the threshold amount of field sensor data received from the field sensor 208 triggers the creation of a new relationship represented by the prediction model generated by the prediction model generator 210. In addition, a new prediction map 264, a prediction control area map 265, or both can be regenerated using the new prediction model. Block 318 represents detecting a threshold amount of field sensor data used to trigger the creation of a new prediction model.

[0087] In other examples, the learning trigger criteria can be based on the degree to which the field sensor data from the field sensor 208 has changed from a previous value or threshold. For example, if the change in the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a range, less than a defined amount, or below a threshold, the predictive model generator 210 does not generate a new predictive model. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if, for example, the change in the field sensor data exceeds the range or exceeds the defined amount or the threshold, or if, for example, the relationship between the field sensor data and the information in the prior information map 258 changes by a defined amount, the predictive model generator 210 generates a new predictive model using all or a portion of the newly received field sensor data used by the predictive map generator 212 to generate the new predictive map 264. At block 320, changes in the field sensor data (such as the magnitude by which the data exceeds a selected range, or the magnitude of a change in the relationship between the field sensor data and the information in the prior information map 258) can be used as triggers to cause the generation of new predictive models and predictive maps. The thresholds, ranges, and limits can be set to default values; set by an operator or user via user interface interaction; set by an automated system; or set in other ways.

[0088] Other learning triggering criteria may also be used. For example, if the prediction model generator 210 switches to a different prior information map (different from the initially selected prior information map 258), then switching to the different prior information map may trigger relearning by the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control system 214, or other entities. In another example, the agricultural harvester 100 switching to different terrain or a different control zone may also be used as a learning triggering criterion.

[0089] In some cases, operator 260 may also edit prediction map 264 or prediction control zone map 265, or both. The edits may change the values ​​on prediction map 264, or the size, shape, location, presence, or value of the control zones on prediction control zone map 265, or both. Block 321 shows that the edited information may be used as a learning trigger criterion.

[0090] In some cases, the operator 260 may also observe that the automated control of the controllable subsystem is not what the operator desired. In these cases, the operator 260 may provide operator-initiated adjustments to the controllable subsystem, reflecting the operator 260's desire for the controllable subsystem to operate in a different manner than that being commanded by the control system 214. Thus, the operator-initiated changes to the settings made by the operator 260 may cause the predictive model generator 210 to relearn the model, the predictive map generator 212 to regenerate the map 264, the control zone generator 213 to regenerate the control zones on the predictive control zone map 265, and the control system 214 to relearn its control algorithm or perform machine learning on one of the controller components 232 to 246 in the control system 214, as shown in block 322, based on the operator 260's adjustments. Block 324 represents the use of other triggered learning criteria.

[0091] In other examples, relearning may be performed periodically or intermittently, for example, based on selected time intervals, such as discrete time intervals or variable time intervals. This is indicated by block 326.

[0092] As indicated by block 326, if relearning is triggered (whether based on a learning triggering criterion or based on the passage of a time interval), one or more of the predictive model generator 210, the predictive map generator 212, the control zone generator 213, and the control system 214 performs machine learning based on the learning triggering criterion to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Execution of the relearning is indicated by block 328.

[0093] If harvesting operations have been completed, then operations move from block 312 to block 330 where one or more of the forecast map 264, the forecast control area map 265, and the forecast model generated by the forecast model generator 210 are stored. The forecast map 264, the forecast control area map 265, and the forecast model may be stored locally on the data store 202 or sent to a remote system using the communication system 206 for later use.

[0094] It will be noted that although some examples herein describe the prediction model generator 210 and the prediction map generator 212 receiving prior information maps when generating the prediction model and the functional prediction map, respectively, in other examples, the prediction model generator 210 and the prediction map generator 212 may receive other types of maps when generating the prediction model and the functional prediction map, respectively, including prediction maps, such as functional prediction maps generated during harvesting operations.

[0095] Figure 4A yes Figure 1 1 is a block diagram of a portion of an agricultural harvester 100 shown in FIG. In particular, among other things, Figure 4A An example of the prediction map generator 212 is shown in more detail. Figure 4A Also shown are the information flows between the various components shown. Predictive model generator 210 receives a priori information map 258. Priori information map 258 includes values ​​of agricultural characteristics corresponding to different geographic locations within a field. In some examples, priori information map 258 may include one or more of a sowing map 335, a VI map 336, a yield map 338, a biomass map 340, or other maps 342. Predictive model generator 210 also receives a geographic location 334, or an indication of a geographic location, from geographic location sensor 204. Field sensor 208 detects values ​​indicating agricultural characteristics of one or more of kernel size or EHP characteristics. Thus, field sensor 208 may include a kernel size sensor 344 for sensing kernel size of the crop being harvested, an EHP characteristic sensor 346 for sensing EHP characteristics, and one or more of processing system 352. In some cases, one or more of sensors 344 and 346 may be mounted on agricultural harvester 100. Processing system 352 processes sensor data generated from one or more sensors 344 and 346 to generate processed data 354 , some examples of which are described below.

[0096] In some examples, one or more sensors 344 and 346 can generate electronic signals indicative of the characteristics sensed by the sensors. Processing system 352 processes one or more of the sensor signals obtained via the sensors to generate processed data identifying one or more characteristics. The characteristics identified by processing system 352 can include kernel size or EHP characteristics.

[0097] The sensors 344 and 346 may be or include optical sensors, such as cameras located in the agricultural harvester 100 (referred to below as "process cameras") that view an interior portion of the agricultural harvester 100 that processes agricultural material for grain. Thus, in some examples, the processing system 352 may be operable to detect one or more of kernel size and one or more EHP characteristics of the material passing through the agricultural harvester 100 based on the images captured by the sensors 344 and 346. In other examples, the process camera may be the clean grain camera 150, and the processing system 352 may be operable to detect kernel size and the EHP characteristics.

[0098] Other sensors may also be used.In some examples, raw data or processed data from sensors 344 and 346 may be presented to operator 260 via operator interface mechanism 218. Operator 260 may be onboard agricultural harvester 100 or located at a remote location.

[0099] Figure 4B is a block diagram illustrating one example of the prediction model generator 210 in more detail. Figure 4B In the example shown in , the prediction model generator 210 may include a model generator 366 for vegetation index versus grain size, a model generator 368 for vegetation index versus EHP characteristics, a model generator 372 for sowing characteristics versus grain size, a model generator 374 for sowing characteristics versus EHP characteristics, a model generator 380 for yield versus grain size, a model generator 382 for yield versus EHP characteristics, a model generator 388 for biomass versus grain size, a model generator 390 for biomass versus EHP characteristics, a combined model generator 404, and one or more of other objects 406. Figure 4B Each of the model generators shown in generates a model that models the relationship between values ​​on an information graph 259 (which may be a priori information graph or a predictive graph or another type of graph) and values ​​sensed by field sensors 208. Combined model generator 404 can generate one or more models based on different combinations of one or more information graphs 259 and inputs from one or more field sensors 208.

[0100] Kernel size can be affected by things such as plant health (which can be indicated by a vegetation index). A model generator 366 for kernel size based on vegetation index can generate a model that models the relationship between the VI characteristic on the VI map 336 and the output of the kernel size sensor 344. Furthermore, when harvesting canola or rapeseed, for example, it can be difficult to generate a fan speed that retains all of the grain but blows out all of the fruit pods, the skin and pith of the stem, etc. The effectiveness of this fan in doing so can depend on, for example, the size and filling of the kernels. For example, in areas of poor plant health, as indicated by the vegetation index characteristic on the VI map 336, the kernels may be smaller and more likely to be blown out. Thus, the relationship between the characteristic on the VI map 336 (such as plant health) and the kernel size sensed by the sensor 344 can be used by the model generator 366 to generate a model that models the relationship.

[0101] Additionally, the VI values ​​may be related to the EHP characteristics. Thus, the vegetation index to EHP characteristics model generator 368 may generate a model that models the relationship between the values ​​on the VI map 336 and the output of the EHP characteristics sensor 346.

[0102] Different sowing characteristics (such as sowing population characteristics or seed genotype) may have an impact on grain size or otherwise share a relationship with grain size. For example, different plant genotypes can have different grain size characteristics. Population characteristics can also have an inverse relationship with the grain size obtained. Smaller grains may be more susceptible to loss. Therefore, the sowing characteristic to grain size model maker 372 can generate a relationship between the sowing characteristic value (such as population or genotype) on the sowing map 335 and the grain size sensor value generated by the grain size sensor 344.

[0103] Similarly, sowing characteristics may have an impact on or otherwise share a relationship with various EHP characteristics. For example, different genotypes may exhibit differences in the appearance of deformities, disease resistance, and damage resistance. Thus, sowing characteristic versus EHP characteristic model generator 374 generates a model that models the relationship between the sowing characteristics on sowing map 335 and the EHP characteristics sensed by EHP characteristic sensor 346.

[0104] Yield can also be related to kernel size. For example, higher yield areas in a field can indicate larger kernels than lower yield areas. Thus, the yield versus kernel size model generator 380 can generate a model that models the relationship between the predicted yield value on the yield map 338 and the output from the kernel size sensor 344.

