Predictive biomass map generation and control

Abstract

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

Description

Technical Field

[0001] This manual covers agricultural machinery, forestry machinery, construction machinery, and lawn 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] In a common arrangement, the harvester header extends forward from the harvester to engage plant stems, cut the stems, and transport the cut crop into the harvester's own body for processing. In a harvester, throughput (the amount of material moving through the machine) can be varied based on several factors, including different machine settings such as the harvester's speed along the ground and the biomass of the vegetation encountered by the harvester. Assuming a throughput, several machine settings can then be set to efficiently process the crop, and the machine speed can then be adjusted to maintain the desired throughput as the operator observes differences in vegetation biomass.

[0004] The above discussion is provided only as general background information 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. As the agricultural machinery moves across the field, field sensors on the machinery sense the agricultural characteristics. A prediction map generator generates prediction maps of predicted 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. The prediction maps can be output and used for automated machine control. This summary is provided to introduce selected concepts in a simplified form, which are further described in the detailed description below. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter. The claimed subject matter is not limited to examples addressing any or all of the disadvantages pointed out in the background art. Attached Figure Description

[0006] Figure 1 This is a partial schematic diagram of an example of a combine harvester.

[0007] Figure 2 This is a block diagram showing some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0008] Figures 3A to 3B A flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram is shown.

[0009] Figure 4 This is a block diagram illustrating an example of a prediction model generator and a prediction metric graph generator.

[0010] Figure 5 This is a flowchart illustrating an example of how an agricultural harvester receives vegetation index maps, detects characteristics, and generates functional predictive biomass maps for use in controlling the harvester during harvesting operations.

[0011] Figure 6 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0012] Figures 7 to 9 An example of a mobile device that can be used in agricultural harvesters is shown.

[0013] Figure 10 This is a block diagram illustrating an example of a computing environment that can be used in agricultural harvesters and the architecture illustrated in the foregoing figures. Detailed Implementation

[0014] To facilitate understanding of the principles of this disclosure, reference will now be made to the examples shown in the accompanying drawings, and they will be described using specific language. However, it will be understood that this is not intended to limit the scope of this disclosure. Any changes and further modifications to the described apparatus, systems, and methods, as well as any further application of the principles of this disclosure, are fully contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. Specifically, it is fully contemplated that features, components, and / or steps described with respect to one example may be combined with features, components, and / or steps described with respect to other examples of this disclosure.

[0015] This specification relates to generating predictive maps, and more specifically, predictive biomass maps, using field data acquired concurrently with agricultural operations in combination with prior data. In some examples, predictive biomass maps can be used to control agricultural machinery (e.g., agricultural harvesters). Biomass, as used herein, refers to the amount of vegetation material (such as crop plants and weeds) on the ground in a given area or location. Typically, this amount is measured by weight, such as weight per given area, like tons per acre. A variety of different characteristics can indicate biomass (referred to herein as biomass characteristics) and can be used to predict biomass in a field of interest. For example, biomass characteristics can include a variety of different vegetation characteristics, such as vegetation height (e.g., the height of vegetation above the field surface, such as the height of the crop or crop canopy above the field surface), vegetation density (the amount of crop material in a given volume, which can be derived from crop mass and crop volume), vegetation mass (such as the weight of vegetation or the weight of vegetation components), or vegetation volume (how much of a given area or region or location is occupied by vegetation, i.e., the space occupied or contained by vegetation). It is important to note that vegetation characteristics can include individual characteristics of different vegetation types; for example, vegetation characteristics can be crop characteristics or weed characteristics. For instance, vegetation characteristics can include crop height, crop density, crop mass, or crop volume. Therefore, as used herein, vegetation characteristics (such as vegetation height, vegetation density, vegetation mass, or vegetation volume) can include or contain crop height, crop density, crop mass, or crop volume. In another example, biomass characteristics can include a variety of different machine characteristics of an agricultural harvester, such as machine settings, operating characteristics, or machine performance characteristics. For example, the force (such as fluid pressure or torque) used to drive the threshing drum of an agricultural harvester can be a machine characteristic indicating biomass.

