Predictive speed map generation and control system

Abstract

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

Description

Technical Field

[0001] This manual covers agricultural machinery, forestry machinery, construction machinery, and turf management machinery. Background Technology

[0002] There are various types of agricultural machinery. Some agricultural machinery includes harvesters, such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, and reapers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] Various conditions in the field can have many detrimental effects on harvesting operations. Therefore, when such conditions are encountered during harvesting, the operator may attempt to modify the controls of the harvester.

[0004] The above discussion is provided for general background information only and is not intended to help determine the scope of the subject matter for which protection is sought. Summary of the Invention

[0005] One or more information maps are obtained through agricultural machinery. These maps map one or more agricultural characteristic values ​​to different geographical locations within the field. Field sensors on the agricultural machinery sense the agricultural characteristics as the machinery moves through the field. A prediction map generator produces prediction maps that predict agricultural characteristics at different locations within the field based on the relationships between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. These prediction maps can be output and used for automated machine control.

[0006] This summary is provided to present, in a simplified form, the selection of concepts that will be further described in the following detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. The claimed subject matter is not limited to examples addressing any or all of the shortcomings pointed out in the background art. Attached Figure Description

[0007] Figure 1 This is a partially drawn, partially schematic illustration of an example of a combine harvester.

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

[0009] Figures 3A to 3B (Hereinafter referred to collectively as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester when generating a graph.

[0010] Figure 4 This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0011] Figure 5 This is a flowchart illustrating an example of the operation of an agricultural harvester during harvesting operations, including receiving information maps, detecting speed characteristics, and generating a predictive speed map for use in controlling the agricultural harvester.

[0012] Figure 6 A block diagram of an example agricultural harvester communicating with a remote server environment is shown.

[0013] Figures 7 to 9 An example of a mobile device that can be used in agricultural harvesters is shown.

[0014] Figure 10 This is a block diagram illustrating an example of a computing environment that can be used in agricultural harvesters and in the structures shown in the previous figures. Detailed Implementation

[0015] To facilitate an understanding of the principles of this disclosure, reference will now be made to the examples described herein and illustrated in the accompanying drawings, and these examples will be described in specific language. However, it will be understood that this is not intended to limit the scope of this disclosure. Any changes and additional modifications to the described apparatus, systems, and methods, as well as any additional applications of the principles of this disclosure, are fully contemplated, as would normally occur to those skilled in the art related to this disclosure. In particular, it is fully contemplated that features, components, and / or steps described with respect to one example can be combined with features, components, and / or steps described with respect to other examples of this disclosure.

[0016] This specification relates to using field data acquired concurrently with agricultural operations, combined with prior data, to generate predictive maps, and more specifically, predictive speed maps. In some examples, predictive speed maps can be used to control agricultural machinery, such as combine harvesters. As discussed above, predictive speed maps can improve combine harvester performance by controlling the harvester's speed as it engages with varying conditions in the field. For example, if the crop is not yet mature, weeds may still be green, thus increasing the moisture content of the biomass encountered by the combine harvester. This problem can be exacerbated when weed clumps are wet (such as shortly after rainfall or when weed clumps contain dew) and before the weeds have a chance to dry. Therefore, when the combine harvester encounters an area of ​​increased biomass, the operator can slow the harvester to maintain a constant feed rate of material through it. Maintaining a constant feed rate helps maintain the performance of the combine harvester.

[0017] The performance of agricultural harvesters can be adversely affected by many different criteria. These criteria can include changes in biomass, crop condition, topography, soil properties, sowing characteristics, or other conditions. Therefore, it can also be useful to control the harvester speed based on other conditions that may exist in the field. For example, by controlling the harvester speed based on the biomass encountered by the harvester, the crop condition of the crop being harvested, the topography of the field being harvested, the soil properties of the soil in the field being harvested, the sowing characteristics of the field being harvested, the yield of the field being harvested, or other conditions existing in the field, the performance of the agricultural harvester can be maintained at an acceptable level.

[0018] Some current systems provide vegetation index maps. Vegetation index maps exemplarily map vegetation index values ​​(which can indicate plant growth) across different geographic locations within a field of interest. One example of a vegetation index includes the Normalized Difference Vegetation Index (NDVI). Many other vegetation indices exist within the scope of this disclosure. In some examples, vegetation indices can be derived from sensor readings of one or more electromagnetic radiation bands reflected by plants. These electromagnetic radiation bands are not limited to the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0019] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, these maps enable the identification and georeferencing of vegetation in the presence of bare soil, crop residues, or other vegetation (including crops or other weeds).

[0020] In some examples, a biomass map is provided. A biomass map exemplarily maps a measure of biomass in a harvested field at different locations within the field. A biomass map can be generated based on vegetation index values, historically measured or estimated biomass levels, images acquired during previous operations in the field, or other sensor readings, or otherwise. In some examples, biomass can be adjusted by a factor representing a portion of the total biomass passing through an agricultural harvester. For cereals, this factor is typically around 50%. In some examples, this factor can vary based on crop moisture content. In some examples, the factor can represent a portion of weed material or weed seeds. In some examples, the factor can represent a portion of one crop in an intercropping mix.

[0021] In some examples, a crop state map is provided. Crop state can define whether the crop is fallen, standing, partially fallen, or the orientation of a fallen or partially fallen crop relative to the ground surface or compass direction, and other factors. A crop state map exemplarily maps the crop state in a harvested field at different locations within the field. The crop state map can be generated based on spatial or other images of the field, images acquired during previous operations in the field, or other sensor readings, or otherwise generated prior to harvest.

[0022] In some examples, a seeding map is provided. A seeding map can map seeding characteristics at different locations in the field, such as seed location, seed variety, or seed population. The seeding map can be generated during past seeding operations in the field. The seeding map can be derived from control signals used by the seeder when planting seeds, or from sensors on the seeder that confirm seed dispensing or planting. The seeder may also include geolocation sensors to geolocate seeding characteristics in the field.

[0023] In some examples, soil property maps are provided. A soil property map exemplarily maps measures of one or more soil properties at different locations in a harvested field, such as soil type, soil chemical composition, soil structure, residue cover, tillage history, or soil moisture content. Soil property maps can be generated based on vegetation index values, historically measured or estimated soil properties, images or other sensor readings acquired during previous operations in the field, or otherwise.

[0024] In some examples, other infographics are provided. Such infographics may include topographic maps of fields being harvested, predicted yield maps of fields being harvested, or other infographics.

[0025] Therefore, this discussion continues with a system that receives a map or field information map generated during previous operations and also uses field sensors to detect one or more variables indicating machine speed and outputs from a feed rate control system. The system generates a model that models the relationship between information values ​​from the map and output values ​​from the field sensors. This model is used to generate a functionally predicted speed map that predicts, for example, the desired machine speed at different locations in the field. The functionally predicted speed map generated during harvesting operations can be presented to the operator or other user during harvesting operations or used to automatically control the agricultural harvester, or both.

[0026] Figure 1This is a partially drawn, partially schematic illustration of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although the 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, stalkers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the various types of harvesters described, and is therefore not limited to combine harvesters. In addition, this disclosure relates to other types of operating machines (such as agricultural seeders and sprayers), construction equipment, forestry equipment, and turf management equipment that can be applied to generate predictive maps. Therefore, this disclosure is intended to cover these various types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0027] like Figure 1 As shown, the agricultural harvester 100 exemplarily includes an operator's cab 101, which may have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes a set of front-end devices, 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 the frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the movement of the header 102 about the axis 105 in a direction generally indicated by arrow 109. Therefore, the vertical position (cutting height) of the cutting platform 102 above the ground 111 can be controlled by actuating the actuator 107, and the cutting platform 102 moves on the ground 111. Although Figure 1 Not shown, but the agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a tumble angle, or both to the header 102 or portions thereof. Tilt refers to the angle at which the cutter 104 engages with the crop. For example, the tilt angle is increased by controlling the header 102 to point the distal edge 113 of the cutter 104 more towards the ground. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 further away from the ground. Tumble refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0028] The threshing machine 110 exemplarily includes a threshing rotor 112 and a set of concave plates 114. Additionally, the combine harvester 100 includes a separator 116. The combine harvester 100 also includes a grain cleaning subsystem or grain cleaner (collectively referred to as 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 an unloading screw conveyor 134 and a nozzle 136. The clean grain elevator moves clean grain into a clean grain bin 132. The combine harvester 100 also includes a residue subsystem 138, which may include a shredder 140 and a spreader 142. The combine harvester 100 also includes a propulsion subsystem, which includes an engine driving ground engagement components 144 (such as wheels or tracks). In some examples, the combine harvester within the scope of this disclosure may have more than one of the subsystems mentioned above. In some examples, the agricultural harvester 100 may have left and right grain cleaning subsystems, separators, etc., which... Figure 1 Not shown in the image.

[0029] In operation, and in an outlined manner, the combine harvester 100 exemplarily moves across the field in the direction indicated by arrow 147. As the combine 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 combine harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the combine harvester 100 can determine one or more of the header 102's height setting, tilt angle setting, or tumble angle setting. For example, the operator inputs one or more settings to a control system of the actuator 107, which is described in more detail below. The control system can also receive settings from the operator to establish the header 102's tilt angle and tumble angle, and implement the input settings by controlling the associated actuators (not shown) to change the header 102's tilt angle and tumble angle. Actuator 107 maintains the cutter head 102 at a height above ground 111 based on a height setting, and, where applicable, maintains the cutter head 102 at a desired tilt and roll angle. Each of the height, roll, and tilt settings can be implemented independently of the others. The control system responds to cutter head errors (e.g., the difference between the height setting and the measured height of the cutter head 104 above ground 111, and in some examples, tilt and roll angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set to a higher level, the control system responds to smaller cutter head position errors and attempts to reduce the detected errors faster than when the sensitivity level is lower.