[0105] Yield can also be related to EHP characteristics. For example, increased yield can be associated with healthier crops. Therefore, yield versus EHP characteristic model generator 382 can generate a model that models the relationship between the predicted yield value on yield map 338 and the EHP characteristic sensed by EHP characteristic sensor 346.

[0106] The amount of biomass being processed by the agricultural harvester 100 can also be related to grain size. For example, an increase in the amount of biomass being processed by the agricultural harvester 100 at a given time can indicate a more robust crop with larger grains. Thus, in some examples, various machine settings (such as the settings of screens, chaff screens, and cleaning fans) can be controlled based on grain size to reduce potential grain loss. Therefore, the biomass versus grain size model generator 388 can generate a model that models the relationship between the biomass characteristic values ​​on the biomass map 340 and the grain size sensed by the grain size sensor 344.

[0107] Biomass can also be related to EHP characteristics. Diseased, damaged, or deformed crops can have different biomass characteristics. Thus, biomass versus EHP characteristic model generator 390 can generate a model that models the relationship between the biomass characteristics on biomass map 340 and the EHP characteristics sensed by EHP characteristic sensor 346.

[0108] Other relationships may also exist.Thus, the combined model generator 404 can generate a model that models the relationship between the characteristics on one or more information graphs 259 and one or more sensors 344 and 346.

[0109] Return to Figure 4A , the prediction map generator 212 may include one or more of a kernel size map generator 410, an EHP property map generator 412, a combination map generator 414, and other items 417. A number of examples of different combinations of field sensors 208 and information maps 259 will now be described.

[0110] The prediction model generator 210 is operable to generate a prediction model 408 or a plurality of prediction models 408, such as by Figure 4B In another example, two or more of the prediction models described above can be combined into a single prediction model that predicts two or more characteristics, such as kernel size or EHP characteristics, based on characteristics at different locations in the field from one or more information graphs 259. Any of these machine models, or a combination thereof, can be combined into a single prediction model. Figure 4A are collectively represented as machine model 408.

[0111] The prediction model 408 is provided to the prediction map generator 212. Figure 4A In the example shown, the prediction map generator 212 includes a kernel size map generator 410, an EHP characteristic map generator 412, and a combination map generator 414. In other examples, the prediction map generator 212 may include additional, fewer, or different map generators. Thus, in some examples, the prediction map generator 212 may include other objects 417, which may include other types of map generators for generating maps for other types of characteristics.

[0112] The kernel size map generator 410 illustratively generates a predicted kernel size map 418 that predicts kernel size at different locations in the field based on the information values ​​at the different locations in the field indicated by the information map 259 and the prediction model 408 .

[0113] EHP characteristic map generator 412 illustratively generates a predicted EHP map 420 that predicts EHP characteristics at different locations in the field based on the characteristics at those locations in information map 259 and prediction model 408 .

[0114] Combination map generator 414 illustratively generates a predicted combination map 422 that predicts the combination of characteristics at different locations in the field based on combining information maps 259 and prediction model 408 .

[0115] Prediction map generator 212 outputs one or more prediction maps 418, 420, 422 that predict the characteristic as prediction map 264. Each of prediction maps 418, 420, 422 predicts a corresponding characteristic at a different location in the field. Prediction map 264 (which can be one or more of maps 418, 420, 422) can be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates control zones and merges those control zones into functional prediction maps (i.e., prediction maps 418, 420, 422) to provide prediction map 418 with control zones, prediction map 420 with control zones, and prediction map 422 with control zones. One or more of the prediction maps 418, 420, and 422 (with or without control zones) may be provided to the control system 214, which generates control signals to control one or more controllable subsystems 216 based on the one or more prediction maps 418, 420, and 422 (with or without control zones).

[0116] Figure 5 is a flow chart of an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating the prediction model 408 and the prediction characteristic maps 418, 420, and 422. At block 430, the prediction model generator 210 and the prediction map generator 212 receive the information map 259, which may be a Figure 4A At block 432, the processing system 352 receives one or more sensor signals from the field sensors 208. As discussed above, the field sensors may be the kernel size sensors 344 or the EHP property sensors 346.

[0117] At block 434, the processing system 352 processes the one or more received sensor signals to generate data indicative of a characteristic. In some cases, as indicated at block 436, the sensor data may indicate kernel size. In some cases, as indicated at block 438, the sensor data may indicate one or more EHP characteristics.

[0118] At block 444, the predictive model generator 210 also obtains the geographic location 334 corresponding to the sensor data. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location at which the sensor data was captured or derived based on machine latency, machine speed, etc. Additionally, at block 444, the orientation of the agricultural harvester 100 on the field can be determined. The orientation of the agricultural harvester 100 can be obtained, for example, to identify its orientation relative to a slope on the field.

[0119] At block 446 , the predictive model generator 210 generates one or more predictive models, such as the machine model 408 , that model the relationship between one or more characteristics on the information graph 259 and the characteristics being sensed by the field sensors 208 or related characteristics.

[0120] At block 448, a prediction model, such as prediction model 408, is provided to prediction map generator 212, which generates a functional prediction map that maps the predicted characteristics based on information map 259 and prediction model 408. In some examples, the functional prediction map is a predicted kernel size map 418. In some examples, the functional prediction map is a predicted EHP map 420. In some examples, the functional prediction map is a predicted combination map 422.

[0121] The functional prediction map may be generated during the course of an agricultural operation. Thus, as the agricultural harvester moves through a field to perform an agricultural operation, the functional prediction map is generated while the agricultural operation is being performed.

[0122] At block 450, the predictive map generator 212 outputs the functional predictive map. At block 452, the predictive map generator 212 outputs the functional predictive map for presentation to the operator 260 and possible interaction by the operator 260. At block 454, the predictive map generator 212 may configure the functional predictive map for use by the control system 214. At block 456, the predictive map generator 212 may also provide the functional predictive map to the control zone generator 213 for use in generating control zones. At block 458, the predictive map generator 212 may also configure the functional predictive map in other ways. The functional predictive map (with or without control zones) is provided to the control system 214. At block 460, the control system 214 generates control signals based on the functional predictive map to control the controllable subsystem 216.

[0123] The control system 124 may generate control signals to control actuators that themselves control one or more of the speed at which the screen 124 and chaff screen 122 are oscillated, the size of the openings in the screen 124 and chaff screen 122, the speed of the cleaning fan 120 and rotor 112, the rotor pressure driving the rotor 112, and the gap between the rotor 112 and the concave plate 114, or other things.

[0124] Thus, it can be seen that the system uses one or more information maps that map characteristics to different locations in the field. The system also uses one or more field sensors that sense field sensor data indicative of a characteristic and generates a model that models the relationship between the characteristic or related characteristics sensed using the field sensors and the characteristics mapped in the information map. Thus, the system uses the model, field data, and information map to generate a functional prediction map, and the generated functional prediction map can be configured for use by the control system or for presentation to a local or remote operator or other user, or both. For example, the control system can use the map to control one or more systems of an agricultural harvester.

[0125] Figure 6 A block diagram illustrating one example of a control zone generator 213 is shown. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 may also include other objects 492. The control zone generation system 488 includes a control zone standard identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other objects 520. The regime zone generation system 490 includes a regime zone standard identifier component 522, a regime zone boundary definition component 524, a setting parser identifier component 526, and other objects 528. Before describing the overall operation of the control zone generator 213 in more detail, a brief description of some of the objects in the control zone generator 213 and their corresponding operations will first be provided.

[0126] The agricultural harvester 100 or other working machine can have a variety of different types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other working machine are collectively referred to as working machine actuators (WMAs). Each WMA can be independently controllable based on a value on a functional prediction map, or the WMAs can be controlled as a set based on one or more values ​​on the functional prediction map. Therefore, the control zone generator 213 can generate a control zone corresponding to each individually controllable WMA or corresponding to a set of WMAs that are controlled in a coordinated manner.

[0127] WMA selector 486 selects the set of WMA or WMA, will generate the corresponding control zone that is used for described WMA or the set of WMA.Then, control zone generation system 488 generates the control zone that is used for selected WMA or the set of WMA.For each WMA or the set of WMA, different standards can be used when identifying the control zone.For example, for a WMA, the WMA response time can be used as the standard on the border that is used to define the control zone.In another example, wear characteristics (for example, the degree that specific actuator or mechanism are worn due to its movement) can be used as the standard on the border that is used to identify the control zone.Control zone standard identifier parts 494 identifications are used to define the specific standard of the control zone for the set of selected WMA or WMA.Control zone boundary definition parts 496 process the value on the function prediction map being analyzed, with the value on the function prediction map being analyzed and based on the control zone standard that is used for the set of selected WMA or WMA to define the border of the control zone on the function prediction map.

[0128] The target setting identifier component 498 sets the value of the target setting that will be used to control the WMA or set of WMAs in different control zones. For example, if the selected WMA is the cleaning fan 120 and the functional prediction map being analyzed is the functional prediction kernel size map 418, then the target setting in each control zone may be a target fan speed setting based on the kernel size value contained in the functional kernel size map 418 within the identified control zone.