[0016] When a combine harvester engages with areas of the field with varying biomass, its performance can be affected. For example, if the harvester's machine settings are based on anticipated or desired throughput, changes in biomass can cause throughput variations, and therefore the machine settings may not be optimally suited to handle vegetation, including crops. As mentioned above, the operator can attempt to predict the biomass ahead of the machine. Furthermore, some systems, such as feedback control systems, passively adjust the harvester's forward speed on the ground in an attempt to maintain the desired throughput. This can be done by attempting to identify the biomass based on sensor inputs (e.g., from sensors that sense variables indicating biomass). However, such arrangements are prone to errors and may be too slow to react to impending changes in biomass to effectively alter the machine's operation (e.g., by changing the harvester's forward speed) to control throughput. For example, such systems are often reactive, adjusting the machine settings only after the machine encounters vegetation to attempt to reduce further errors, as is the case in feedback control systems.

[0017] The vegetation index map illustratively maps vegetation index values ​​(which can indicate vegetation growth) at different geographic locations within a field of interest. An example of a vegetation index includes the normalized difference vegetation index (NDVI). Many other vegetation indices also 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 the plants. Without limitation, these electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0018] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, vegetation index maps enable crop identification and geolocation even in the presence of bare soil, crop residues, or other vegetation such as weeds. In other examples, vegetation index maps enable the detection of multiple different crop characteristics, such as crop growth and crop health or vigor, across different geographic locations in a field of interest. However, vegetation index maps may not accurately or reliably indicate other vegetation characteristics, such as those indicating vegetation biomass. Therefore, in some cases, such as when considering biomass, the usefulness of vegetation index maps in predicting how to control an agricultural harvester as it moves across the field may be reduced.

[0019] Therefore, this discussion is conducted with regard to a system that receives a vegetation index map of a field or a map generated during a previous or a priori operation, and also uses field sensors to detect variables indicating biomass during the harvesting operation. In some cases, field sensors can detect the height, density, mass, or volume of vegetation in an area or location on the field (e.g., the area in front of the header of an agricultural harvester (e.g., header 102 of agricultural harvester 100)). The detected vegetation height, density, mass, or volume can indicate vegetation biomass. For example, by knowing the height of the vegetation, such as the height of the crop or crop canopy above the field surface, vegetation biomass can be estimated. This is because there is a relationship between vegetation height and vegetation biomass; generally, the higher the height, the greater the biomass. Other vegetation characteristics (such as density, mass, or volume) are also related to biomass, such that the values ​​of vegetation characteristics can be correlated with biomass, and thus vegetation biomass can be estimated. By detecting one or more of these vegetation characteristics, a biomass level value, such as high, medium, or low biomass, can be predicted. In some examples, more limited values, such as predicted weight values, can also be predicted. Illustrated, a detected relatively high crop height (relative to the general or known height of a particular vegetation type, such as a specific crop or a specific genotype) can indicate high biomass production. In some examples, a single characteristic can be detected and used for biomass estimation. For example, given a detected vegetation height, other vegetation characteristics, such as vegetation index values, historical data, previous or a priori operational data (such as data from a seeding map, which may include genotype data, seed spacing data, seed depth data, and various other seeding characteristic data), crop genotype data (e.g., species data, hybridization data, cultivar data, etc.), operator or user input, third-party information, expert knowledge, machine learning, and various other information or combinations thereof, can be estimated. In some examples, combinations of characteristics can be detected and used for biomass estimation, such as a combination of vegetation height, vegetation density, vegetation mass, or vegetation volume.

[0020] In another example, field sensors can detect forces, such as fluid pressure or torque, used to drive the threshing drum when an agricultural harvester (e.g., harvester 100) is processing crops. For example, the force used to drive the threshing drum at a given setting (e.g., a given speed (e.g., RPM) setting) may be affected by the load on the drive system (e.g., engine assembly or hydraulic motor assembly). The force used to drive the threshing drum at a given setting under no-load conditions (no vegetation being processed) can be known. Therefore, because the biomass of the vegetation will increase the load on the drive system, the additional force used to drive the threshing drum at a given setting when the harvester is processing vegetation can indicate the biomass of the vegetation being processed.

[0021] The system generates a model that models the relationship between vegetation index values ​​on a vegetation index map, or values ​​on a map generated from previous or a priori operations, and output values ​​from field sensors. This model is used to generate a functional predictive biomass map, which predicts, for example, biomass at different locations in the field. The functional predictive biomass map generated during harvesting operations can be presented to the operator or other users and / or used to automatically control the harvester during harvesting operations.