[0030] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material moves in the feeder housing 106 via a conveyor toward the feed accelerator 108, which accelerates the crop material into the thresher 110. The crop material is threshed by the rotation of the rotor 112 against the concave plate 114. The threshed crop material moves through the separator rotor in the separator 116, with a portion of the residue moving toward the residue subsystem 138 via the discharge agitator 126. The residue portion delivered to the residue subsystem 138 is shredded by the residue shredder 140 and spread on the field by the spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in piles. In other examples, the residue subsystem 138 may include a weed seed remover (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0031] The grain falls into the cleaning subsystem 118. A husk sieve 122 separates some larger clumps of grain, and a screen 124 separates some finer clumps from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet end of a clean grain elevator 130, which moves the clean grain upwards, 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 upwards along an airflow path through the screen and husk sieve. The airflow carries the residue backwards in the agricultural harvester 100 toward the residue treatment subsystem 138.

[0032] The tail material elevator 128 returns the tail material to the thresher 110, where it is re-threshed. Alternatively, the tail material can be transported by the tail material elevator or another conveying device to a separate re-threshing unit, where it is also re-threshed.

[0033] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a machine speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-view image capture mechanism 151 (which may 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.

[0034] Machine speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Machine speed sensor 146 can sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground-mounted 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), dead reckoning system, Long Range Navigation (LORAN) system, or a variety of other systems or sensors that provide an indication of travel speed.

[0035] The loss sensor 152 exemplarily provides an output signal indicating the amount of grain loss occurring on the left and right sides of the grain cleaning subsystem 118. In some examples, sensor 152 is a strike sensor that counts grain strikes per unit time or per unit distance traveled to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The strike sensors on the right and left sides of the grain cleaning subsystem 118 can provide individual signals, or they can provide combined or aggregated signals. In some examples, instead of providing individual sensors for each grain cleaning subsystem 118, sensor 152 may include a single sensor.

[0036] Separator loss sensor 148 provides indication of the left and right separators (in Figure 1 (Not shown separately) The grain loss signal in the separator. The separator loss sensor 148 can be associated with the left and right separators and can provide individual grain loss signals, or combined or aggregated signals. In some cases, sensing grain loss in the separator can also be performed using a variety of different types of sensors.

[0037] 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 oscillating or bumpy motion (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, generate a pile, etc.; a 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; and a threshing rotor speed sensor that senses the rotational speed of the rotor 112. The harvester 100 includes: a chaff screen gap sensor that senses the size of the opening in the chaff screen 122; a screen mesh gap sensor that senses the size of the opening in the screen 124; a grain outside material (MOG) moisture sensor that senses the moisture level of the MOG passing through the harvester 100; one or more machine setting sensors configured to sense various configurable settings of the harvester 100; a machine orientation sensor that senses the orientation of the 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 the characteristics of the cut crop material while the harvester 100 is processing the crop material. For example, in some cases, the crop property sensor may sense: grain quality, such as broken grain, MOG level; grain composition, such as starch and protein; and the grain feed rate as the grain travels through the feeder housing 106, the clean grain elevator 130, or other locations within the harvester 100. The crop property sensor can also sense the feed rate of biomass through the feeder housing 106, through the separator 116, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate as the mass flow rate of grain through the elevator 130 or through other parts of the harvester 100, or provide other output signals indicating other sensed variables.

[0038] Before describing how the agricultural harvester 100 generates a function prediction speed map and uses the function prediction speed map for control, a brief description of some objects on the agricultural harvester 100 and their operation will be given first. Figure 2Figure 3 illustrates the reception of a general type of infographic and the combination of information from the infographic with georeferenced sensor signals generated by field sensors, wherein the sensor signals indicate characteristics of the field, such as characteristics of the field itself, crops present in the field, or crop characteristics of grains or characteristics of agricultural harvesters. Field characteristics may include, but are not limited to: field characteristics such as slope, weed intensity, weed type, soil moisture, and surface quality; crop characteristics such as crop height, crop moisture, crop density, and crop condition; grain characteristics such as grain moisture, grain size, and grain test weight; and machine operating characteristics such as machine speed, outputs from different controllers, and machine performance (such as wear levels, work quality, fuel consumption, and power utilization). Relationships between characteristic values ​​obtained from or derived from the field sensor signals and infographic values ​​are identified, and these relationships are used to generate new functional prediction maps. The functional prediction maps predict values ​​at different geographic locations in the field, and one or more of these values ​​can be used to control one or more subsystems of a machine, such as an agricultural harvester. In some cases, functional prediction maps can be presented to users, such as operators of agricultural machinery, which may be harvesters. Functional prediction maps can be presented to users visually (e.g., via a display), tactilely, or audibly. Users can interact with the functional prediction map to perform editing operations and other user interface operations. In some cases, functional prediction maps can be used to control agricultural machinery (such as harvesters), be presented to operators or other users, and be presented to operators or users to facilitate operator or user interaction, among one or more of these methods.

[0039] In reference Figure 2 After describing the overall method in Figure 3, refer to Figure 4 and Figure 5 A more specific method for generating a functional predictive speed map is described, which may be presented to an operator or user, or used to control an agricultural harvester 100, or both. Furthermore, while this discussion continues with agricultural harvesters (and particularly combine harvesters), the scope of this disclosure covers other types of agricultural harvesters or other agricultural machinery.

[0040] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2As shown, an agricultural harvester 100 exemplarily includes one or more processors or servers 201, data storage 202, a geographic location sensor 204, a communication system 206, and one or more field sensors 208 that simultaneously sense one or more agricultural characteristics of the field during harvesting operations. Agricultural characteristics can include any characteristics that may affect the harvesting operations. Some examples of agricultural characteristics include characteristics of the 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 relation generator (hereinafter collectively referred to as "prediction model generator 210"), a prediction map generator 212, a control area 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 field characteristics during agricultural operations. Predictive model generator 210 exemplarily includes an information variable-to-in-situ variable model generator 228, and 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 belt controller 240, a platform position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and may include other objects 246. The controllable subsystem 216 includes machine and header actuator 248, propulsion subsystem 250, steering subsystem 252, residue subsystem 138, machine grain cleaning subsystem 254, and subsystem 216 may include various other subsystems 256.

[0041] Figure 2 It is also shown that the agricultural harvester 100 can receive an information map 258. As described below, the information map 258 includes, for example, a vegetation index map, a biomass map, a crop status map, a topographic map, a soil property map, a sowing map, or a map from a previous operation. However, the information map 258 may also encompass other types of data obtained prior to the harvesting operation, or maps from a previous operation. Figure 2It is also shown that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include joysticks, levers, steering wheels, linkages, pedals, buttons, dials, keyboards, user-actuable elements (such as icons, buttons, etc.) on a user interface display device, microphones and speakers (where speech recognition and speech synthesis are provided), and a variety of other types of control devices. Where a touch-sensitive display system is provided, operator 260 can interact with operator interface mechanism 218 using touch gestures. The examples described above are provided as illustrative examples and are not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may be used and are within the scope of this disclosure.

[0042] Information diagram 258 can be downloaded to the agricultural harvester 100 and stored in data storage 202 using communication system 206 or otherwise. In some examples, communication system 206 may be a cellular communication system, a system for communication over a wide area network or a local area network, a system for communication over a near-field communication network, or a communication system configured to communicate on any of a variety of other networks or a combination of networks. Communication system 206 may also include a system that facilitates downloading or transferring information to or from a Secure Digital (SD) card or a Universal Serial Bus (USB) card, or both a Secure Digital (SD) card and a USB card.

[0043] The geolocation sensor 204 exemplarily senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a Global Navigation Satellite System (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geolocation sensor 204 may also include a real-time kinematics (RTK) component configured to improve the accuracy of position data derived from GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0044] Field sensor 208 can be referenced above. Figure 1 Any of the sensors described. Field sensor 208 includes airborne sensor 222, which is mounted airborne on the agricultural harvester 100. Such sensors may include, for example, those described above. Figure 1The discussed sensors include any of the sensors, sensing sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems), and image sensors within the agricultural harvester 100 (such as one or more clean grain cameras mounted to identify material leaving the agricultural harvester 100 through or from the residue subsystem). Field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the harvester or from any sensors that detect data during harvesting operations.

[0045] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and metrics mapped to the field by infographic 258. For example, if infographic 258 maps vegetation index values ​​to different locations in the field, and field sensors 208 are sensing values ​​indicating machine speed, then information variable to field variable model generator 228 generates a predictive speed model that models the relationship between vegetation index values ​​and machine speed values. The predictive speed model can also be generated based on vegetation index values ​​from infographic 258 and multiple field data values ​​generated by field sensors 208. Then, predictive map generator 212 uses the predictive speed model generated by predictive model generator 210 to generate a functional predictive speed map, which predicts the desired machine speed sensed by field sensors 208 at different locations in the field based on infographic 258.