[0129] In some examples where the agricultural harvester 100 is controlled based on its current or future location, multiple target settings for the WMA at a given location may be possible. In such cases, the target settings may have different and potentially conflicting values. Therefore, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, if the WMA is an actuator in the cleaning fan 120 that is being controlled to control the speed of the cleaning fan 120, the control zone generation system 488 takes into account the possibility of multiple different sets of conflicting criteria when identifying control zones and target settings for selected WMAs in the control zones. For example, different target settings for controlling fan speed may be generated based on, for example, a detected or predicted feed rate value, a detected or predicted grain characteristic value (such as a detected or predicted grain size characteristic value), a detected or predicted EHP characteristic value, a detected or predicted fuel efficiency value, a detected or predicted grain loss value, or a combination of these values. However, at any given time, the agricultural harvester 100 cannot control the cleaning fan 120 to operate at multiple speeds simultaneously. Instead, at any given time, the agricultural harvester 100 controls the cleaning fan 120 to operate at a single speed. Thus, one of the conflicting target settings is selected for controlling the speed of the cleaning fan 120 in the agricultural harvester 100.

[0130] Therefore, in some examples, the state zone generation system 490 generates a state zone to resolve multiple different conflicting target settings. The state zone standard identification component 522 identifies the standard for establishing the state zone for the selected WMA or the set of WMAs on the functional prediction map being analyzed. Some standards that can be used to identify or limit the state zone include, for example, crop types or crop varieties based on the planting map, or crop types or crop varieties from another source, weed types, weed intensity, crop status (such as whether the crop falls, partially falls or stands, yield, biomass, vegetation index, grain size, EHP characteristics, or terrain). Just as each WMA or the set of WMAs can have a corresponding control zone, different WMAs or the set of WMAs can have a corresponding state zone. The state zone boundary definition component 524 identifies the boundary of the state zone on the functional prediction map being analyzed based on the state zone standard identified by the state zone standard identification component 522.

[0131] In some examples, status zones can overlap with each other. For example, the EHP characteristic status zone can overlap with a part or all of the grain size status zone. In such an example, different status zones can be assigned priority levels so that in the place where two or more status zones overlap, the status zone with a larger level position or importance in the priority level is assigned priority over the status zone with a smaller level position or importance in the priority level. A rule-based system, a model-based system or another system can be used to manually set or automatically set the priority level of the status zone. As an example, in the place where the grain size status zone overlaps with the EHP characteristic status zone, the grain size status zone can be assigned importance greater than the EHP characteristic status zone in the priority level so that the grain size status zone takes precedence.

[0132] Additionally, each state region may have a unique settings parser for a given WMA or set of WMAs.Settings parser identifier component 526 identifies the specific settings parser for each state region identified on the functional prediction graph being analyzed, and the specific settings parser for the selected WMA or set of WMAs.

[0133] Once a setting parser for a particular state zone is identified, the setting parser can be used to resolve conflicting target settings, where more than one target setting is identified based on the control zone. Different types of setting parsers can have different forms. For example, the setting parser identified for each state zone may include a manual selection parser, where conflicting target settings are presented to an operator or other user for resolution. In another example, the setting parser may include a neural network or other artificial intelligence or machine learning system. In such an instance, the setting parser can resolve conflicting target settings based on predicted or historical quality indicators corresponding to each of the different target settings. As an example, increasing the fan speed setting can improve grain cleanliness and increase grain loss. Reducing the cleaning fan speed setting can reduce grain cleanliness and reduce grain loss. When harvested grain loss or grain quality is selected as the quality indicator, given two conflicting fan speed setting values, the predicted or historical value for the selected quality indicator can be used to resolve the speed setting. In some cases, the setting parser can be a set of threshold rules that can be used instead of or in addition to the state zone. Examples of threshold rules can be represented as follows:

[0134] If the predicted grain quality value within 200 feet of the header of the agricultural harvester 100 is greater than x (where x is a selected value or a predetermined value), the target setting value selected based on grain loss is used instead of other conflicting target settings, otherwise the target setting value based on grain quality is used instead of other conflicting target settings.

[0135] The parser can be a logic component of the logic rules when the identification target is set. For example, the parser can be set to analyze the target setting, and simultaneously attempt to minimize the harvesting time, or minimize the total harvesting cost, or maximize the grain harvested, or other variables calculated based on different candidate target settings. When the amount of completing harvesting is reduced to a selected threshold or lower than a selected threshold, the harvesting time can be minimized. When the total harvesting cost is reduced to a selected threshold or lower than a selected threshold, the total harvesting cost can be minimized. When the amount of the grain harvested is increased to a selected threshold or higher than a selected threshold, the grain harvested can be maximized.

[0136] Figure 7 is a flow chart illustrating one example of the operation of the control zone generator 213 in generating control zones and status zones for a map received by the control zone generator 213 for zone processing (eg, for a map being analyzed).

[0137] At block 530, the control region 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 may be one of the functional prediction graphs 418, 420, or 422. Block 534 indicates that the graph being analyzed may also be another graph.

[0138] At block 536, WMA selector 486 selects the WMA or set of WMAs for which a control zone will be generated on the map being analyzed. At block 538, control zone criteria identification component 494 obtains control zone definition criteria for the selected WMA or set of WMAs. Block 540 indicates an example where the control zone criteria is or includes wear characteristics of the selected WMA or set of WMAs. Block 542 indicates an example where the control zone definition criteria is or includes values ​​and variations of input source data, such as values ​​or variations on the map being analyzed or values ​​or variations of inputs from various field sensors 208. Block 544 indicates an example where the control zone definition criteria is or includes physical machine characteristics, such as the machine's physical dimensions, the speed at which various subsystems operate, or other physical machine characteristics. Block 546 indicates an example where the control zone definition criteria is or includes the response of the selected WMA or set of WMAs upon reaching a new commanded setpoint. Box 548 indicates an example where the control zone definition criteria is or includes a machine performance indicator. Box 550 indicates an example where the control zone definition criteria is or includes an operator preference. Box 552 also indicates an example where the control zone definition criteria is or includes other items. Box 549 indicates an example where the control zone definition criteria is time-based, meaning that the agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has passed since the agricultural harvester 100 entered the particular control zone. In some cases, the selected amount of time can be a minimum amount of time. Thus, in some cases, the control zone definition criteria can prevent the agricultural harvester 100 from crossing the boundary of a control zone until at least a selected amount of time has passed. Box 551 indicates an example where the control zone definition criteria is based on a selected size value. For example, a control zone definition criteria based on a selected size value can exclude the definition of control zones that are smaller than a selected size. In some cases, the selected size can be a minimum size.

[0139] At block 554, the status zone criteria identification component 522 obtains the status zone definition criteria for the selected WMA or set of WMAs. Block 556 illustrates an example where the status zone definition criteria are based on manual input from the operator 260 or another user. Block 558 illustrates an example where the status zone definition criteria are based on crop type or crop variety. Block 560 illustrates an example where the status zone definition criteria are based on kernel size. Block 562 illustrates an example where the status zone definition criteria are based on or include crop status. Block 564 also illustrates an example where the status zone definition criteria are or include other criteria.

[0140] At block 566, the control zone boundary definition component 496 generates the boundaries of the control zone on the graph being analyzed based on the control zone criteria. The status zone boundary definition component 524 generates the boundaries of the status zone on the graph being analyzed based on the status zone criteria. Block 568 indicates an example of identifying zone boundaries for the control zone and the status zone. Block 570 shows that the target setting identifier component 498 identifies the target setting for each control zone. The control zone and status zone may also be generated in other ways, as indicated by block 572.

[0141] At block 574, the settings resolver identifier component 526 identifies a settings resolver for the selected WMA in each state zone defined by the 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 the predicted or historical quality of each conflicting goal setting 580, a rules-based resolver 582, a performance criteria-based resolver 584, or other resolver 586.

[0142] At box 588, WMA selector 486 determines whether there are more WMAs or sets of WMAs to be processed. If there are additional WMAs or sets of WMAs to be processed, processing returns to box 436, where the next WMA or set of WMAs to be defined for control and status zones is selected. When there are no additional WMAs or sets of WMAs for which control or status zones are to be generated, processing moves to box 590, where control zone generator 213 outputs a diagram with control zones, target settings, status zones, and settings parsers for each WMA or set of WMAs. As discussed above, the outputted diagram can be presented to operator 260 or another user; the outputted diagram can be provided to control system 214; or the outputted diagram can be output in other ways.

[0143] Figure 8An example of the operation of the control system 214 when controlling the agricultural harvester 100 based on the map output by the control zone generator 213 is shown. Thus, at box 592, the control system 214 receives a map of the work site. In some cases, the map can be a functional prediction map that can include control zones and status zones, as represented by box 594. In some cases, the received map can be a functional prediction map that excludes control zones and status zones. Box 596 indicates an example of a received map indicating the work site that can be an information map with control zones and status zones identified thereon. The information map can be a priori information map or a prediction map. Box 598 indicates an example of a received map that can include multiple different maps or multiple different layers. Box 610 indicates an example of a received map that can also take other forms.