[0022] Figure 1 This is a partial schematic diagram of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although combine harvesters are provided as examples 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 hay harvesters, slashers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the many different 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 to which predictive mapping can be applied, construction equipment, forestry equipment, and turf management equipment. Therefore, this disclosure is intended to cover these many different types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0023] like Figure 1As 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 front-end equipment, such as a header 102 and a cutter generally indicated by 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher generally indicated by 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 header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Therefore, the vertical position (cutting height) of the cutting table 102 above the ground 111 (where the cutting table 102 travels) can be controlled by actuating the actuator 107. Although Figure 1 As not shown, the agricultural harvester 100 may also include one or more actuators operable to apply a tilt angle, a tumble angle, or both to the header 102 or a portion 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.

[0024] The threshing machine 110 exemplarily includes a threshing rotor 112 and a set of concave plates 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning chamber 118 (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a waste lifter 128, a clean grain lifter 130, and an unloading screw conveyor 134 and a nozzle 136. The clean grain lifter moves clean grain into a clean grain bin 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a shredder 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem, which includes an engine driving a ground engagement assembly 144 (e.g., wheels or tracks). In some examples, the combine harvester within the scope of this disclosure may have more than one of any of the above subsystems. In some examples, the agricultural harvester 100 may have Figure 1 The left and right grain cleaning subsystems and separators are not shown in the diagram.

[0025] In operation, as an overview, 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 gathers 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. Operator commands are commands issued by the operator. 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 the control system (described in more detail below) that controls the actuator 107. The control system can also receive settings from the operator for establishing the tilt angle and tumble angle of the header 102 and implements the input settings by controlling the associated actuators (not shown) to change the tilt angle and tumble angle of the header 102. Actuator 107 maintains the header 102 at a height above ground 111 based on a height setting, and, where applicable, at a desired tilt and yaw angle. Each of the height setting, tumble setting, and tilt setting can be implemented independently of the others. The control system responds to header errors (e.g., the difference between the height setting and the measured height of the header 104 above ground 111, and in some cases, tilt and tumble angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher level, the control system responds to smaller header position errors and attempts to reduce the detected error faster than when the sensitivity level is lower.

[0026] 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 threshing mill 110. The crop is threshed by causing the crop material to abut against a rotating drum 112 of a concave plate 114. Forces used to drive the drum 112 (or to power the drum) can be sensed, and the sensed forces or indications of the sensed forces can be used to determine the biomass being threshed. For example, fluid pressure (e.g., hydraulic or pneumatic pressure) used to drive the drum 112 can be sensed, and the sensed fluid pressure can be used to determine the biomass being processed by the agricultural harvester 100. In another example, torque used to drive the drum 112 can be sensed, and the sensed torque can be used to determine the biomass being processed by the agricultural harvester 100. The threshing drum drive force can be used as an indicator of the biomass being processed by the threshing machine in the agricultural harvester 100, because the threshing drum drive force is the force, such as torque or pressure, used to maintain the threshing drum 112 at a desired speed. The threshing drum drive force (along with various other machine settings, such as concave settings and threshing drum speed settings) is related to the biomass moving through the threshing machine in the agricultural harvester 100 at a given time. In some cases, the threshing drum 112 may be driven (or powered by) other power systems, and the power from these other power systems used to operate the threshing drum can be sensed and used as an indicator of the biomass being processed by the threshing machine in the agricultural harvester 100.

[0027] In separator 116, the separator rotor moves the threshed crop, while discharge agitator 126 moves a portion of the residue toward residue subsystem 138. That portion of residue delivered to residue subsystem 138 is shredded by residue shredder 140 and spread across the field by spreader 142. In other configurations, the residue is discharged in piles from agricultural harvester 100. In other examples, residue subsystem 138 may include a seed ejector (not shown), such as a seed bagger or other seed collector, or a seed shredder or other seed crusher.

[0028] The grain falls into the grain cleaning subsystem 118. A husk sieve 122 separates larger pieces of grain, while a screen 124 separates smaller pieces from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet of a clean grain elevator 130, which then moves the clean grain upwards, causing it to settle in a clean grain bin 132. Airflow generated by a cleaning fan 120 removes residue from the grain cleaning subsystem 118. The cleaning fan 120 directs air upwards along an airflow path through the screen and husk sieve. The airflow then transports the residue backwards within the agricultural harvester 100 toward the residue handling subsystem 138.

[0029] The waste elevator 128 returns the waste to the threshing machine 110, where it is re-threshed. Alternatively, the waste may also be conveyed by the waste elevator or another conveying device to a separate re-threshing mechanism, where it is also re-threshed.

[0030] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-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.