[0046] In some examples, the type of values ​​in Functional Prediction Chart 263 may be the same as the field data type sensed by Field Sensor 208. In some cases, the type of values ​​in Functional Prediction Chart 263 may have different units than the data sensed by Field Sensor 208. In some examples, the type of values ​​in Functional Prediction Chart 263 may be different from the data type sensed by Field Sensor 208, but related to the type of data sensed by Field Sensor 208. For example, in some examples, the data type sensed by Field Sensor 208 may indicate the type of values ​​in Functional Prediction Chart 263. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in Infographic 258. In some cases, the data type in Functional Prediction Chart 263 may have different units than the data in Infographic 258. In some examples, the data type in Functional Prediction Chart 263 may be different from the data type in Infographic 258, but related to the data type in Infographic 258. For example, in some examples, the data type in Infographic 258 may indicate the data type in Functional Prediction Chart 263. In some examples, the data type in the function prediction graph 263 is different from one or both of the field data type sensed by the field sensor 208 and the data type in the 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 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 information graph 258, but different from the other.

[0047] Continuing with the previous example, where Infographic 258 is a vegetation index map and field sensor 208 senses values ​​indicating machine speed, prediction map generator 212 can use the vegetation index values ​​in Infographic 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 predicting the desired machine speed at different locations in the field. Therefore, prediction map generator 212 outputs prediction map 264.

[0048] like Figure 2As shown, prediction map 264 uses information values ​​from information map 258 at those locations and a prediction model to predict values ​​of sensed characteristics (sensed by field sensors 208) or characteristics related to sensed characteristics at multiple locations across the field. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between vegetation index values ​​and machine speed, then given vegetation index values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting target machine speed values ​​at those different locations across the field. The vegetation index values ​​at those locations obtained from the vegetation index map, and the relationship between the vegetation index values ​​and machine speed obtained from the prediction model, are used to generate prediction map 264.

[0049] The following will now describe some variations in the data types mapped in Infographic 258, the data types sensed by Field Sensor 208, and the data types predicted in Prediction Graph 264.

[0050] In some examples, the data type in Infographic 258 differs from the data type sensed by Field Sensor 208, while the data type in Prediction Graph 264 is the same as that sensed by Field Sensor 208. For example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be yield. Prediction Graph 264 could then be a predicted yield map mapping the predicted yield values ​​to different geographic locations in the field. In another example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be crop height. Prediction Graph 264 could then be a predicted crop height map mapping the predicted crop height values ​​to different geographic locations in the field.

[0051] Furthermore, in some examples, the data type in Infographic 258 differs from the data type sensed by Field Sensor 208, and the data type in Prediction Graph 264 differs from both the data type in Infographic 258 and the data type sensed by Field Sensor 208. For example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be crop height. Prediction Graph 264 could then be a predicted biomass map mapping predicted biomass values ​​to different geographic locations in the field. In another example, Infographic 258 could be a vegetation index map, and the variable sensed by Field Sensor 208 could be yield. Prediction Graph 264 could then be a predicted speed map mapping predicted harvester speed values ​​to different geographic locations in the field.

[0052] In some examples, Infographic 258 is generated based on previous passage through the field during a previous operation, and the data type differs from the data type sensed by Field Sensor 208, while the data type in Predicted Infographic 264 is the same as that sensed by Field Sensor 208. For example, Infographic 258 could be a seed population map generated during planting, and the variable sensed by Field Sensor 208 could be straw size. Predicted Infographic 264 could then be a predicted straw size map mapping the predicted straw size values ​​to different geographic locations in the field. In another example, Infographic 258 could be a seed mix map, and the variable sensed by Field Sensor 208 could be crop state, such as standing or fallen crops. Predicted Infographic 264 could then be a predicted crop state map mapping the predicted crop state values ​​to different geographic locations in the field.

[0053] In some examples, InfoMap 258 is generated based on previous passage through the field during a previous operation, and the data type is the same as that sensed by Field Sensor 208, and the data type in Predictive Map 264 is also the same as that sensed by Field Sensor 208. For example, InfoMap 258 could be a yield map generated during the previous year, and the variable sensed by Field Sensor 208 could be yield. Predictive Map 264 could then be a predicted yield map mapping the predicted yield values ​​to different geographic locations within the field. In such an example, the relative yield difference between the geographically referenced InfoMap 258 and the previous year can be used by Predictive Model Generator 210 to generate a predictive model that models the relationship between the relative yield difference on InfoMap 258 and the yield values ​​sensed by Field Sensor 208 during the current harvest operation. The predictive model is then used by Predictive Map Generator 210 to generate a predicted yield map.

[0054] In another example, Infographic 258 could be a weed intensity map generated during a previous operation, such as from a sprayer, and the variable sensed by field sensor 208 could be weed intensity. Prediction map 264 could then be a predicted weed intensity map mapping predicted weed intensity values ​​to different geographic locations in the field. In such an example, the weed intensity map at the time of spraying is georeferenced and provided to the harvester 100 as Infographic 258 of weed intensity. Field sensor 208 can detect weed intensity at geographic locations in the field, and then prediction model generator 210 can construct a predictive model that models the relationship between weed intensity at harvest and weed intensity at spraying. This is because spraying will affect weed intensity at the time of application, but weeds may reappear in similar areas at harvest. However, at harvest, weed areas are likely to have different intensities based on the time of harvest, weather, weed type, and other factors.

[0055] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups adjacent portions of a region into one or more control zones based on data values ​​associated with those adjacent portions of prediction map 264. A control zone may include two or more consecutive portions of a region (such as a field), for which control parameters corresponding to the control zones used to control the controllable subsystem are constant. For example, the response time for changing the settings of controllable subsystem 216 may be insufficient to provide a satisfactory response to changes in 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 defined dimensions to accommodate the response time of controllable subsystem 216. In another example, 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 prediction control zone map 265. Therefore, except that the predicted control area map 265 includes control area information defining the control area, the predicted control area map 265 can be similar to the predicted map 264. Thus, the functional prediction map 263, as described herein, may or may not include control areas. Both predicted map 264 and predicted control area map 265 are functional prediction maps 263. In one example, the functional prediction map 263 does not include control areas, as in predicted map 264. In another example, the functional prediction map 263 includes control areas, as in predicted control area map 265. In some examples, if an intercropping production system is implemented, multiple crops can coexist in the field. In that case, the prediction map generator 212 and the control area generator 213 can identify the location and characteristics of two or more crops and then generate the predicted map 264 and predicted control area map 265 accordingly.

[0056] It will also be understood that the control region generator 213 can cluster values ​​to generate control regions, and these control regions can be added to the predicted control region graph 265 or simply shown as a discrete graph of the generated control regions. In some examples, the control regions can be used to control and / or calibrate the agricultural harvester 100. In other examples, the control regions can be presented to the operator 260 and used to control or calibrate the agricultural harvester 100, and in still other examples, the control regions can be presented to the operator 260 or another user, or stored for later use.

[0057] Prediction map 264 or prediction control area map 265, or both, is provided to control system 214, which generates control signals based on prediction map 264 or prediction control area map 265, or both. In some examples, communication system controller 229 controls communication system 206 to communicate prediction map 264 or prediction control area map 265, or control signals based on prediction map 264 or prediction control area map 265, to other agricultural harvesters harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to transmit prediction map 264, prediction control area map 265, or both, to other remote systems.

[0058] 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 area map 265, or other information derived from or based on the prediction map 264, the prediction control area map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanism to display one or both of the prediction map 264 and the prediction control area map 265 to the operator 260. The controller 231 can generate an operator-actuable mechanism that is displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting the type of weeds displayed on the map based on the operator's observation. The settings controller 232 can generate control signals based on the prediction map 264, the prediction control area map 265, or both, to control various settings of the agricultural harvester 100. For example, controller 232 can generate control signals for controlling the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, concave plate clearance, rotor settings, grain clearing fan speed settings, header height, header function, reel speed, reel position, conveyor function (where the harvester 100 is coupled to the conveyor header), corn header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. Path planning controller 234 exemplarily generates control signals to control steering subsystem 252 to turn the harvester 100 according to a desired path. Path planning controller 234 can control a path planning system to generate a route for the harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along said route. The feed rate controller 236 can receive various inputs indicating the feed rate of material through the harvester 100, and can control various subsystems (such as the propulsion subsystem 250 and machine actuator 248) to control the feed rate based on prediction diagram 264 or prediction control area diagram 265, or both. For example, when the harvester 100 approaches a clump of weeds with a strength value higher than a selected threshold, the feed rate controller 236 can generate a control signal for controlling the propulsion subsystem 252 to reduce the speed of the harvester 100, thereby maintaining a constant feed rate of biomass through the 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 prediction diagram 264, prediction control area diagram 265, or both.The platform position controller 242 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the position of the platform included on the harvester, and the residue system controller 244 can control the residue subsystem 138 based on prediction diagram 264 or prediction control area diagram 265, or both. The machine cleaning controller 245 can generate control signals for controlling the machine cleaning subsystem 254. For example, the frequency of a specific type of machine cleaning operation or the execution of the cleaning process can be controlled based on the different types of seeds or weeds passing through the agricultural harvester 100. Other controllers included on the agricultural harvester 100 can also control other subsystems based on prediction diagram 264 or prediction control area diagram 265, or both.

[0059] Figure 3A and Figure 3B (Hereinafter referred to collectively as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester 100 when generating a prediction map 264 and a prediction control area map 265 based on information map 258.