[0144] At block 612, the control system 214 receives a sensor signal from the geographic location sensor 204. The sensor signal from the geographic location sensor 204 may include data indicating the geographic location 614 of the agricultural harvester 100, the speed 616 of the agricultural harvester 100, the heading 618 of the agricultural harvester 100, or other information 620. At block 622, the zone controller 247 selects a status zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geographic 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 in which one or more of the target settings for the selected WMA or set of WMAs is based on input from a control zone on the map of the worksite. Box 632 illustrates an example where one or more of the target settings are obtained from manual input from operator 260 or another user. Box 634 illustrates an example where the target settings are obtained from field sensors 208. Box 636 illustrates an example where one or more target settings are obtained from one or more sensors on other machines operating in the same field at the same time as agricultural harvester 100, or from one or more sensors on machines that have operated in the same field in the past. Box 638 also illustrates an example where the target settings are obtained from other sources.

[0145] At block 640, the zone controller 247 accesses a settings parser for the selected state zone and controls the settings parser to resolve the conflicting target settings into resolved target settings. As discussed above, in some cases, the settings parser can be a manual parser, 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 parser 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 parser can be based on predicted or historical quality indicators, threshold rules, or logic components. In any of these latter examples, the zone controller 247 executes the settings parser based on predicted or historical quality indicators, based on threshold rules, or by using logic components to obtain the resolved target settings.

[0146] At block 642, if the zone controller 247 has identified the resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214 to generate control signals based on the resolved target setting and apply the control signals to the selected WMA or set of WMAs. For example, if the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the setting controller 232 or the header / true controller 238 or both the setting 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 an additional WMA or set of additional WMAs is to be controlled at the current geographic location of the agricultural harvester 100 (as detected at block 612), processing 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 no additional WMAs or sets of WMAs to be controlled at the current geographic location of the agricultural harvester 100, processing proceeds to block 646, where the zone controller 247 determines whether additional control zones are believed to exist in the selected status zone. If additional control zones are believed to exist, processing returns to block 624, where the next control zone is selected. If no additional control zones are believed to exist, processing proceeds to block 648, where it is determined whether additional status zones are believed to exist. The zone controller 247 determines whether additional status zones are believed to exist. If additional status zones are believed to exist, processing returns to block 622, where the next status zone is selected.

[0147] 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—continue processing, as indicated by block 652. For example, as mentioned above, the control zone definition criteria may include criteria that define when a control zone boundary can be crossed by the agricultural harvester 100. For example, whether a 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 a selected amount of time has passed. In that case, at block 652, the zone controller 247 determines that the selected time period has passed. In addition, the zone controller 247 can continuously perform processing. Thus, the zone controller 247 does not have to 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 when to continue processing, and processing then continues at block 612 where the zone controller 247 again receives input from the geo-location sensor 204. It will also be understood that the zone controller 247 may control the WMAs and collections of WMAs simultaneously using a multi-input, multi-output regulator instead of sequentially controlling the WMAs and collections of WMAs.

[0148] Figure 9 6 is a block diagram illustrating an example of an operator interface controller 231. In the illustrated example, the operator interface controller 231 includes an operator input command processing system 654, an 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 objects 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 objects 682. The action signal generator 660 includes a visual control signal generator 684, an auditory control signal generator 686, a tactile control signal generator 688, and other objects 690. When describing the processing of various operator interface actions, Figure 9 Before describing the operation of the example operator interface controller 231 , some objects in the operator interface controller 231 and their related operations are first provided.

[0149] The operator input command processing system 654 detects operator inputs on the operator interface mechanism 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 mechanism 218 and processes those inputs for commands.

[0150] Other controller interaction system 656 handles interactions with other controllers in control system 214. Controller input processing system 668 detects and processes inputs from other controllers in control system 214, and controller output generator 670 generates outputs and provides those outputs to other controllers in control system 214. Speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides outputs indicating the meaning of the spoken inputs. For example, speech processing system 658 may recognize speech input from operator 260 when operator 260 is commanding control system 214 to change a setting for controllable subsystem 216. In such an example, speech processing system 658 recognizes the content of the spoken command, identifies the meaning of the command as a setting change command, and provides the meaning of the input back to speech processing system 662. Speech processing system 662, in turn, interacts with controller output generator 670 to provide command outputs to the appropriate controllers in control system 214 to implement the spoken setting change command.

[0151] The speech processing system 658 can be called in a variety of different ways. For example, in one example, the speech processing system 662 continuously provides input from a microphone (as one of the operator interface mechanisms 218) to the speech processing system 658. 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 indicating that the speech processing system 658 is called. In some cases, when 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 called using a wake-up word. That is, in some cases, the operation of the speech processing system 658 can be initiated based on the recognition of a selected spoken word (referred to as a wake-up word). In such an example, when the recognition component 674 recognizes the wake-up word, the recognition component 674 provides an indication that the wake-up word has been recognized by the trigger detector 672. Trigger detector 672 detects that speech processing system 658 has been invoked or triggered by a wake word. In another example, speech processing system 658 can be invoked by operator 260 activating an actuator on a user interface mechanism, such as by touching an actuator on a touch-sensitive display, by pressing a button, or by providing another triggering input. In such an example, trigger detector 672 can detect that speech processing system 658 has been invoked when the triggering input is detected by the user interface mechanism. Trigger detector 672 can also detect that speech processing system 658 has been invoked in other ways.

[0152] Once the speech processing system 658 is called, the speech input from the operator 260 is provided to the speech recognition component 674. The speech recognition component 674 identifies the language elements in the speech input, such as words, phrases, or other language units. The natural language understanding system 678 identifies the meaning of the recognized speech. The meaning can be any of a variety of other outputs such as natural language output, command output that identifies a command reflected in the recognized speech, value output that identifies a value in the recognized speech, or a variety of other outputs that reflect the 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.

[0153] In some examples, the speech processing system 658 can also generate output based on the speech input to navigate the operator 260 through the user experience. 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 specific values ​​required to execute 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 a speech synthesis that can be presented to the user through an auditory operator interface mechanism (such as a speaker). Thus, the conversation managed by the dialog management system 680 can be entirely spoken conversation, or a combination of visual and spoken conversation.

[0154] The action signal generator 660 generates action signals based on outputs 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 mechanism 218. The visual control signal generator 684 generates control signals to control visual objects in the operator interface mechanism 218. Visual objects may be lights, display screens, warning indicators, or other visual objects. The auditory control signal generator 686 generates outputs to control auditory elements of the operator interface mechanism 218. Auditory elements may include speakers, auditory alarms, horns, or other auditory elements. The tactile control signal generator 688 generates control signals as outputs to control tactile elements of the operator interface mechanism 218. Tactile elements may include vibration elements that can be used, for example, to vibrate the operator's seat, steering wheel, pedals, or joystick used by the operator. The tactile elements may include haptic or force feedback elements that provide tactile or force feedback to the operator via the operator interface mechanism. The tactile element may also include a variety of other tactile elements.

[0155] Figure 10 is a flow chart illustrating one example of the operation of the operator interface controller 231 in generating an operator interface display on the operator interface mechanism 218 (which may include a touch-sensitive display screen). Figure 10 Also shown is one example of how the operator interface controller 231 may detect and process operator interaction with the touch-sensitive display screen.

[0156] At block 692, the operator interface controller 231 receives a map. Block 694 indicates that the map is an example of a functional prediction map, and block 696 indicates that the map is an example of another type of map. At block 698, the operator interface controller 231 receives input from the geolocation sensor 204 identifying the geographic location of the agricultural harvester 100. As indicated in block 700, the input from the geolocation sensor 204 may include the heading and position of the agricultural harvester 100. Block 702 indicates an example of the input from the geolocation sensor 204 including the speed of the agricultural harvester 100, and block 704 indicates an example of the input from the geolocation sensor 204 including other objects.

[0157] 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 may include a current position marker showing the current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example of a displayed field including a next work unit marker identifying the next work unit (or area on the field) in which the agricultural harvester 100 will operate. Block 712 indicates an example of a displayed field including an upcoming area display portion showing an area to be processed by the agricultural harvester 100, and block 714 indicates an example of a displayed field including a previously visited display portion showing areas of the field that have already been processed by the agricultural harvester 100. Block 716 indicates an example of a displayed field showing various characteristics of the field with a geographically referenced location on the map. For example, if the received map is a map of grain size, the displayed field can show different categories of grain size present in the field, geo-referenced within the displayed field. The mapped characteristics can be shown in the previously visited area (as shown in box 714), the upcoming area (as shown in box 712), and the next operation unit (as shown in box 710). Box 718 also indicates that the displayed field includes other objects.

[0158] Figure 11 is an illustration showing one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be mounted in the operator cab of the agricultural harvester 100, or mounted on a mobile device, or mounted elsewhere. Figure 10 Prior to the flowchart shown in FIG. 7 , the user interface display 720 will be described.

[0159] exist Figure 11In the example shown in FIG, user interface display 720 shows that the touch-sensitive display includes display features for operating microphone 722 and speaker 724. Thus, the touch-sensitive display can be communicatively coupled to microphone 722 and speaker 724. Block 726 indicates that the touch-sensitive display can include a variety of user interface control actuators, such as buttons, keyboards, soft keyboards, links, icons, switches, etc. Operator 260 can activate the user interface control actuators to perform various functions.