[0031] Ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Ground speed sensor 146 can sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components (e.g., wheels or tracks), drive shafts, axles, or other components. In some cases, a positioning system can be used to sense the travel speed, such as a Global Positioning System (GPS), dead reckoning system, LoRAN (Local Remote Navigation System), or a variety of other systems or sensors that provide an indication of travel speed.

[0032] Loss sensor 152 exemplarily provides an output signal indicating the amount of grain loss occurring on both the right and left sides of the grain cleaning subsystem 118. In some examples, sensor 152 is an impact sensor that counts grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The impact sensors on the right and left sides of the grain cleaning subsystem 118 can provide separate signals or combined or aggregated signals. In some examples, instead of providing separate sensors for each grain cleaning subsystem 118, sensor 152 may include a single sensor.

[0033] Separator loss sensor 148 provides indication of the left and right separators ( Figure 1(Not shown separately) 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, various types of sensors may also be used to sense grain loss in the separator.

[0034] 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 oscillations or vibrations (such as oscillation frequency and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, pile it, etc.; a cleaning chamber fan speed sensor that senses the speed of the fan 120; a concave plate gap sensor that senses the size of the gap between the rotor 112 and the concave plate 114; a threshing drum speed sensor that senses the drum speed of the drum 112; and a force sensor that senses the force used to drive the threshing drum 112, such as sensing the force used to drive the threshing drum. The harvester 100 includes: a pressure sensor for the fluid pressure of 112, or a torque sensor for sensing the torque used to drive the threshing drum 112; a husk screen gap sensor for sensing the opening size in the husk screen 122; a screen mesh gap sensor for sensing the opening size in the screen mesh 124; a material other than grain (MOG) moisture sensor for sensing 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 for sensing the orientation of the harvester 100; and a crop property sensor for sensing various types of crop properties, such as crop height, crop density, crop volume, crop weight, and other crop properties. While the harvester 100 is processing crop material, the crop property sensor can also be configured to sense the characteristics of the cut crop material. For example, in some cases, crop property sensors can sense: grain quality, such as broken grain and MOG levels; grain composition, such as starch and protein; and the grain feed rate as the grain passes through feeder housing 106, clean grain elevator 130, or elsewhere in the harvester 100. Crop property sensors can also sense the feed rate of biomass through feeder housing 106, separator 116, or elsewhere in the harvester 100. Crop property sensors can also sense the feed rate as the mass flow rate of grain through elevator 130 or other parts of the harvester 100, or provide additional output signals indicating other sensed variables. Crop property sensors may include one or more yield sensors that sense the yield of the crop being harvested by the harvester.

[0035] In one example, multiple different machine settings can be set or controlled to achieve desired performance. Machine settings may include, for example, concave clearance, threshing drum speed, screen and chaff screen settings, and cleaning fan speed. Other machine settings may also be controlled. Illustratively, these machine settings can be set or controlled based on the expected throughput (i.e., the amount of material processed by the harvester 100 per unit time). Thus, if biomass varies spatially in the field and the ground speed of the harvester 100 remains constant, the throughput will vary with the biomass. In some examples, the amount of biomass being processed is indicated by sensing the force used to drive the threshing drum 112 at the desired speed, and the ground speed of the harvester 100 is varied in an attempt to maintain the desired throughput. In other examples, the forward-view image capture mechanism 151 can be used to estimate one or more of the vegetation height, vegetation density, vegetation volume, and vegetation mass in a given area of ​​the field in front of the harvester 100 to predict the amount of biomass to be processed by the harvester 100. Other vegetation characteristics can also be estimated using images (one or more) captured from the forward-view image capture unit 151. In such an example, vegetation characteristics can be converted into georegulated biomass values, which indicate the biomass that will be engaged by the agricultural harvester 100 in an upcoming area of ​​the field. Machine speed and various other machine settings (e.g., header height) can be controlled based on the estimated biomass to maintain the desired throughput.

[0036] Before describing how the agricultural harvester 100 generates a functional predictive biomass map and uses that map for control, a brief description of some items on the agricultural harvester 100 and their corresponding operations will be provided first. Figure 2 , Figure 3A and Figure 3BThe plot describes receiving a general type of prior information map and combining information from the prior information map with georegistered sensor signals generated by field sensors, where the sensor signals indicate characteristics of the field, such as characteristics of crops present in the field. Field characteristics may include (but are not limited to): field characteristics such as slope, weed density, weed type, soil moisture, and surface quality; vegetation characteristics such as vegetation height, vegetation volume, vegetation moisture, vegetation mass, and vegetation density; crop characteristics such as crop height, crop volume, crop moisture, crop mass, crop density, and crop state; grain characteristics such as grain moisture, grain size, and grain test weight; and machine performance characteristics such as loss level, working quality, fuel consumption, and power utilization. Relationships between characteristic values ​​obtained from field sensor signals and prior information map 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 those values ​​can be used to control machines, such as controlling one or more subsystems of an agricultural harvester. In some cases, functional prediction maps may be presented to users, such as operators of agricultural machinery (e.g., combine 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 actions. In some cases, functional prediction maps may be used to control agricultural machinery (e.g., combine harvesters), presented to operators or other users, or presented to operators or users to facilitate operator or user interaction, among one or more of these methods.