[0060] At 280, the agricultural harvester 100 receives information map 258. Examples of information map 258 or receiving information map 258 are discussed with reference to boxes 281, 282, 284, and 286. As discussed above, information map 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field, as indicated by box 282. As indicated by box 281, receiving information map 258 may include selecting one or more of a plurality of possible information maps available. For example, one information map may be a vegetation index map generated according to aerial imaging methods. Another information map may be a map generated during a previous passage through the field that can be performed by different machines performing previous operations in the field, such as sprayers, planting machines, seeding machines, unmanned aerial vehicles (UAVs), or other machines. The process of selecting one or more information maps can be manual, semi-automatic, or automatic. Information map 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, data can be collected based on aerial images acquired in the previous year, early in the current growing season, or at another time. This data can be based on data detected in ways other than using aerial images. For example, the agricultural harvester 100 can be equipped with sensors, such as internal optical sensors, to identify weed seeds or other types of material leaving the harvester 100. Weed seed or other data detected by the sensors during the previous year's harvest can be used as data to generate Infographic 258. The sensed weed data or other data is combined with other data to generate Infographic 258. For example, based on the amount of weed seeds leaving the agricultural harvester 100 at different locations and based on other factors (such as whether the seeds are being dispersed by a spreader or falling into a stockpile), weather conditions (such as wind when the seeds are falling or being dispersed), drainage conditions that can move the seeds around the field, or other information, the location of those weed seeds can be predicted, allowing Infographic 258 to map the predicted planting locations in the field. The data in Infographic 258 can be transmitted to the harvester 100 using communication system 206 and stored in data memory 202. The data in Infographic 258 can also be provided to the harvester 100 in other ways using communication system 206, as indicated by block 286 in the flowchart of Figure 3. In some examples, Infographic 258 can be received by communication system 206.

[0061] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values ​​of indicative characteristics, such as velocity characteristics, as indicated by box 288. Examples of field sensor 288 are discussed with respect to boxes 222, 290, and 226. As explained above, field sensor 208 includes: airborne sensor 222; remote field sensor 224, such as a UAV-based sensor that collects field data during a single flight, shown in box 290; or other types of field sensors specified by field sensor 226. In some examples, position, direction of travel, or velocity data from geolocation sensor 204 is georeferenced against data from airborne sensors.

[0062] Predictive model generator 210 controls model generator 228 of information variables to generate a model that models the relationship between the mapped values ​​contained in Infographic 258 and the field values ​​sensed by field sensor 208, as indicated by box 292. The characteristics of the data types represented by the mapped values ​​in Infographic 258 and the field values ​​sensed by field sensor 208 can be the same or different.

[0063] The relation or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 generates a prediction map 264, which uses the prediction model and the information map 258 to predict the values ​​of characteristics sensed by the field sensor 208 at different geographic locations in the field that are being harvested, or the values ​​of different characteristics related to the characteristics sensed by the field sensor 208, as indicated by the box 294.

[0064] It should be noted that in some examples, Infographic 258 may include two or more different plots, or two or more different layers of a single plot. Each layer may represent a data type different from that of another layer, or the layers may have the same data type obtained at different times. Each plot in the two or more different plots, or each layer in the two or more different layers of a plot, maps different types of variables to geographical locations in the field. In such an example, Predictive Model Generator 210 generates a predictive model that models the relationship between the field data and each of the different variables mapped by the two or more different plots or the two or more different layers. Similarly, Field Sensors 208 may include two or more sensors, each sensing different types of variables. Thus, Predictive Model Generator 210 generates a predictive model that models the relationship between each type of variable mapped by Infographic 258 and each type of variable sensed by Field Sensors 208. The prediction map generator 212 can generate a functional prediction map 263, which uses a prediction model and each of the maps or layers in the 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 that are being harvested.

[0065] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be executed (or used) by control system 214. Prediction map generator 212 may provide prediction map 264 to control system 214 or control area generator 213, or both. Some examples of different ways in which prediction map 264 can be configured or output are described with reference to blocks 296, 295, 299, and 297. For example, prediction map generator 212 configures prediction map 264 such that prediction map 264 includes values ​​that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems for agricultural harvester 100, as indicated in block 296.

[0066] Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Consecutive geolocation values ​​within a threshold can be grouped into control zones. The threshold can be a default threshold, or it can be set based on operator input, input from an automation system, or other criteria. The size of each zone can be based on the response of control system 214, controllable subsystem 216, wear considerations, or other criteria, as indicated by box 295. Control zone generator 213 can configure prediction control zone map 265 for presentation to an operator or other user. This is indicated by box 299. When presented to an operator or other user, the presentation of prediction map 264 or prediction control zone map 265, or both, can include geolocation-related predicted values ​​on prediction map 264, geolocation-related control zones on prediction control zone map 265, and one or more setting values ​​or control parameters used based on the predicted values ​​on map 264 or zones on prediction control zone map 265. In another example, the presentation may include either more concise or more detailed information. The presentation may also include a confidence level indicator, which indicates the accuracy of the degree of agreement between the predicted value on prediction map 264 or the area on prediction control area map 265 and measurements that can be taken by sensors on the agricultural harvester 100 as it moves through the field. Additionally, where information is presented in more than one location, an authentication and authorization system may be provided to implement the authentication and authorization process. For example, there may be individual levels authorized to view and modify the maps and other presented information. By way of example, the onboard display may display the maps locally on the machine in near real-time, or the maps may be generated at one or more remote locations, or both. In some examples, each physical display at each location may be associated with a person or user permission level. The user permission level may be used to determine which display identifiers are visible on the physical display and which values ​​the corresponding person can change. As an example, the local operator of machine 100 may not be able to see the information corresponding to prediction map 264 or make any changes to the operation of the machine. However, a monitor (such as one at a remote location) may be able to see the prediction diagram 264 on the display but be prevented from making any changes. An administrator, possibly located at a separate remote location, may be able to see all elements on the prediction diagram 264 and also be able to modify it. In some cases, the prediction diagram 264, accessible and modifiable by a remote administrator, can be used for machine control. This is an example of the levels of authorization that can be implemented. The prediction diagram 264, or the prediction control area diagram 265, or both, may also be configured in other ways, as indicated by box 297.

[0067] At box 298, inputs from the geolocation sensor 204 and other field sensors 208 are received by the control system. Specifically, at box 300, the control system 214 detects inputs from the geolocation sensor 204, which identifies the geographical location of the harvester 100. Box 302 indicates that the control system 214 receives sensor inputs indicating the trajectory or direction of travel of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives other information from the various field sensors 208.

[0068] At block 308, control system 214 generates control signals to control controllable subsystem 216 based on prediction map 264 or prediction control area map 265, or both, and inputs from geographic location sensor 204 and any other field sensors 208. At block 310, control system 214 applies the control signals to the controllable subsystem. It will be understood that the specific control signals generated and the specific controllable subsystem 216 being controlled can vary based on one or more different things. For example, the generated control signals and the controllable subsystem 216 being controlled can be based on the type of prediction map 264 or prediction control area map 265, or both, being used. Similarly, the generated control signals and the controllable subsystem 216 being controlled, as well as the timing of the control signals, can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0069] As an example, the generated prediction map 264, in the form of a predicted velocity map, can be used to control one or more subsystems 216. For example, the predicted velocity map may include velocity values ​​that are georeferenced to locations within the field being harvested. Velocity values ​​from the predicted velocity map can be extracted and used to control the propulsion subsystem 250. By controlling the propulsion subsystem 250, the feed rate of material movement through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to accommodate more or less material, and thus the header height can also be controlled to control the feed rate of material through the agricultural harvester 100. In other examples, control of the header height can be achieved if the prediction map 264 maps weed height relative to locations in the field. For example, if the values ​​presented in the predicted weed map indicate a weed height of a first height in one or more areas, the header and reel controller 238 can control the header height such that, when performing a harvesting operation, the header is positioned above the first height of weeds in one or more areas with the first height of weeds. Therefore, the header and reel controller 238 can be controlled using the geographic reference values ​​presented in the predicted weed map to position the header at a height higher than the predicted height value of the weeds obtained from the predicted weed map. Additionally, when the agricultural harvester 100 passes through the field, the header height can be automatically changed using the geographic reference values ​​obtained from the predicted weed map via the header and reel controller 238. The previous example involving the use of weed height and intensity from the predicted weed map is provided only as an example. Therefore, a wide variety of other control signals can be generated using values ​​obtained from the predicted weed map or other types of prediction maps to control one or more of the controllable subsystems 216.

[0070] At box 312, it is determined whether the harvesting operation has been completed. If the harvesting is not completed, the process proceeds to box 314, where it continues to read field sensor data from geolocation sensor 204 and field sensor 208 (and possibly other sensors).

[0071] 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 following: the prediction map 264, the prediction control area map 265, the model generated by the prediction model generator 210, the area generated by the control area generator 213, one or more control algorithms executed by the controller in the control system 214, and other triggered learning.

[0072] Learning triggering criteria can include any of a wide variety of different criteria. Some examples of triggering criteria detection are discussed with reference to boxes 318, 320, 321, 322, and 324. For example, in some examples, learning triggered when a threshold amount of field sensor data is received from field sensor 208 can involve recreating the relationships used to generate the predictive model. In such an example, receiving more than a threshold amount of field sensor data from field sensor 208 triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212. Thus, as the agricultural harvester 100 continues, a threshold amount of field sensor data received from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Additionally, a new predictive map 264, predictive control area map 265, or both can be regenerated using the new predictive model. Box 318 indicates the threshold amount of field sensor data detected to trigger the creation of a new predictive model.