[0160] exist Figure 11 In the example shown in , the user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which the agricultural harvester 100 is operating. The field display portion 728 is illustrated by a current position marker 708 that corresponds to the current position of the agricultural harvester 100 in the portion of the field shown in the field display portion 728. In one example, the operator can control the touch-sensitive display to zoom in on portions of the field display portion 728, or to pan or scroll the field display portion 728 to show different portions of the field. The next work unit 730 is shown as an area of ​​the field directly in front of the current position marker 708 of the agricultural harvester 100. The current position marker 708 can also be configured to identify the direction of travel of the agricultural harvester 100, the speed of travel of the agricultural harvester 100, or both the direction of travel and the speed of travel. In Figure 11 , the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 within the field, which can be used as an indication of the direction of travel of the agricultural harvester 100 .

[0161] The size of the next work unit 730 marked on the field display portion 728 can vary based on a variety of different criteria. For example, the size of the next work unit 730 can vary based on the speed at which the agricultural harvester 100 is traveling. Thus, when the agricultural harvester 100 is traveling faster, the area of ​​the next work unit 730 can be larger than the area of ​​the next work unit 730 if the agricultural harvester 100 is traveling slower. The field display portion 728 is also shown as displaying a previously visible area 714 and an upcoming area 712. The previously visible area 714 represents the area that has already been harvested, while the upcoming area 712 represents the area that still needs to be harvested. The field display portion 728 is also shown as displaying different characteristics of the field. Figure 11In the example shown in FIG, the graph being displayed is a grain size graph. Therefore, a plurality of different grain size markers are displayed on the field display portion 728. A set of grain size markers 732 are shown in the already visible area 714. There are also a set of grain size markers 732 shown in the upcoming area 712, and a set of grain size markers 732 are shown in the next operation unit 730. Figure 11 As shown, the kernel size display indicia 732 is composed of different symbols indicating areas of similar kernel size. In the example shown in FIG. 3 , the ! symbol indicates an area of ​​large kernel size; the * symbol indicates an area of ​​medium kernel size; and the # symbol indicates an area of ​​small kernel size. Thus, the field display portion 728 shows different measured or predicted kernel sizes for different areas located within the field. As previously described, the display indicia 732 can be composed of different symbols, and as described below, the symbols can be any display characteristics, such as different colors, shapes, patterns, intensities, text, icons, or other display characteristics. In some cases, each location of the field can have a display indicia associated with it. Thus, in some cases, a display indicia can be provided at each location of the field display portion 728 to identify the nature of the characteristic mapped for each particular location of the field. Thus, the present disclosure contemplates providing display indicia at one or more locations on the field display portion 728, such as the loss level display indicia 732 (e.g., at the Figure 11 ) to identify the nature, extent, etc. of the characteristic being displayed, thereby identifying the characteristic at the corresponding position in the field being displayed.

[0162] exist Figure 11 In the example shown, the user interface display 720 also has a control display portion 738. The control display portion 738 allows an operator to view information and interact with the user interface display 720 in various ways.

[0163] The actuators and display indicia in portion 738 may be displayed as, for example, individual items, a fixed list, a scrollable list, a drop-down menu, or a drop-down list. Figure 11 In the example shown in FIG, display portion 738 displays information for three different seed sizes corresponding to the three symbols mentioned above. Display portion 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touching. For example, operator 260 can touch a touch-sensitive actuator with a finger to activate the corresponding touch-sensitive actuator.

[0164] Flag bar 739 shows flags that have been set automatically or manually. Flag actuator 740 allows operator 260 to mark the current location and then add information indicating the seed size found at the current location. For example, when operator 260 activates flag actuator 740 by touching flag actuator 740, touch gesture processing system 664 in operator interface controller 231 identifies the current location as a location where agricultural harvester 100 encounters large seed sizes. When operator 260 touches button 742, touch gesture processing system 664 identifies the current location as a location where agricultural harvester 100 encounters medium seed sizes. When operator 260 touches button 744, touch gesture processing system 664 identifies the current location as a location where agricultural harvester 100 encounters small seed sizes. The touch gesture processing system 664 also controls the visual control signal generator 684 to mark a point, start, end, or continuation of a position corresponding to a kernel size identified on the field display portion 728 when the button 740, 742, or 744 is activated. For example, the visual control signal generator may add a symbol corresponding to a kernel size identified on the field display portion 728 when the button 740, 742, or 744 is activated.

[0165] Column 746 displays a symbol corresponding to each category of kernel size tracked on field display portion 728. Indicator column 748 displays an indicator (which may be a text indicator or other indicator) identifying the kernel size for that category. Without limitation, the kernel size symbols in column 746 and the indicators in column 748 may include any display indicia feature, such as different colors, shapes, patterns, intensities, text, graphics, or other display indicia features. The value displayed in column 750 may be a predicted kernel size or a kernel size measured by field sensor 208. In one example, operator 260 may select a specific portion of field display portion 728 for displaying the value in column 750. Thus, the value in column 750 may correspond to a value in display portion 712, 714, or 730. Column 752 displays an action threshold. The action threshold in column 752 may be a threshold corresponding to the measured value in column 750. If the measured value in column 750 meets the corresponding action threshold in column 752, control system 214 takes the action identified in column 754. In some cases, the measurement value may satisfy a corresponding action threshold by meeting or exceeding the corresponding action threshold. In one example, operator 260 may select a threshold by touching the threshold in field 752, for example, to change the threshold. Once selected, operator 260 may change the threshold. The thresholds in field 752 may be configured such that a specified action is performed when measurement value 750 exceeds the threshold, is equal to the threshold, or is less than the threshold.

[0166] Similarly, operator 260 can touch the action indicator in column 754 to change the action to be taken. When the threshold is met, multiple actions can be taken. For example, at the bottom of column 754, reducing the fan speed by 50 revolutions per minute (RPM) and reducing the screen opening by 1 millimeter (mm) are identified as actions to be taken if the measurement in column 750 meets the threshold in column 752.

[0167] The actions that can be set in column 754 can be any of a variety of different types of actions. For example, the action can include a keep out action that, when performed, prevents the agricultural harvester 100 from further harvesting in the area. The action can include a speed change action that, when performed, changes the speed at which the agricultural harvester 100 travels through the field or the speed of the grain cleaning fan 120. The action can include a setting change action for changing the setting of an internal actuator or another WMA or collection of WMAs (such as an actuator that changes a screen setting, a chaff screen setting, a concave plate gap, or a rotor speed). The action can also include a setting change action for implementing a change action that changes the reel position or the setting of another WMA. These are examples only, and a variety of other actions are contemplated herein.

[0168] The display indicia shown on the user interface display 720 can be visually controlled. Visually controlling the interface display 720 can be performed to attract the attention of the operator 260. For example, the display indicia can be controlled to modify the intensity, color, or pattern displayed by the display indicia. In addition, the display indicia can be controlled to flash. The described changes to the visual appearance of the display indicia are provided as examples. Accordingly, other aspects of the visual appearance of the display indicia can be changed. Thus, the display indicia can be modified in a desired manner in various circumstances, for example, to attract the attention of the operator 260.

[0169] Now return to Figure 10The flowchart continues with a description of the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects an input for setting a flag and controls the touch-sensitive user interface display 720 to display the flag on the 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, the operator interface controller 231 detects a field sensor input indicating a characteristic of the field measured by one of the field sensors 208. At block 768, the visual control signal generator 684 generates a control signal to control the user interface display 720, thereby displaying actuators for modifying the user interface display 720 and for modifying machine controls. For example, block 770 indicates that one or more of the actuators for setting or modifying the values ​​in fields 739, 746, and 748 can be displayed. Thus, a user can set flags and modify the characteristics of those flags. For example, a user can modify the kernel size and biomass level indicators corresponding to the flags. Box 772 shows the display of the action threshold in column 752. Box 776 shows the display of the action in column 754, and box 778 shows the display of the measured field data in column 750. Box 780 indicates that various other information and actuators may also be displayed on the user interface display 720.

[0170] At block 782, the operator input command processing system 654 detects and processes operator input corresponding to an interaction performed by the operator 260 with the user interface display 720. In the event that the user interface mechanism on which the user interface display 720 is displayed is a touch-sensitive display screen, the interaction input performed by the operator 260 with the touch-sensitive display screen may be a touch gesture 784. In some cases, the operator interaction input may be an input using a pointing device 786 or other operator interaction input 788.

[0171] At block 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, block 792 indicates that a signal may be received by the controller input processing system 668 indicating that the detected value in column 750 meets the threshold condition presented in column 752. As previously explained, a threshold condition may include a value below a threshold, at a threshold, or above a threshold. Block 794 shows that the action signal generator 660 may, in response to receiving the alarm condition, alert the operator 260 by generating a visual alarm using the visual control signal generator 684, an audible alarm using the audible control signal generator 686, a tactile alarm using the tactile control signal generator 688, or any combination thereof. Similarly, as indicated by block 796, the controller output generator 670 may generate outputs to other controllers in the control system 214, causing those controllers to perform the corresponding actions identified in column 754. Block 798 shows that the operator interface controller 231 may also detect and process the alarm condition in other ways.

[0172] Block 900 shows that the speech processing system 662 can detect and process input that invokes the speech processing system 658. Block 902 shows that performing speech processing can include using the dialog management system 680 to conduct a dialog with the operator 260. Block 904 shows that speech processing can include providing a signal to the controller output generator 670 to automatically perform a control operation based on the speech input.