[0037] In reference Figure 2 , Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and Figure 5 More specific methods are described for generating functional predictive biomass maps that can be presented to an operator or user or used to control an agricultural harvester 100 or both. Similarly, although this discussion is directed toward agricultural harvesters (specifically, combine harvesters), the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0038] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2The agricultural harvester 100, as illustrated, includes one or more processors or servers 201, a data storage device 202, a geolocation sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of the field simultaneously with the harvesting operation. Agricultural characteristics may include any characteristics that can affect the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, 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 (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 airborne sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. Predictive model generator 210 exemplarily includes a prior information variable-to-field variable model generator 228, and may include other items 230. Control system 214 includes a communication system controller 229, an operator interface controller 231, a setting controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor controller 240, a cover plate position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and may include other items 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 a variety of other subsystems 256.

[0039] Figure 2 The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, for example, the priori information map 258 includes a vegetation index map or a vegetation map from a prior or previous operation. However, the priori information map 258 may also encompass other types of data obtained prior to the harvesting operation or maps from a prior or previous operation. Figure 2The diagram also shows an operator 260 capable of operating an agricultural harvester 100. The operator 260 interacts with an operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a joystick, joystick, steering wheel, linkage, pedals, buttons, dials, keypad, user-actuable elements on a user interface display (e.g., icons, buttons, etc.), microphone and speaker (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, the operator 260 may interact with the operator interface mechanism 218 using touch gestures. The examples provided above are exemplary and not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of this disclosure.

[0040] Using communication system 206 or other methods, prior information map 258 can be downloaded to agricultural harvester 100 and stored in data storage device 202. In some examples, communication system 206 may be a cellular communication system, a system communicating via a wide area network or local area network, a system communicating via a near field communication network, or a communication system configured to communicate via any or a combination of various other networks. Communication system 206 may also include a system for facilitating the download or transfer of information to and from a Secure Digital (SD) card or a Universal Serial Bus (USB) card, or both.

[0041] 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 Kinematic (RTK) component configured to enhance the accuracy of position data derived from GNSS signals. The geolocation sensor 204 may include a dead reckoning system, a cellular triangulation system, or any of a variety of other geolocation sensors.

[0042] The field sensor 208 can be referenced above. Figure 1Any sensors described. Field sensors 208 include onboard sensors 222 mounted on the agricultural harvester 100. These sensors may include, for example, perception sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems) and image sensors inside the agricultural harvester 100 (e.g., grain-cleaning cameras, or cameras mounted to identify weed seeds leaving the agricultural harvester 100 through or from the grain-cleaning subsystem). Field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the agricultural harvester or, where data is detected during harvesting operations, data acquired by any sensor.

[0043] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and values ​​mapped to the field via prior information map 258. For example, if prior information map 258 establishes a mapping between vegetation index values ​​and different locations in the field, and field sensors 208 sense values ​​indicating biomass, then prior information variable-to-field variable model generator 228 generates a predictive biomass model that models the relationship between vegetation index values ​​and biomass values, thereby allowing the prediction of biomass values ​​at a location in the field based on a vegetation index value corresponding to that location. This is because biomass at any given location in the field may be influenced by or related to characteristics indicated by the vegetation index values ​​contained in prior information map 258 (such as crop growth or crop health associated with the corresponding location in the field). Predictive biomass models can also be generated based on vegetation index values ​​from prior information map 258 and multiple field data values ​​generated by field sensors 208. Predictive map generator 212 uses the predictive biomass model generated by predictive model generator 210 to generate a functional predictive biomass map that predicts values ​​for biomass or biomass characteristics (e.g., vegetation height, vegetation density, vegetation volume, or vegetation characteristics of a specific type of vegetation (e.g., crop), such as crop height, crop density, crop volume, and crop mass). In other examples, biomass characteristics may be the force used to drive the threshing drum. The predicted biomass or biomass characteristic values ​​are generated using values ​​sensed by field sensors 208 at different locations in the field (which may be sensed values ​​of biomass or biomass characteristics) and values ​​of characteristics mapped in prior information map 258 corresponding to those locations in the field (e.g., vegetation index values).