[0073] In other examples, the learning trigger criterion can be based on the degree to which field sensor data from field sensor 208 changes (e.g., changes over time or compared to previous values). For example, if the change in field sensor data (or the relationship between field sensor data and information in Infographic 258) is within a selected range or less than a threshold or a limit specified below, the prediction model generator 210 will not generate a new prediction model. As a result, the prediction map generator 212 will not generate a new prediction map 264, prediction control area map 265, or both. However, for example, if the change in field sensor data is outside the selected range, greater than a threshold, or above a limit, the prediction model generator 210 will use all or part of the newly received field sensor data used by the prediction map generator 212 to generate a new prediction map 264 to generate a new prediction model. At block 320, changes in field sensor data (such as the magnitude of data exceeding a selected range, or the magnitude of changes in the relationship between the field sensor data and information in Infographic 258) can be used as triggers to induce the generation of new prediction models and prediction maps. Consistent with the examples described above, the thresholds, ranges, and limits can be set to default values; set by an operator or user through a user interface interaction; set by an automated system; or set in other ways.

[0074] Other learning trigger criteria can also be used. For example, if the prediction model generator 210 switches to a different infographic (different from the initially selected infographic 258), switching to a different infographic can trigger relearning by the prediction model generator 210, the prediction map generator 212, the control area generator 213, the control system 214, or other objects. In another example, the agricultural harvester 100 switching to a different terrain or a different control area can also be used as a learning trigger criterion.

[0075] In some cases, operator 260 can also edit prediction graph 264 or prediction control region graph 265, or both. The editing can change the values ​​on prediction graph 264, change the size, shape, position, or presence of control regions on prediction control region graph 265, or both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0076] In some cases, operator 260 may also observe that the automated control of the controllable subsystem is not as the operator expects. In these cases, operator 260 may provide manual adjustments to the controllable subsystem reflecting the operator's expectation that the controllable subsystem will operate in a different manner than what is being commanded by control system 214. Thus, manual changes to settings made by operator 260 may, based on operator 260's adjustments, cause predictive model generator 210 to relearn the model, cause predictive graph generator 212 to regenerate graph 264, cause control area generator 213 to regenerate one or more control areas on predictive control area graph 265, and cause control system 214 to relearn the control algorithm, or perform machine learning on one or more controller components 232 to 246 in control system 214, as shown in block 322. Block 324 indicates the use of other triggered learning criteria.

[0077] In other examples, relearning can be performed periodically or intermittently, for example, based on selected time intervals (such as discrete or variable time intervals), as indicated in box 326.

[0078] As indicated by box 326, if relearning is triggered (whether based on a learning trigger criterion or on the elapsed time interval), one or more of the predictive model generator 210, predictive map generator 212, control region generator 213, and control system 214 perform machine learning based on the learning trigger criterion to generate a new predictive model, a new predictive map, a new control region, and a new control algorithm, respectively. Any additional data collected since the last learning operation is performed is used to generate the new predictive model, the new predictive map, and the new control algorithm. Relearning is instructed to be performed by box 328.

[0079] If the harvesting operation has been completed, the operation moves from box 312 to box 330, where one or more of the prediction map 264, the prediction control area map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control area map 265, and the prediction model can be stored locally on data storage 202 or sent to a remote system using communication system 206 for later use.

[0080] It should be noted that while some examples in this document describe the predictive model generator 210 and the predictive graph generator 212 receiving information graphs when generating the predictive model and the functional predictive graph, respectively, in other examples, the predictive model generator 210 and the predictive graph generator 212 may receive other types of graphs, including predictive graphs, such as functional predictive graphs generated during the harvesting operation, when generating the predictive model and the functional predictive graph, respectively.

[0081] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, among other things, Figure 4 Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4 The information flow between the various components shown is also illustrated. The prediction model generator 210 receives an information map 258, which may be a vegetation index map 332, a predicted yield map 333, a biomass map 335, a crop status map 337, a topographic map 339, a soil property map 341, a sowing map 343, or another map 353 as an information map. The prediction model generator 210 also receives a geographic location 334, or an indication of geographic location, from the geographic location sensor 204. The field sensors 208 exemplarily include a machine speed sensor 146 or an output sensor 336 sensing data from the feed rate controller 236, and a processing system 338. The processing system 338 processes sensor data generated from the machine speed sensor 146 or sensor 336, or both, to generate processed data, some examples of which are described below.

[0082] In some examples, sensor 336 may be a sensor that generates a signal indicating the control output from feed rate controller 236. The control signal may be a speed control signal or other control signal applied to controllable subsystem 216 to control the feed rate of material through agricultural harvester 100. Processing system 338 processes the signal obtained via sensor 336 to generate processed data 340 identifying the speed of agricultural harvester 100.

[0083] In some examples, raw or processed data from field sensors 208 can be presented to operator 260 via operator interface mechanism 218. Operator 260 can be on agricultural harvester 100 or at a remote location.

[0084] This discussion continues with the example of field sensor 208 being the machine speed sensor 146. It will be understood that this is merely an example, and other examples of field sensor 208 from which machine speed can be derived are also considered herein. Figure 4 The exemplary prediction model generator 210 shown includes one or more of the following: vegetation index (VI) value to velocity model generator 342, biomass to velocity model generator 344, topography to velocity model generator 345, yield to velocity model generator 347, crop state to velocity model generator 349, soil property to velocity model generator 351, and sowing characteristic to velocity model generator 346. In other examples, the prediction model generator 210 may include... Figure 4 The examples shown are compared to those with additional, fewer, or different components. Therefore, in some examples, the predictive model generator 210 may also include other objects 348, which may include other types of predictive model generators for generating other types of models.

[0085] Model generator 342 identifies the relationship between machine speeds detected in processed data 340 at geographic locations corresponding to the obtained processed data 340 and vegetation index values ​​from vegetation index map 332 corresponding to the same locations in the field where speed characteristics were detected. Model generator 342 generates a predictive speed model based on this relationship. The predictive speed model is used by speed map generator 352 to predict target machine speeds at different locations in the field based on georeferenced vegetation index values ​​included in vegetation index map 332 at the same locations in the field.

[0086] Model generator 344 identifies the relationship between machine speed at a geographic location corresponding to processed data 340 and biomass values ​​at the same geographic location, represented as processed data 340. Furthermore, the biomass values ​​are geographic reference values ​​included in biomass map 335. Model generator 344 then generates a predicted velocity model based on the biomass values ​​at a location in the field, which is used by velocity map generator 352 to predict the target machine speed at said location in the field.

[0087] Model generator 345 identifies the relationship between the machine speed at a specific location in the field, as identified by processed data 340, and the terrain speed model used by speed map generator 352, based on terrain characteristic values ​​at a specific location in the field, in order to predict the desired machine speed at said location in the field.

[0088] Model generator 346 identifies the relationship between the machine speed at a specific location in the field, as identified by processed data 340, and the seeding characteristic values ​​at the same location from seeding characteristic map 343. Model generator 346 generates a predictive speed model based on the seeding characteristic values ​​at the specific location in the field, which is used by speed map generator 352 to predict the desired machine speed at said location in the field.

[0089] Model generator 347 identifies the relationship between the machine speed at a specific location in the field identified by processed data 340 and the yield characteristic value at the same location from the predicted yield map 333. Model generator 347 generates a predicted speed model used by speed map generator 352 based on the yield characteristic value at the specific location in the field to predict the desired machine speed at said location in the field.

[0090] Model generator 349 identifies the relationship between machine speed at a specific location in the field, as identified by processed data 340, and crop state characteristic values ​​at the same location from crop state map 337. Model generator 349 generates a predictive speed model, used by speed map generator 352, based on the crop state characteristic values ​​at the specific location in the field, to predict the desired machine speed at said location in the field.

[0091] Model generator 351 identifies the relationship between machine speed at a specific location in the field, as identified by processed data 340, and soil property characteristic values ​​at the same location from soil property map 341. Model generator 351 generates a predictive speed model, used by speed map generator 352, based on the soil property characteristic values ​​at the specific location in the field, to predict the desired machine speed at said location in the field.

[0092] As described above, the prediction model generator 210 is operable to generate multiple prediction speed models, such as one or more prediction speed models generated by model generators 342, 344, 345, 346, 347, 349, and 351. In another example, two or more of the prediction speed models described above can be combined into a single prediction speed model, which predicts the desired machine speed based on two or more of the following: vegetation index values, biomass values, topography, yield, sowing characteristics, crop status, and soil properties at different locations in the field. Any of these speed models, and combinations thereof, in... Figure 4 The three are collectively represented as prediction model 350.

[0093] The prediction model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a velocity map generator 352. 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 358, which may include other types of map generators for generating velocity maps. The velocity map generator 352 receives a prediction model 350 (which predicts the speed of a target machine based on values ​​from one or more infographics 258) and generates a prediction map that predicts the speed of the target machine at different locations in the field.

[0094] Prediction map generator 212 outputs one or more functional prediction speed maps 360, which predict desired machine speeds. The functional prediction speed maps 360 predict desired machine speeds at different locations in the field. The functional prediction speed maps 360 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 the functional prediction maps (i.e., prediction maps 360) to generate a predictive control zone map 265. One or both of prediction map 264 and predictive control zone map 265 can be provided to control system 214, which controls one or more controllable subsystems 216, such as propulsion subsystem 250, based on prediction map 264, predictive control zone map 265, or both.