[0173] Table 1 below shows an example of a conversation between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 uses a trigger word or wake-up word detected by the trigger detector 672 to invoke the speech processing system 658. In the example shown in Table 1, the wake-up word is "Johnny."

[0174] Table 1

[0175] Operator: "Johnny, tell me the kernel size."

[0176] Operator Interface Controller: "Current kernel size is Large."

[0177] Table 2 shows an example of the speech synthesis component 676 providing an 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 based on coverage area or distance (such as every five acres), or based on exceptions (such as when a measurement is greater than a threshold).

[0178] Table 2

[0179] Operator Interface Controller: "Over the past 10 minutes, kernel size was 5% large, 80% medium, and 15% small."

[0180] Operator Interface Controller: "Predict the kernel size distribution for the next acre to be: 10% large, 80% medium, 10% small."

[0181] 3 shows that some actuators or user input mechanisms on the touch-sensitive display 720 can complement voice dialogue. The example in Table 3 shows that the action signal generator 660 can generate an action signal to automatically mark large kernel size plots in a field being harvested.

[0182] Table 3

[0183] Person: "Johnny, mark the large seed-sized plots."

[0184] Operator Interface Controller: "Large kernel size plots have been marked."

[0185] The example shown in Table 4 shows that the action signal generator 660 can engage in a dialog with the operator 260 to begin and end marking large kernel size plots.

[0186] Table 4

[0187] Person: "Johnny, start marking the large kernel-sized plots."

[0188] Operator Interface Controller: "Mark large kernel size plots."

[0189] Person: "Johnny, stop marking large kernel-sized plots."

[0190] Operator Interface Controller: "Large kernel size plot marking has stopped."

[0191] The example shown in Table 5 shows that the action signal generator 160 can generate signals to mark small kernel size plots in a different manner than shown in Tables 3 and 4.

[0192] Table 5

[0193] Person: "Johnny, mark the next 100 feet as small seed-sized plots."

[0194] Operator Interface Controller: "The next 100 feet have been marked as small kernel size plots."

[0195] Return to Figure 10As shown in block 906, the operator interface controller 231 may also detect and process conditions for outputting messages and other information in other ways. For example, the other controller interaction system 656 may detect input from another controller indicating that an alarm or output message should be presented to the operator 260. Block 908 shows that the output may be an auditory message. Block 910 shows that the output may be a visual message, and block 912 shows that the output may 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.

[0196] Once the operation is complete, any desired values ​​displayed or displayed on the user interface display 720 can be saved. Those values ​​can also be used for machine learning to improve various parts of the prediction model generator 210, the prediction map generator 212, the control zone generator 213, the control algorithm, or other objects. Saving the desired values ​​is indicated by box 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.

[0197] Thus, it can be seen that an information map is obtained by an agricultural harvester and shows values ​​at different geographic locations in a field being harvested. Field sensors on the harvester sense characteristics as the agricultural harvester moves through the field. A prediction map generator generates a prediction map based on the values ​​in the information map and the characteristics sensed by the field sensors, which predicts control values ​​at different locations in the field. The control system controls the controllable subsystems based on the values ​​in the prediction map.

[0198] A control value is a value on which an action can be based. As described herein, a control value can include any value (or a characteristic indicated by or derived from the value) that can be used when controlling the agricultural harvester 100. A control value can be any value that indicates an agricultural characteristic. 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 priori information graph, or a value provided by a prediction graph (such as a functional prediction graph). A control value can also include any of the characteristics 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 entered by the operator of the agricultural machine.

[0199] This discussion has referred to processors and servers. In some examples, processors and servers include computer processors with associated memory and timing circuits (not shown separately). Processors and servers are functional components of the systems or devices to which they belong and are activated by and facilitate the functions of other components or objects in those systems.

[0200] Furthermore, many user interface displays have been discussed. The displays can take many different forms and can have a 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, and the like. The user-activatable operator interface mechanisms can also be actuated in a variety of different ways. For example, the user-activatable operator interface mechanism can be actuated using an operator interface mechanism such as a pointing device (such as a trackball or mouse, hardware buttons, switches, joysticks, or keyboards, thumb switches or thumb pads, etc.), a virtual keyboard, or other virtual actuators. In addition, if the screen displaying the user-activatable operator interface mechanism is a touch-sensitive screen, the user-activatable operator interface mechanism can be activated using touch gestures. Furthermore, the user-activatable operator interface mechanism can be activated using voice commands utilizing voice recognition functionality. Voice recognition can be implemented using a voice detection device, such as a microphone, and software for recognizing the detected voice and executing commands based on the received voice.

[0201] A number of data stores have also been discussed. It will be noted 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 remote from the system utilizing the data store, or one or more data stores can be local while others are remote. The present disclosure contemplates all of these configurations.

[0202] Likewise, the accompanying drawings illustrate many blocks, with functionality assigned to each block. It will be noted that fewer blocks can be used to illustrate that the functionality attributed to a plurality of different blocks is performed by fewer components. More blocks can also be used to illustrate that the functionality can be distributed among more components. In various examples, some functionality can be added, and some can be removed.

[0203] It will be noted 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 objects that perform the functions associated with those systems, components, logic, or interactions, such as processors, memory, or other processing components, including but not limited to artificial intelligence components such as neural networks, some of which are described below. In addition, one or all of the systems, components, logic, and interactions can be implemented by software loaded into memory and then executed by a processor or server or other computing component, as described below. One or all of the systems, components, logic, and interactions can be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement one or all of the systems, components, logic, and interactions described above. Other structures can also be used.

[0204] Figure 12 is a block diagram of an agricultural harvester 600, which may be similar to Figure 2 1. The agricultural harvester 100 shown in FIG. The agricultural harvester 600 communicates with the 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 the end user 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 applications over a wide area network and can be accessed through a web browser or any other computing component. Figure 2 The software or components shown in and the data associated therewith 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 if the service appears as a single access point to the user. Thus, the components and functions described herein can be provided from a remote server located at a remote location using a remote server architecture. Alternatively, the components and functions can be provided from a server, or the components and functions can be installed on a client device directly or otherwise.

[0205] exist Figure 12 In the example shown in , some objects are similar to Figure 2 and those items are similarly numbered. Figure 12 Specifically, it is 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. Figure 12 In the example shown in , an agricultural harvester 600 accesses the system through a remote server location 502 .

[0206] Figure 12 Another example of a remote server architecture is also depicted. Figure 12 It shows that Figure 2 Some elements of can be set at remote server location 502, and other elements can be located elsewhere. By way of example, data storage 202 can be set at a location separated from location 502 and accessed via a remote server at location 502. No matter where the element is located, the element can be directly accessed by the agricultural harvester 600 through a network (such as a wide area network or a local area network); the element can be hosted by a server at a remote site; or the element can be provided as a server, or accessed by a connection server located at a remote location. In addition, data can be stored at any location, and the stored data can be accessed by an operator, user or system or forwarded to an operator, user or system. For example, a physical carrier can be used instead of an electromagnetic wave carrier or in addition to an electromagnetic wave carrier. In some examples, when wireless telecommunication service coverage crosses or does not exist, another machine (such as a refueling truck or other mobile machine or vehicle) can have an automated, semi-automated or manual information collection system. When the combine harvester 600 approaches a machine containing an information collection system (such as a fuel truck before refueling), the information collection system uses any type of ad-hoc wireless connection to collect information from the combine harvester 600. Then, when the machine containing the received information arrives at a location where wireless telecommunications coverage or other wireless coverage is available, the collected information can be forwarded to another network. For example, a fuel truck can enter an area with wireless communication coverage when driving to a location where other machines are refueled or when in a primary fuel storage location. All of these architectures are contemplated herein. In addition, information can be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can send information to another network.

[0207] It will also be noted that Figure 2 The components or parts thereof may be provided on a variety of different devices. One or more of these devices may include an onboard computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile devices, such as a palmtop computer, a mobile phone, a smart phone, a multimedia player, a personal digital assistant, etc.

[0208] In some examples, remote server architecture 500 may include network security measures. Without limitation, these measures may include data encryption on storage devices, encryption of data sent between network nodes, authentication of persons or processes accessing data, and the use of a ledger to record metadata, data, data transmission, data access, and data conversion. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0209] Figure 13 1 is 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, the mobile device can be deployed in the cockpit of an agricultural harvester 100 for generating, processing, or displaying the diagrams discussed above. Figures 14 and 15 is an example of a handheld or mobile device.

[0210] Figure 13 Provides a general block diagram of the components of client device 16, which can operate Figure 2 Some of the components shown in Figure 2 , or both. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices and, in some examples, automatically provides a channel or passage for receiving information, such as by scanning. Examples of communication link 13 include protocols that allow communication via one or more communication protocols, such as cellular wireless service for providing access to a network, and protocols that provide local wireless connectivity to a network.

[0211] In other examples, the application may be received on a removable secure digital (SD) card connected to the interface 15. The interface 15 and the communication link 13 communicate with a processor 17 (which may also be embodied as a processor or server according to other figures) along a bus 19, which 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.

[0212] In one example, an I / O component 23 is provided to facilitate input and output operations. The I / O components 23 for various examples of device 16 may 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 may also be used.