[0044] In some examples, the type of values ​​in functional prediction graph 263 may be the same as the type of field data sensed by field sensor 208. In some cases, the type of values ​​in functional prediction graph 263 may have a different unit than the data sensed by field sensor 208. In some examples, the type of values ​​in functional prediction graph 263 may be different from the type of data sensed by field sensor 208, but related to the type of data sensed by field sensor 208. For example, in some examples, the type of data sensed by field sensor 208 may indicate the type of values ​​in functional prediction graph 263. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in prior information graph 258. In some cases, the type of data in functional prediction graph 263 may have a different unit than the data in prior information graph 258. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in prior information graph 258, but related to the type of data in prior information graph 258. For example, in some examples, the data type in the prior information graph 258 may indicate the type of data in the functional prediction graph 263. In some examples, the type of data in the functional prediction graph 263 differs from one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one of the field data type sensed by the field sensor 208 or the data type in the prior information graph 258, and different from the other.

[0045] Continuing with the previous example, where prior information map 258 is a vegetation index map and field sensor 208 senses values ​​indicating biomass, prediction map generator 212 uses the vegetation index values ​​in prior information map 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 predicting biomass at different locations in the field. Prediction map generator 212 then outputs prediction map 264.

[0046] like Figure 2As shown, prediction map 264 is based on prior information values ​​at those locations in prior information map 258 and uses a prediction model to predict the 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 biomass, then given vegetation index values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting the values ​​of biomass at different locations across the field. Prediction map 264 is generated using vegetation index values ​​at those locations obtained from the vegetation index map and the relationship between vegetation index values ​​and biomass obtained from the prediction model.

[0047] The following will describe some changes to the data types mapped in prior information graph 258, the data types sensed by field sensor 208, and the data types predicted in prediction graph 264.

[0048] In some examples, the data type in the prior infographic 258 differs from the data type sensed by the field sensor 208, while the data type in the prediction infographic 264 is the same as that sensed by the field sensor 208. For example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be vegetation height. The prediction infographic 264 could then be a predicted vegetation height map mapping the predicted vegetation height values ​​to different geographic locations within the field. In another example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be vegetation density. Therefore, the prediction infographic 264 could be a predicted vegetation density map mapping the predicted vegetation density values ​​to different geographic locations within the field.

[0049] Furthermore, in some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, and the data type in the prediction map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 may be a vegetation index map, and the variable sensed by the field sensor 208 may be crop height. In such an example, the prediction map 264 may be a predicted biomass map mapping predicted biomass values ​​to different geographic locations in the field. In another example, the prior information map 258 may be a vegetation index map, and the variable sensed by the field sensor 208 may be the threshing drum driving force. In such an example, the prediction map 264 may be a predicted biomass map mapping predicted biomass values ​​to different geographic locations in the field.

[0050] In some examples, the prior information map 258 is derived from prior or previous traversals of the field during a prior or previous operation, and the data type differs from that sensed by the field sensor 208, while the data type in the prediction map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a seed population map generated during planting, and the variable sensed by the field sensor 208 could be stem size. Thus, the prediction map 264 could be a predicted stem size map mapping predicted stem size values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a seed mix map, and the variable sensed by the field sensor 208 could be crop state, such as upright or lodged crops. Thus, the prediction map 264 could be a predicted crop state map mapping predicted crop state values ​​to different geographic locations in the field.

[0051] In some examples, the prior information map 258 is derived from prior or previous traversals of the field during a prior or previous operation, and the data type is the same as that sensed by the field sensor 208. Similarly, the data type in the prediction map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a yield map generated in the previous year, and the variable sensed by the field sensor 208 could be yield. Therefore, the prediction map 264 could be a predicted yield map that maps predicted yield values ​​to different geographic locations within the field. In this example, the prediction model generator 210 can use the relative yield differences from the georeferenced prior information map 258 from the previous year to generate a prediction model that models the relationship between the relative yield differences on the prior information map 258 and the yield values ​​sensed by the field sensor 208 during the current harvest operation. The prediction map generator 210 then uses the prediction model to generate the predicted yield map.