[0095] Figure 5This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating the prediction model 350 and the functional prediction speed map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive an information map 258. Information map 258 can be any of figures 332, 333, 335, 337, 339, 341, or 343. Additionally, block 361 indicates that the received information map can be a single map. Block 363 indicates that the information map can be multiple maps or multiple layers. Block 365 indicates that information map 258 can also take other forms. At block 364, the processing system 338 receives signals from machine speed sensor 146 or sensor 336, or one or more of both.

[0096] At box 372, the processing system 338 processes one or more received signals to generate processed data 340 indicating the machine speed of the agricultural harvester 100.

[0097] At box 382, ​​the predictive model generator 210 also obtains the geographic location 334 corresponding to the processed 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 of the processed data 340 obtained or derived from it based on machine latency, machine speed, etc.

[0098] At box 384, prediction model generator 210 generates one or more prediction models, such as prediction model 350, that model the relationship between values ​​in one or more infographics 258 and machine speeds sensed by field sensors 208. VI value-speed model generator 342 generates a prediction model that models the relationship between VI values ​​in VI graph 332 and machine speeds sensed by field sensors 208. Biomass-speed model generator 344 generates a prediction model that models the relationship between biomass values ​​in biomass graph 335 and machine speeds sensed by field sensors 208. Topography-speed model generator 345 generates a prediction model that models the relationship between one or more topography values ​​(such as pitch, roll, or tilt in topography graph 339) and machine speeds sensed by field sensors 208. Seeding characteristic-speed model generator 346 generates a prediction model that models the relationship between seeding characteristics in seeding graph 343 and machine speeds sensed by field sensors 208. Yield-to-speed model generator 347 generates a predictive model that models the relationship between yield values ​​in yield map 333 and machine speeds sensed by field sensor 208. Crop state-to-speed model generator 342 generates a predictive model that models the relationship between crop state values ​​in crop state map 337 and machine speeds sensed by field sensor 208. Soil property-to-speed model generator 351 generates a predictive model that models the relationship between soil property values ​​in soil property map 337 and machine speeds sensed by field sensor 208. At box 386, predictive model 350 is provided to predictive map generator 212, which maps the predicted target machine speed based on information map 258 and predictive speed model 350. Speed ​​map generator 352 can use predictive model 350, which models the relationship between VI values ​​in VI map 332 and machine speed, and use VI map 332 to generate the functional predicted speed map 360. Speed ​​map generator 352 can use a prediction model 350 that models the relationship between yield values ​​and machine speed in yield map 333, and use the yield map 333 to generate a functional predicted speed map 360. Speed ​​map generator 352 can use a prediction model 350 that models the relationship between biomass values ​​and machine speed in biomass map 335, and use the biomass map 335 to generate a functional predicted speed map 360. Speed ​​map generator 352 can use a prediction model 350 that models the relationship between crop state values ​​and machine speed in crop state map 337, and use the crop state map 337 to generate a functional predicted speed map 360. Speed ​​map generator 352 can use a prediction model 350 that models the relationship between topography values ​​and machine speed in topography map 339, and use the topography map 332 to generate a functional predicted speed map 360.Speed ​​map generator 352 can use a prediction model 350 that models the relationship between soil property values ​​and machine speed in soil property map 341, and use soil property map 341 to generate a functional predicted speed map 360. Speed ​​map generator 352 can use a prediction model 350 that models the relationship between sowing characteristic values ​​and machine speed in sowing map 343, and use sowing map 343 to generate a functional predicted speed map 360. Speed ​​map generator 352 can use another prediction model 350 that models the relationship between other characteristic values ​​and machine speed in other maps 353, and use other maps 353 to generate a functional predicted speed map 360.

[0099] Therefore, as agricultural harvesters move through fields to perform agricultural operations, one or more functional speed prediction maps 360 are generated while the agricultural operations are in progress.

[0100] At block 394, the prediction map generator 212 outputs a functional predicted speed map 360. At block 391, the functional predicted speed map generator 212 outputs a functional predicted speed map 360 for presentation to operator 260 and for possible interaction by operator 260. At block 393, the prediction map generator 212 can configure map 360 for use by control system 214. At block 395, the prediction map generator 212 can also provide map 360 to control area generator 213 to generate a control area. At block 397, the prediction map generator 212 also configures map 360 in other ways. The functional predicted speed map 360 (with or without a control area) is provided to control system 214. At block 396, control system 214 generates control signals based on the functional predicted speed map 360 to control controllable subsystem 216. Control system 214 can control propulsion subsystem 250 or other subsystems 399.

[0101] Therefore, it can be seen that this system employs an information map mapping agricultural characteristics, such as vegetation indices, crop status, sowing characteristics, soil properties, biomass, predicted yield, topography, or information from one or more previous operations to different locations in the field. The system also uses one or more field sensors to sense indicative characteristics, field sensor data indicating machine speed, and generates a model that models the relationship between the characteristics sensed using the field sensors or related characteristics 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 to be presented to local or remote operators or other users, or both. For example, the control system can use the map to control one or more systems of a combine harvester.

[0102] Processors and servers have been mentioned in this discussion. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately). Processors and servers are functional components of the system or apparatus to which they belong, and are activated by the function of other components or objects in these systems, and facilitate the function of those other components or objects.

[0103] Furthermore, numerous user interface displays have been discussed. These displays can take many different forms and can have various user-activated interface mechanisms mounted thereon. For example, user-activated interface mechanisms may include text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-activated interface mechanisms can also be actuated in various ways. For example, user-activated interface mechanisms can be actuated using interface mechanisms such as click devices (e.g., trackballs or mice, hardware buttons, switches, joysticks or keyboards, thumb switches or thumb pads, etc.), virtual keyboards, or other virtual actuators. Additionally, if the screen displaying the user-activated interface mechanism is a touch-sensitive screen, the user-activated interface mechanism can be activated using touch gestures. Furthermore, user-activated interface mechanisms can be activated using voice commands utilizing speech recognition functionality. Speech recognition can be implemented using a speech detection device, such as a microphone, and the software is used to recognize the detected speech and execute commands based on the received speech.

[0104] Many data storage devices have already been discussed. It will be noted that each data storage device can be divided into multiple data storage devices. In some examples, one or more data storage devices may be local to the system accessing that data storage device; one or more data storage devices may be entirely located remotely from the system utilizing that data storage device; or one or more data storage devices may be local while the others are remote. This disclosure takes all these configurations into account.

[0105] Similarly, the accompanying drawings show a number of boxes, each assigned a function. Note that fewer boxes can be used to illustrate a function belonging to multiple different boxes, performed by fewer components. More boxes can also be used to illustrate a function that can be distributed among more components. In different examples, some functions can be added, and some can be removed.

[0106] Note that the foregoing discussion has described various different systems, components, logics, and interactions. It will be understood that any or all of such systems, components, logics, or interactions can be implemented by hardware objects that perform functions associated with those systems, components, logics, or interactions, such as processors, memory, or other processing units, some of which are described below. Additionally, one or all of the people in the systems, components, logics, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing unit, as described below. One or all of the people in the systems, components, logics, 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 people in the systems, components, logics, and interactions described above. Other structures may also be used.

[0107] Figure 6 This is a block diagram of an agricultural harvester 600, which can be similar to... Figure 2 The agricultural harvester 100 is shown in the diagram. The agricultural harvester 600 communicates with components in a 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 via a web browser or any other computing component. Figure 2 The software or components shown herein, along with their associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at remote data center locations, or computing resources can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through shared data centers, even if the service appears as a single access point for a user. Therefore, a remote server architecture can be used to provide the components and functions described herein from remote servers located at remote locations. Alternatively, the components and functions can be provided from servers, or they can be installed directly or otherwise onto client devices.

[0108] exist Figure 6 In the example shown, some objects are similar to Figure 2 The objects shown are similarly numbered. Figure 6 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 far from the agricultural harvester 600. Therefore, in Figure 6In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0109] Figure 6 Another example of a remote server architecture is also described. Figure 6 It is shown that, Figure 2 Some components can be located at a remote server location 502, while others can be located elsewhere. For example, data storage 202 can be located at a location separate from location 502 and accessed via a remote server at location 502. Regardless of their location, the components can be directly accessed by the agricultural harvester 600 via a network (such as a wide area network or local area network); the components can be hosted by a server at a remote location; or the components can be provided as a server or accessed by a connection server located at a remote location. Furthermore, data can be stored at any location, and the stored data can be accessed or forwarded to an operator, user, or system. For example, a physical carrier can be used instead of an electromagnetic carrier, or a physical carrier can be used in addition to an electromagnetic carrier. In some examples, where wireless telecommunications service coverage overlaps or is absent, 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 a combine harvester 600 approaches a machine containing an information collection system (such as a refueling truck before refueling), the information collection system uses any type of ad-hoc wireless connection to collect information from the combine harvester 600. The collected information can then be forwarded to another network when the machine containing the received information reaches a location with wireless telecommunications coverage or other available wireless coverage. For example, a refueling truck may enter an area with wireless communication coverage while traveling to a location to refuel other machines or while at a primary fuel storage location. All these architectures are considered herein. Additionally, information can be stored on the combine harvester 600 until it enters an area with wireless communication coverage. The combine harvester 600 itself can also transmit information to another network.