[0213] The clock 25 illustratively includes a real-time clock component that outputs time and date. The clock 25 can also illustratively provide a timing function to the processor 17.

[0214] Positioning system 27 illustratively includes components for outputting the current geographic location of device 16. For example, this may include a Global Positioning System (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning systems. Positioning system 27 may also include, for example, mapping software or navigation software for generating desired maps, navigation routes, and other geographic functions.

[0215] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. Memory 21 may include all types of tangible, volatile, and non-volatile computer-readable memory devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions in accordance with the instructions. Processor 17 may also be activated by other components to facilitate its functions.

[0216] Figure 14 An example is shown where the device 16 is a tablet computer 600. Figure 14 In FIG, computer 601 is shown having 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 via a suitable attachment mechanism, such as a wireless link or a USB port, for example. Computer 601 can also illustratively receive voice input.

[0217] Figure 15 Similar to Figure 14 , except that the device is a smartphone 71. Smartphone 71 has a touch-sensitive display 73 that displays icons or tiles or other user input mechanisms 75. The user can use mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Typically, smartphone 71 is built on a mobile operating system and provides more advanced computing power and connectivity than feature phones.

[0218] Note that other forms of device 16 are possible.

[0219] Figure 16 It can be deployed Figure 2 An example of a computing environment with elements of Figure 16, an example system for implementing some embodiments includes a computing device in the form of a computer 810 that is programmed to operate as discussed above. Components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include a processor or server in accordance with 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 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Figure 2 The memory and program described can be deployed in Figure 16 in the corresponding part of .

[0220] 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 volatile and non-volatile media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer storage media and communication media. Computer storage media is distinct from and does not include modulated data signals or carrier waves, and does not include modulated data signals or carrier waves. Computer-readable media includes hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing 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, cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by computer 810. Communication media can contain computer-readable instructions, data structures, program modules, or other data in a transmission mechanism, and includes any information transfer media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a way as to encode information in the signal.

[0221] The system memory 830 includes computer storage media in the form of volatile and / or nonvolatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS), containing the basic routines that help to transfer information between elements within the computer 810, such as during startup, is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are immediately accessible to and / or currently being operated on by or by the processing unit 820. By way of example, and not limitation, Figure 16 Operating system 834 , application programs 835 , other program modules 836 , and program data 837 are shown.

[0222] The computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. By way of example only, Figure 16 Shown are a hard disk drive 841 that reads from or writes to non-removable non-volatile media, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 through a non-removable disk storage interface, such as interface 840, and the optical disk drive 855 is typically connected to the system bus 821 through a removable storage interface, such as interface 850.

[0223] Alternatively, or in addition, the functions described herein may be at least partially performed 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), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0224] Discussed above and Figure 16 The drives and their associated computer storage media shown in FIG. 8 provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. Figure 16 844, application programs 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0225] A user can enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device or pointing device 861, such as a mouse, trackball, or touch pad. Other input devices (not shown) may include a joystick, a game pad, a satellite dish, a scanner, and the like. These and other input devices are typically connected to the processing unit 820 through a user input interface 860 (coupled to the system bus), but may be connected through other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface, such as a video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printer 896, which may be connected via an output peripheral interface 895.

[0226] The computer 810 operates in a network environment using logical connections, such as a controller area network (CAN), a local area network (LAN), or a wide area network (WAN), to one or more remote computers, such as remote computer 880 .

[0227] 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 network environment, program modules may be stored in the remote memory storage device. For example, Figure 16 Remote application programs 885 are shown as being resident on remote computer 880 .

[0228] It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of these scenarios are contemplated herein.

[0229] Example 1 is an agricultural machine comprising:

[0230] crop handling systems;

[0231] a communication system that receives an information map including values ​​of a first agricultural characteristic corresponding to different geographic locations in a field;

[0232] a geographic location sensor, the geographic location sensor detecting a geographic location of the agricultural machine;

[0233] a field sensor that detects a value of a second agricultural characteristic corresponding to the geographic location, the second agricultural characteristic being indicative of a characteristic of harvested material;

[0234] a prediction map generator that generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map, the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to different geographical locations in the field; and

[0235] A control system controls the crop treatment system based on the predicted values ​​of the second agricultural characteristic at different locations in the field and based on the detected geographic location.

[0236] Example 2 is an agricultural work machine according to any or all of the preceding examples, further comprising:

[0237] A prediction model generator, which generates a prediction agricultural model based on the value of the first agricultural characteristic at the geographical location in the information map and the value of the second agricultural characteristic at the geographical location sensed by the field sensor, the prediction agricultural model models the relationship between the first agricultural characteristic and the second agricultural characteristic, wherein the prediction map generator generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map and based on the prediction agricultural model, the functional prediction agricultural map maps the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0238] Example 3 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material comprises grain, and wherein the field sensor comprises:

[0239] A kernel size sensor senses kernel size characteristics of kernels in the agricultural work machine.

[0240] Example 4 is an agricultural work machine according to any or all of the preceding examples, wherein the harvested material includes at least one of ears, heads, and fruit pods (EHPs), and wherein the field sensor comprises:

[0241] An EHP characteristic sensor senses an EHP characteristic indicative of a characteristic of one or more of an ear, an ear head, and a pod in the agricultural work machine.

[0242] Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material includes grain, and wherein the prediction map generator comprises:

[0243] A grain size map generator is provided for generating a predicted grain size map as the functional prediction agricultural map based on the value of the first agricultural characteristic in the information map and based on the prediction model; the predicted grain size map maps the predicted value of the grain size as the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0244] Example 6 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material includes at least one of ears, heads, and fruit pods (EHPs), and wherein the prediction map generator comprises:

[0245] An EHP characteristic map generator, which generates a predicted EHP map as the functional predicted agricultural map based on the value of the first agricultural characteristic in the information map and based on the prediction model; the predicted EHP map maps the predicted values ​​of one or more EHP characteristics as the predicted values ​​of the second agricultural characteristic to different geographical locations in the field.

[0246] Example 7 is an agricultural work machine according to any or all of the preceding examples, wherein the communication system receives a first information map and a second information map as the information map, the first information map including values ​​of the first agricultural characteristic, the second information map including values ​​of a third agricultural characteristic corresponding to different locations in the field,

[0247] wherein the prediction model generator generates the prediction agricultural model based on the value of the second agricultural characteristic at the geographical location sensed by the field sensor and based on the value of the first agricultural characteristic at the geographical location in the first information map and the value of the third agricultural characteristic at the geographical location in the second information map, so as to model the relationship between the combination of the first agricultural characteristic and the third agricultural characteristic and the second agricultural characteristic, and

[0248] Wherein, the prediction graph generator includes:

[0249] A combination map generator that generates a prediction combination map as the functional prediction map based on the prediction agricultural model, based on the value of the first agricultural characteristic in the first information map, and based on the value of the third agricultural characteristic in the second information map, the prediction combination map mapping the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0250] Example 8 is an agricultural working machine according to any or all of the preceding examples, wherein the communication system receives a seed genotype map as a prior information map, the seed genotype map including the seed genotype as the first agricultural characteristic, and wherein the predictive model generator generates the predictive agricultural model to model the relationship between the seed genotype and the second agricultural characteristic.

[0251] Example 9 is an agricultural working machine according to any or all of the preceding examples, wherein the communication system receives a vegetation index map as the information map, the vegetation index map including a vegetation index characteristic as the first agricultural characteristic, and wherein the predictive model generator generates the predictive agricultural model to model the relationship between the vegetation index characteristic and the second agricultural characteristic.

[0252] Example 10 is an agricultural working machine according to any or all of the preceding examples, wherein the communication system receives a yield map as the information map, the yield map including a predicted yield characteristic as the first agricultural characteristic, and wherein the predictive model generator generates a predictive agricultural model to model the relationship between the predicted yield characteristic and the second agricultural characteristic.

[0253] Example 11 is an agricultural working machine according to any or all of the preceding examples, wherein the communication system receives a biomass map as the information map, the biomass map including a biomass characteristic as the first agricultural characteristic, and wherein the predictive model generator generates the predictive agricultural model to model the relationship between the biomass characteristic and the second agricultural characteristic.

[0254] Example 12 is a computer-implemented method of controlling an agricultural machine including a crop processing system, comprising:

[0255] receiving, at the agricultural work machine, an information map indicating values ​​of a first agricultural characteristic corresponding to different geographical locations in a field;

[0256] detecting a geographic location of the agricultural machine;

[0257] detecting a second agricultural characteristic with an in-situ sensor, the second agricultural characteristic indicative of a characteristic of harvested material corresponding to the geographic location;

[0258] controlling a prediction map generator to generate a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map, the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to different locations in the field; and

[0259] The crop treatment system is controlled based on the predicted values ​​of the second agricultural characteristic at different locations in the field and based on the detected geographic location.