[0052] In another example, the prior information map 258 could be a weed density map generated during a previous or prior operation, such as from a sprayer, and the variable sensed by the field sensor 208 could be weed density. Thus, the prediction map 264 could be a predicted weed density map mapping the predicted weed density values ​​to different geographic locations in the field. In such an example, the weed density map at spraying time is recorded in a georegistration manner and provided to the agricultural harvester 100 as the prior information map 258 for weed density. The field sensor 208 can detect the weed density at geographic locations in the field, and then the prediction model generator 210 can build a prediction model that models the relationship between the weed density at harvest and the weed density at spraying time. This is because the sprayer affects weed density at spraying time, but weeds may still regrow in similar areas at harvest time. However, the weed area at harvest time may have different densities based on factors such as harvest time, weather, and weed type.

[0053] 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 adjacent portions of the region in prediction map 264. A control zone may include two or more consecutive portions of a region (e.g., a field), for which control parameters corresponding to the control zone for controlling the controllable subsystem are constant. For example, changing the response time of the controllable subsystem 216 settings may not satisfactorily respond to changes in values ​​contained in a map such as prediction map 264. In this case, control zone generator 213 parses the map and identifies control zones with defined dimensions adapted to the response time of the 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 individual controllable subsystems 216 or 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 may be similar to the predicted map 264. Thus, as described herein, the functional predicted map 263 may or may not include control areas. Both predicted map 264 and predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include control areas (e.g., predicted map 264). In another example, the functional predicted map 263 does include control areas (e.g., predicted control area map 265). In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops and then generate the predicted map 264 and the predicted control area map 265 accordingly.

[0054] 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 map 265 or to a separate map displaying only 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 for controlling or calibrating 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.

[0055] Predictive map 264 or predictive control area map 265, or both, are provided to control system 214, which generates control signals based on predictive map 264 or predictive control area map 265, or both. In some examples, communication system controller 229 controls communication system 206 to communicate predictive map 264 or predictive control area map 265, or control signals based on predictive map 264 or predictive 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 predictive map 264, predictive control area map 265, or both, to other remote systems.

[0056] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present the predictive map 264 or the predictive control area map 265, or other information derived from or based on the predictive map 264, the predictive 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 predictive map 264 and the predictive 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 biomass displayed on the map based on the operator's observation. The setting controller 232 can generate control signals for various settings on the agricultural harvester 100 based on the predictive map 264, the predictive control area map 265, or both. For example, the setting controller 232 can generate control signals to control 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, drum settings, grain clearing fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to a belt conveyor header), grain header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. The path planning controller 234 exemplarily generates control signals to control the steering subsystem 252 to turn the harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control the propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along that route. The feed rate controller 236 can control various subsystems, such as the propulsion subsystem 250 and the machine actuator 248, to control the feed rate (throughput) based on prediction graph 264 or prediction control area graph 265, or both. For example, as the agricultural harvester 100 approaches an upcoming area in the field where the crop has a biomass value above a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant feed rate of biomass through the machine 100. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on prediction graph 264, prediction control area graph 265, or both, to control the belt conveyor belt or other belt conveyor functions.The cover 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 cover included on the harvester, and the residue system controller 244 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. For example, based on the different types of seeds and weeds passing through the machine 100, a specific type of machine cleaning operation or the frequency of performing cleaning operations can be controlled. 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.

[0057] Figure 3A and Figure 3B A flowchart is shown, illustrating an example of the operation of an agricultural harvester 100 in generating a prediction map 264 and a prediction control area map 265 based on prior information map 258.

[0058] At box 280, the agricultural harvester 100 receives a priori information map 258. Examples of priori information map 258 or receiving priori information map 258 are discussed with reference to boxes 281, 282, 284, and 286. As described above, priori 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 priori information map 258 may involve selecting one or more of a plurality of possible priori information maps available. For example, one priori information map may be a vegetation index map generated from aerial imagery. Another priori information map may be a map generated during a previous pass through the field, which may be performed by a different machine (e.g., a sprayer or other machine) performing a priori or priori operation in the field. The process of selecting one or more priori information maps may be manual, semi-automatic, or automatic. Priori information map 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, the data can be collected based on aerial images obtained in the previous year, earlier in the current growing season, or at other times.