[0110] Also note that Figure 2 The components or multiple parts thereof can be mounted on a wide variety of different devices. One or more of these devices may include an onboard computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile device, such as a handheld computer, mobile phone, smartphone, multimedia player, personal digital assistant, etc.

[0111] In some examples, the remote server architecture 500 may include network security measures. These measures, without limitation, may include data encryption on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of a ledger to record metadata, data, data transfer, data access, and data transformation. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0112] Figure 7 This is a simplified block diagram of an illustrative embodiment of a handheld or mobile computing device 16 that can be used as a user or client, where the 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 8 to 9 Examples are handheld or mobile devices.

[0113] Figure 7 A general block diagram of the components of client device 16 is provided, which can operate... Figure 2 Some of the components shown in the image are related to... Figure 2 Some components shown interact, or both. In device 16, a communication link 13 is provided, which allows the handheld device to communicate with other computing devices and, in some examples, automatically provides a channel or pathway for receiving information, such as through scanning. Examples of communication link 13 include those allowing communication via one or more communication protocols, such as protocols for providing cellular wireless services to access the network and protocols for providing local wireless connectivity to the network.

[0114] In other examples, the application can be received on a removable secure digital (SD) card connected to interface 15. Interface 15 and communication link 13 communicate along bus 19 with processor 17 (which may also be represented as a processor or server according to other figures), bus 19 being also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and positioning system 27.

[0115] In one example, I / O components 23 are 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, touchscreens, proximity sensors, accelerometers, and orientation sensors, and output components such as displays, speakers, and / or printer ports. Other I / O components 23 may also be used.

[0116] Clock 25 exemplarily includes a real-time clock component that outputs time and date. Clock 25 may also exemplarily provide timing functionality to processor 17.

[0117] Positioning system 27 exemplarily includes components that output 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 mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0118] 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 storage 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. Processor 17 may also be activated by other components to enable its functionality.

[0119] Figure 8 The illustration shows an example where device 16 is a tablet computer 600. Figure 8 In the diagram, computer 600 is shown with a user interface display screen 602. Screen 602 may be a touchscreen or a pen-activated interface that receives input from a pen or stylus. Tablet computer 600 may also use an on-screen virtual keyboard. Alternatively, computer 600 may be attached to a keyboard or other user input device via a suitable attachment mechanism, such as a wireless link or USB port. Computer 600 may also, by way of example, receive voice input.

[0120] Figure 9 Similar to Figure 8 The device in question is a smartphone 71. The smartphone 71 has a touch-sensitive display 73 that displays icons or tiles or other user input mechanisms 75. Users can use the mechanisms 75 to run applications, make calls, perform data transfers, etc. Typically, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than feature phones.

[0121] Note that other forms of electricity are possible for device 16.

[0122] Figure 10 It is one of the deployable ones Figure 2 An example of a computing environment for the components. (See also...) Figure 10An example system for implementing some embodiments includes a computing device, in the form of a computer 810, programmed to operate as discussed above. Components of computer 810 may include, but are not limited to, a processing unit 820 (which may include a processor or server according to previous figures), system memory 830, and a system bus 821 that couples various system components (including system memory) to the processing unit 820. System bus 821 may be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 10 In the corresponding part.

[0123] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available medium accessible to 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 are distinct from and do not include modulated data signals or carriers, and do not include modulated data signals or carriers. Computer-readable media include hardware storage media, which includes 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 include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disc storage, cassette tape, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to computer 810. Communication media can contain computer-readable instructions, data structures, program modules or other data in a transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal having one or more characteristics that are set or altered to encode information in the signal.

[0124] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input / output system 833 (BIOS) (containing basic routines) is typically stored in ROM 831, which facilitates (e.g., during startup) the transfer of information between components within computer 810. RAM 832 typically contains data and / or program modules, or both, that are readily accessible and / or currently being processed on or operated by unit 820. This is by way of example, not limitation. Figure 10 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0125] The computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is by way of example only. Figure 10 A hard disk drive 841, an optical disk drive 855, and a non-volatile optical disk 856 are shown for reading from or writing to non-removable non-volatile media. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable disk storage interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable storage interface (such as interface 850).

[0126] Alternatively, or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), application-specific standard products (e.g., ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and the like.

[0127] The above discussion and Figure 10 The driver and its associated computer storage media shown provide storage for computer-readable instructions, data structures, program modules, and other data for the computer 810. Figure 10 For example, hard disk drive 841 is shown storing operating system 844, application program 845, other program modules 846, and program data 847. It should be noted that these components may be the same as or different from operating system 834, application program 835, other program modules 836, and program data 837.

[0128] Users can input commands and information into computer 810 using input devices such as keyboard 862, microphone 863, and clicking or pointing devices 861, such as mouse, trackball, or touchpad. Other input devices (not shown) may include joysticks, gamepads, satellite dish antennas, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860 (coupled to the system bus), but may be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface (such as video interface 890). In addition to a monitor, the computer may include other peripheral output devices, such as speakers 897 and printer 896, which may be connected via peripheral output interface 895.

[0129] Computer 810 operates in a network environment using logical connections (such as Controller Area Network (CAN), Local Area Network (LAN), or Wide Area Network (WAN)) to one or more remote computers (such as remote computer 880).

[0130] When used in a LAN network environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN network environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a network environment, program modules may be stored in remote storage devices. For example, Figure 10 This demonstrates that remote application 885 can remain on remote computer 880.

[0131] It should also be noted that the different examples described in this article can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of these cases are considered in this article.

[0132] Example 1 is an agricultural operating machine, comprising:

[0133] A communication system that receives an information map, the information map including values ​​of a first agricultural characteristic corresponding to different geographical locations in the field;

[0134] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0135] A field sensor that detects a value of a second agricultural characteristic corresponding to the geographical location;

[0136] A predictive model generator generates a predictive agricultural model based on the value of a first agricultural characteristic in the infographic at the geographic location and the value of a second agricultural characteristic detected by the field sensors at the geographic location, the predictive agricultural model modeling the relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0137] A prediction map generator generates a functional predicted machine speed map of the field based on the value of the first agricultural characteristic in the information map and based on the predicted agriculture model. The functional predicted machine speed map maps the predicted machine speed values ​​to different geographical locations in the field, and the predicted machine speed values ​​indicate the predicted speed of agricultural harvesters.

[0138] Example 2 is an agricultural operating machine according to any or all of the previous examples, wherein the prediction map generator configures the functional prediction machine speed map for use by a control system, which generates control signals based on the predicted machine speed values ​​on the functional prediction machine speed map to control subsystems on the agricultural operating machine.

[0139] Example 3 is an agricultural harvester according to any or all of the foregoing examples, wherein the field sensors on the agricultural harvester are configured to detect a value of a speed characteristic as a value of the second agricultural characteristic, the value of which indicates the speed of the agricultural harvester corresponding to the geographical location.

[0140] Example 4 is an agricultural harvesting machine according to any or all of the foregoing examples, the agricultural harvesting machine further comprising a feed rate controller configured to generate a feed rate control signal to control a controllable subsystem of the agricultural harvester based on a target feed rate of material through the agricultural harvester, and wherein the field sensors include:

[0141] Sensors configured to generate sensor signals that indicate the output of the feed rate controller; and

[0142] A processing system that receives the sensor signals and generates processed data based on the sensor signals to indicate the machine speed of the agricultural harvester.

[0143] Example 5 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic includes a vegetation index map of vegetation index (VI) values ​​corresponding to different geographical locations in the field, and wherein the prediction model generator includes:

[0144] A VI-to-velocity model generator generates a predictive velocity model based on the VI value at the geographic location in the VI map and the velocity characteristic detected by the field sensor at the geographic location. The predictive velocity model models the relationship between the VI value and the velocity characteristic.

[0145] Example 6 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic includes a biomass map of biomass values ​​corresponding to different geographical locations in the field, and wherein the predictive model generator includes:

[0146] A biomass-velocity model generator generates a predictive velocity model based on the biomass value at the geographic location in the biomass map and the velocity characteristic detected by the field sensor at the geographic location. The predictive velocity model models the relationship between the biomass value and the velocity characteristic.

[0147] Example 7 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic comprises a topographic map of topographic values ​​corresponding to different geographic locations in the field, and wherein the prediction model generator comprises:

[0148] A terrain-velocity model generator generates a predicted velocity model based on terrain values ​​at the geographic location in the terrain map and velocity characteristics detected by the field sensors at the geographic location. The predicted velocity model models the relationship between the terrain values ​​and the velocity characteristics.

[0149] Example 8 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic includes a predicted yield map corresponding to predicted yield values ​​in different geographical locations in the field, and wherein the prediction model generator includes:

[0150] A production-to-velocity model generator generates a predicted velocity model based on the predicted production value at the geographical location in the predicted production map and the velocity characteristic detected by the field sensor at the geographical location. The predicted velocity model models the relationship between the predicted production value and the velocity characteristic.

[0151] Example 9 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic includes a soil property map corresponding to soil property values ​​for different geographical locations in the field, and wherein the predictive model generator includes:

[0152] A soil property versus velocity model generator generates a predicted velocity model based on soil property values ​​at the geographical location in the soil property map and velocity characteristics detected by the field sensors at the geographical location. The predicted velocity model models the relationship between the soil property values ​​and the velocity characteristics.