[0260] Example 13 is a computer-implemented method according to any or all of the preceding examples, further comprising:

[0261] A predictive agricultural model is generated based on the value of the first agricultural characteristic at the geographical location in the information map and the value of the second agricultural characteristic at the geographical location sensed by the field sensor, the predictive agricultural model models the relationship between the first agricultural characteristic and the second agricultural characteristic, wherein the predictive map generator is controlled to generate a functional predictive agricultural map of the field based on the value of the first agricultural characteristic in the information map and based on the predictive agricultural model, the functional predictive agricultural map maps the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0262] Example 14 is a computer-implemented method according to any or all of the preceding examples, wherein the harvested material includes grain, and wherein detecting a second agricultural characteristic includes:

[0263] A kernel size characteristic of kernels in the agricultural machine is detected.

[0264] Example 15 is a computer-implemented method according to any or all of the preceding examples, wherein the harvested material comprises at least one of an ear, a head, and a fruit pod (EHP), and wherein detecting the second agricultural characteristic comprises:

[0265] An EHP characteristic is detected, the EHP characteristic being indicative of a characteristic of one or more of an ear, an ear head, and a pod in the agricultural work machine.

[0266] Example 16 is a computer-implemented method according to any or all of the preceding examples, wherein the harvested material includes grain, and wherein controlling the prediction map generator comprises:

[0267] and controlling a grain size map generator to generate a predicted grain size map as the functional predicted agricultural map based on the value of the first agricultural characteristic in the information map and based on the prediction model, wherein the predicted grain size map maps the predicted value of the size of the grain as the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0268] Example 17 is a computer-implemented method according to any or all of the preceding examples, wherein the harvested material comprises at least one of ears, heads, and fruit pods (EHPs), and wherein controlling the prediction map generator comprises:

[0269] Controlling an EHP characteristic map generator, the EHP characteristic map generator generates a predicted EHP map as the functional predicted agricultural map based on the value of the first agricultural characteristic in the prior information map and based on the prediction model, the predicted EHP map mapping the predicted values ​​of one or more EHP characteristics as predicted values ​​of the second agricultural characteristic to different geographical locations in the field.

[0270] Example 18 is a computer-implemented method according to any or all of the preceding examples, wherein receiving an information map comprises: receiving a first information map and a second information map as the information maps, the first information map comprising values ​​of the first agricultural characteristic and the second information map comprising values ​​of a third agricultural characteristic corresponding to different locations in the field; and wherein generating a predictive agricultural model comprises: generating the predictive agricultural model based on the value of the second agricultural characteristic at the geographic location detected by the field sensor and based on the value of the first agricultural characteristic at the geographic location in the first information map and the value of the third agricultural characteristic at the geographic location in the second information map to model a relationship between a combination of the first and third agricultural characteristics and the second agricultural characteristic; and wherein controlling a predictive map generator comprises:

[0271] A prediction combination map is generated as the functional prediction map based on the prediction agricultural model, based on the value of the first agricultural characteristic in the first information map, and based on the value of the third agricultural characteristic in the second information map, wherein the prediction combination map maps the predicted value of the second agricultural characteristic to different geographical locations in the field.

[0272] Example 19 is a computer-implemented method according to any or all of the preceding examples, wherein the communication system receives one or more of a seed genotype map, a vegetation index map, a yield map, and a biomass map as the information map, the seed genotype map including the seed genotype as the first agricultural characteristic, the vegetation index map including the vegetation index characteristic as the first agricultural characteristic, the yield map including the predicted yield characteristic as the first agricultural characteristic, and the biomass map including the biomass characteristic as the first agricultural characteristic.

[0273] Example 20 is an agricultural working machine comprising:

[0274] a communication system that receives an information map including values ​​of a first agricultural characteristic corresponding to different geographic locations in a field;

[0275] a geographic location sensor, the geographic location sensor detecting a geographic location of the agricultural machine;

[0276] a field sensor that detects a value of a second agricultural characteristic corresponding to the geographic location, the second agricultural characteristic indicating one or more of a kernel size of kernels in the agricultural work machine, a characteristic of an ear in the agricultural work machine, a characteristic of an ear head in the agricultural work machine, and a characteristic of a pod in the agricultural work machine;

[0277] a prediction map generator that generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map, the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to different geographical locations in the field; and

[0278] A control system generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural work machine.

[0279] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features and / or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.

Claims

1. An agricultural machine (100), comprising: crop handling systems; a communication system (206) that receives an information map (258) including values ​​of a first agricultural characteristic corresponding to different geographic locations in a field; A geographic location sensor (204), the geographic location sensor (204) detecting the geographic location of the agricultural machine (100); a field sensor (208) that detects a value of a second agricultural characteristic corresponding to the geographic location, the second agricultural characteristic being indicative of a characteristic of harvested material; a prediction map generator (212) configured to generate a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map (258), the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to different geographical locations in the field; as well as A control system (214) controls the crop treatment system based on the predicted values ​​of the second agricultural characteristic at different locations in the field and based on the detected geographic location.

2. The agricultural work machine according to claim 1, further comprising: A prediction model generator, which generates a prediction agricultural model based on the value of the first agricultural characteristic at the geographical location in the information map and the value of the second agricultural characteristic at the geographical location sensed by the field sensor, the prediction agricultural model models the relationship between the first agricultural characteristic and the second agricultural characteristic, wherein the prediction map generator generates a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map and based on the prediction agricultural model, the functional prediction agricultural map maps the predicted value of the second agricultural characteristic to different geographical locations in the field.

3. The agricultural machine according to claim 1, wherein: The harvested material includes grain, and wherein the field sensor comprises: A kernel size sensor senses kernel size characteristics of kernels in the agricultural work machine.

4. The agricultural machine according to claim 1, wherein: The harvested material comprises at least one of ears, heads, and fruit pods (EHPs), and wherein the field sensor comprises: An EHP characteristic sensor senses an EHP characteristic indicative of a characteristic of one or more of an ear, an ear head, and a pod in the agricultural work machine.

5. The agricultural machine according to claim 2, wherein: The harvested material includes grain, and wherein the prediction map generator includes: A grain size map generator is provided for generating a predicted grain size map as the functional prediction agricultural map based on the value of the first agricultural characteristic in the information map and based on the prediction model; the predicted grain size map maps the predicted value of the grain size as the predicted value of the second agricultural characteristic to different geographical locations in the field.

6. The agricultural machine according to claim 2, wherein: The harvested material includes at least one of ears, heads, and fruit pods (EHPs), and wherein the prediction map generator comprises: An EHP characteristic map generator, which generates a predicted EHP map as the functional predicted agricultural map based on the value of the first agricultural characteristic in the information map and based on the prediction model; the predicted EHP map maps the predicted values ​​of one or more EHP characteristics as the predicted values ​​of the second agricultural characteristic to different geographical locations in the field.

7. The agricultural machine according to claim 2, wherein: The communication system receives a first information map and a second information map as the information map, the first information map including values ​​of the first agricultural characteristic and the second information map including values ​​of a third agricultural characteristic corresponding to different locations in the field, wherein the prediction model generator generates the prediction agricultural model based on the value of the second agricultural characteristic at the geographical location sensed by the field sensor and based on the value of the first agricultural characteristic at the geographical location in the first information map and the value of the third agricultural characteristic at the geographical location in the second information map, so as to model the relationship between the combination of the first agricultural characteristic and the third agricultural characteristic and the second agricultural characteristic, and Wherein, the prediction graph generator includes: A combination map generator, which generates a prediction combination map as the functional prediction agricultural map based on the prediction agricultural model, based on the value of the first agricultural characteristic in the first information map, and based on the value of the third agricultural characteristic in the second information map, the prediction combination map mapping the predicted value of the second agricultural characteristic to different geographical locations in the field.

8. The agricultural machine according to claim 2, wherein: The communication system receives a seed genotype map as a priori information map, the seed genotype map including the seed genotype as the first agricultural characteristic, wherein the predictive model generator generates the predictive agricultural model to model the relationship between the seed genotype and the second agricultural characteristic.

9. A computer-implemented method of controlling an agricultural machine (100) including a crop processing system, comprising: receiving, at the agricultural work machine (100), an information map (258) indicating values ​​of a first agricultural characteristic corresponding to different geographical locations in a field; Detecting the geographical location of the agricultural machine (100); detecting a second agricultural characteristic with a field sensor (208), the second agricultural characteristic indicative of a characteristic of harvested material corresponding to the geographic location; controlling a prediction map generator (212) to generate a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map (258), the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to different locations in the field; as well as The crop treatment system is controlled based on the predicted values ​​of the second agricultural characteristic at different locations in the field and based on the detected geographic location.

10. An agricultural machine (100), comprising: a communication system (306) that receives an information map (258) including values ​​of a first agricultural characteristic corresponding to different geographic locations in a field; A geographic location sensor (204) for detecting a geographic location of the agricultural machine; a field sensor (208) that detects a value of a second agricultural characteristic corresponding to the geographic location, the second agricultural characteristic indicating one or more of a kernel size of kernels in the agricultural work machine, a characteristic of an ear in the agricultural work machine (100), a characteristic of an ear head in the agricultural work machine (100), and a characteristic of a pod in the agricultural work machine (100); a prediction map generator (212) configured to generate a functional prediction agricultural map of the field based on the value of the first agricultural characteristic in the information map (258), the functional prediction agricultural map mapping the predicted value of the second agricultural characteristic to different geographical locations in the field; as well as A control system (214) generates control signals based on the functional predictive agricultural map to control controllable subsystems (216) on the agricultural work machine (100).

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