[0153] Example 10 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic includes a seed characteristic map corresponding to seed characteristic values ​​of different geographical locations in the field, and wherein the prediction model generator includes:

[0154] A seeding characteristic versus velocity model generator generates a predicted velocity model based on the seeding characteristic value at the geographical location in the seeding characteristic map and the velocity characteristic value detected by the field sensor at the geographical location. The predicted velocity model models the relationship between the seeding characteristic value and the velocity characteristic.

[0155] Example 11 is an agricultural operation machine according to any or all of the foregoing examples, wherein the infographic includes crop state maps corresponding to crop state values ​​in different geographical locations in the field, and wherein the prediction model generator includes:

[0156] A crop state versus velocity model generator generates a predicted velocity model based on the crop state value at the geographic location in the crop state map and the velocity characteristic detected by the field sensor at the geographic location. The predicted velocity model models the relationship between the crop state value and the velocity characteristic.

[0157] Example 12 is a computer-implemented method for generating functional predictive agricultural maps, the method comprising:

[0158] Receive information maps at agricultural machinery, the information maps indicating values ​​of a first agricultural characteristic corresponding to different geographical locations in the field;

[0159] Detect the geographical location of the agricultural machinery;

[0160] Use field sensors to detect the value of the second agricultural characteristic corresponding to the geographical location;

[0161] Generate a predictive agricultural model that models the relationship between the first agricultural characteristic and the second agricultural characteristic; and

[0162] The control prediction map generator generates a functional predictive machine speed map of the field based on the value of the first agricultural characteristic in the information map and based on the predictive agriculture model. The functional predictive machine speed map maps the predicted target machine speed value to different locations in the field.

[0163] Example 13 is a computer-implemented method according to any or all of the foregoing examples, and the method further includes:

[0164] The functional predictive machine speed map is configured for use by a propulsion controller, which generates control signals based on the functional predictive machine speed map to control the controllable propulsion subsystem on the agricultural machine.

[0165] Example 14 is a computer-implemented method according to any or all of the foregoing examples, wherein detecting the value of the second agricultural characteristic with field sensors includes detecting a velocity characteristic corresponding to the geographic location, and wherein receiving the information map includes receiving one or more of a vegetation index map, a biomass map, a crop status map, a soil property map, a predicted yield map, a topographic map, and a sowing map.

[0166] Example 15 is a computer-implemented method according to any or all of the foregoing examples, wherein receiving the information graph includes:

[0167] Receive multiple different information layers, each of which indicates one or more of the following at the geographic location: vegetation index value, biomass value, predicted yield value, crop status value, soil property value, sowing characteristic value, and topography value.

[0168] Example 16 is a computer-implemented method according to any or all of the foregoing examples, wherein receiving the information graph includes:

[0169] Receive an information map generated from previous operations performed in the field.

[0170] Example 17 is a computer-implemented method according to any or all of the foregoing examples, further comprising:

[0171] The operator interface mechanism is controlled to present the predicted machine speed map for the aforementioned functions.

[0172] Example 18 is an agricultural operating machine, comprising:

[0173] A communication system that receives an information map indicating values ​​of a first agricultural characteristic corresponding to different geographical locations in the field;

[0174] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0175] A field sensor that detects a value of a speed characteristic, the value of which indicates the speed of the agricultural machinery corresponding to the geographical location;

[0176] A predictive model generator generates a predictive agricultural model based on the value of the first agricultural characteristic in the infographic at the geographic location and the value of the velocity characteristic detected by the field sensors at the geographic location, the predictive agricultural model modeling the relationship between the first agricultural characteristic and the velocity characteristic; and

[0177] A prediction map generator generates a functional predicted velocity map of the field based on the value of the first agricultural characteristic in the information map and based on the predicted velocity model. The functional predicted velocity map maps the predicted velocity characteristic value to different locations in the field, and the predicted velocity characteristic value indicates the target machine speed.

[0178] Example 19 is an agricultural operating machine according to any or all of the foregoing examples, wherein the field sensors include:

[0179] A machine speed sensor, configured to detect the speed of the agricultural machinery.

[0180] Example 20 is an agricultural operating machine according to any or all of the foregoing examples, wherein the prediction map generator is configured to configure the functional predicted speed map for use by a control system, which controls the propulsion subsystem based on the target machine speed of the functional predicted speed map.

[0181] Although the subject matter has been described in language specific to structural features and / or methodological behavior, it will be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or behaviors described above. Rather, the specific features and behaviors described above are disclosed as exemplary forms of the claims.

Claims

1. An agricultural system comprising: A communication system (206) receives an information map (258) that includes values ​​of a first agricultural characteristic corresponding to different geographical locations in the field. A geolocation sensor (204) detects the geolocation of the agricultural machinery (100); A field sensor (208) detects a value of a second agricultural characteristic corresponding to a geographical location; A predictive model generator that generates a predictive agricultural model, the predictive agricultural model modeling the relationship between the first agricultural characteristic and the second agricultural characteristic based on the value of the first agricultural characteristic in the information map (258) corresponding to the geographical location and the value of the second agricultural characteristic detected by the field sensor (208) corresponding to the geographical location; as well as A prediction map generator generates a functional prediction machine speed map of the field, the functional prediction machine speed map being based on the value of the first agricultural characteristic in the information map (258) and mapping the prediction machine speed value to the different geographical locations in the field based on the prediction agriculture model, the prediction machine speed value indicating the predicted travel speed of the agricultural operation machine.

2. The agricultural system according to claim 1, wherein, The agricultural system also includes a control system configured to generate control signals based on the predicted machine speed values ​​in the functional predicted machine speed map to control the propulsion subsystem on the agricultural machine.

3. The agricultural system according to claim 1, wherein, The field sensors on the agricultural machinery are configured to detect values ​​of speed characteristics, which, as the second agricultural characteristic, indicate the travel speed of the agricultural machinery corresponding to the geographical location.

4. The agricultural system according to claim 3, wherein, The agricultural system further includes a rate controller configured to generate a feed rate control signal to control a controllable subsystem of the agricultural machine based on a target feed rate of material passing through the agricultural machine, and wherein the field sensors include: Sensors configured to generate sensor signals that indicate the output of the feed rate controller; and A processing system that receives and processes the sensor signals to generate the value of the speed characteristic, the value of the speed characteristic indicating the travel speed of the agricultural machine corresponding to the geographical location.

5. The agricultural system according to claim 3, wherein, The infographic includes a vegetation index map, which includes vegetation index (VI) values ​​corresponding to the different geographical locations in the field as the values ​​of the first agricultural characteristic, and wherein the predictive agriculture model includes: A velocity prediction model is used to model the relationship between the vegetation index and the velocity characteristics based on the vegetation index value corresponding to the geographical location in the vegetation index map and the velocity characteristics detected by the field sensors corresponding to the geographical location.

6. The agricultural system according to claim 3, wherein, The information map includes a biomass map, which includes biomass values ​​corresponding to the different geographical locations in the field as values ​​of the first agricultural characteristic, and wherein the predictive agricultural model includes: A predictive velocity model is used to model the relationship between biomass and velocity characteristics based on the biomass value corresponding to the geographical location in the biomass map and the velocity characteristics detected by the field sensors corresponding to the geographical location.

7. The agricultural system according to claim 3, wherein, The information map includes a topographic map, which includes values ​​of topographic features corresponding to different geographical locations in the field, as the values ​​of the first agricultural feature, and wherein the predictive agricultural model includes: A predicted velocity model, which models the relationship between the terrain characteristics and the velocity characteristics based on the values ​​of the terrain characteristics corresponding to the geographical location in the topographic map and the values ​​of the velocity characteristics corresponding to the geographical location detected by the field sensors.

8. The agricultural system according to claim 3, wherein, The infographic includes a predicted yield map, which includes predicted yield values ​​corresponding to the different geographical locations in the field, as the values ​​of the first agricultural characteristic, and wherein the predicted agricultural model includes: A predictive velocity model is used to model the relationship between output and velocity characteristics based on the predicted output value corresponding to the geographical location in the predicted output map and the velocity characteristics detected by the field sensors corresponding to the geographical location.

9. A computer-based method for generating functional predictive agricultural maps, the method comprising: Receive information map (258), the information map (258) indicating the value of the first agricultural characteristic corresponding to different geographical locations in the field; Detect the geographical location of agricultural machinery (100); The value of the second agricultural characteristic corresponding to the geographical location is detected using a field sensor (208); Generate a predictive agricultural model that models the relationship between the first agricultural characteristic and the second agricultural characteristic; as well as The control prediction map generator (212) generates a functional prediction machine speed map of the field based on the value of the first agricultural characteristic in the information map (258) and based on the prediction agriculture model, as the functional prediction agriculture map, which maps the predicted machine speed value to different geographical locations in the field.

10. An agricultural system comprising: A communication system (206) receives an information map (258) that indicates agricultural characteristics corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of agricultural machinery; A field sensor (208) detects a speed characteristic value, which indicates the travel speed of the agricultural machine corresponding to the geographical location; A prediction model generator that generates a prediction speed model, the prediction speed model modeling the relationship between the agricultural characteristics and the speed characteristics based on the values ​​of the agricultural characteristics in the information map (258) corresponding to the geographical location and the speed characteristic values ​​of the speed characteristics detected by the field sensor (208) corresponding to the geographical location; as well as A prediction map generator generates a functional prediction speed map of the field, which maps the predicted speed characteristic values ​​to different geographical locations in the field based on the values ​​of the agricultural characteristics in the information map (258) and based on the prediction speed model, the predicted speed characteristic values ​​indicating the predicted travel speed of the agricultural machinery.

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