[0059] Data used to generate the prior information map 258 can be acquired through methods other than aerial imaging. For example, the agricultural harvester 100 may be equipped with sensors such as sensing sensors (e.g., forward-view image capture mechanism 151) that determine vegetation characteristics, such as vegetation height, vegetation density, vegetation mass, or vegetation volume, during prior or previous operations. In other cases, other vegetation characteristics may be determined and used. In another example, the agricultural harvester 100 may be equipped with sensors that sense the force or indication of force used to drive the threshing drum 112 when it handles crops harvested by the agricultural harvester 100 during prior or previous operations, such as a pressure sensor that senses the fluid pressure used to drive the threshing drum 112 or a torque sensor that senses the torque used to drive the threshing drum 112. Data detected by the sensors during the previous year's harvest can be used as data for generating the prior information map 258. The sensed data can be combined with other data to generate the prior information map 258. For example, based on vegetation height, density, quality, or volume of vegetation harvested or encountered by the combine harvester 100 at different locations in the field, and based on other factors (such as vegetation type; weather conditions, such as weather conditions during vegetation growth; or soil properties, such as moisture), biomass can be predicted, such that the prior information map 258 maps the predicted biomass in the field. The communication system 206 can be used to send the data for the prior information map 258 to the combine harvester 100 and store it in the data storage device 202. The communication system 206 can also be used to otherwise provide the data for the prior information map 258 to the combine harvester 100, which is determined by… Figure 3A Box 286 in the flowchart indicates this. In some examples, prior information diagram 258 may be received by communication system 206.

[0060] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, such as vegetation characteristics (e.g., biomass or biomass properties), as indicated in box 288. Examples of field sensor 288 are discussed with reference to boxes 222, 290, and 226. As described above, field sensor 208 includes: an airborne sensor 222; a remote field sensor 224, such as a UAV-based sensor that flies once to collect field data (shown in box 290); or other types of field sensors specified by field sensor 226. In some examples, position, heading, or speed data from geolocation sensor 204 is used to georeference the data from the airborne sensor.

[0061] Predictive model generator 210 controls prior information variables to pair with field variable model generator 228 to generate a model that models the relationship between the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208, as indicated by box 292. The characteristics or data types represented by the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208 can be the same or different characteristics or data types.

[0062] The relation or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and prior information map 258 to generate a prediction map 264, which predicts the values ​​of different characteristics sensed by the field sensor 208 or related to the characteristics sensed by the field sensor 208 at different geographical locations in the harvesting field, as indicated by box 294.

[0063] It should be noted that in some examples, the prior information map 258 may include two or more different maps, or two or more different layers of a single map. Each layer may represent a data type different from that of another layer, or the layers may have the same data type acquired at different times. The individual maps in the two or more different maps, or the individual layers in the two or more different layers of a map, map different types of variables to geographical locations in the field. In this example, the predictive model generator 210 generates a predictive model that models the relationship between field data and the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensors 208 may include two or more sensors, each sensing different types of variables. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between the various types of variables mapped by the prior information map 258 and the various types of variables sensed by the field sensors 208. The prediction map generator 212 can use the prediction model and the various maps or layers in the prior information map 258 to generate a functional prediction map 263 that predicts the value of each sensed characteristic (or characteristic related to the sensed characteristic) sensed by the field sensor 208 at different locations in the harvesting field.

[0064] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be manipulated (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 boxes 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 different controllable subsystems of agricultural harvester 100, as indicated in box 296.

[0065] Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Geographically contiguous values ​​within each other's thresholds can be grouped into a control zone. This threshold can be a default threshold, or it can be set based on operator input, input from the automation system, or other criteria. The size of the zones can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as indicated in box 295. Prediction map generator 212 configures prediction map 264 for presentation to an operator or other user. Control zone generator 213 can configure prediction control zone map 265 for presentation to an operator or other user. This is indicated in box 299. When presented to an operator or other user, the presentation of prediction map 264 or prediction control area map 265, or both, may include geographic location-related predicted values ​​on prediction map 264, geographic location-related control areas on prediction control area map 265, and one or more setpoints or control parameters used based on the predicted values ​​on prediction map 264 or the areas on prediction control area map 265. In another example, the presentation may include more abstract or more detailed information. The presentation may also include a confidence level indicating the accuracy with which the predicted values ​​on prediction map 264 or the areas on prediction control area map 265 conform to measurements that can be measured by sensors on the agricultural harvester 100 as the harvester 100 moves through the field. Furthermore, where information is presented to more than one location, a verification and authorization system may be provided to implement the verification and authorization process. For example, there may be a hierarchy of individuals authorized to view and modify the map and other presented information. As an example, an onboard display device may display the map locally on the machine in approximately real-time, or the map may be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or user permission level. The user permission level can be used to determine which display markers are visible on the physical display device and which values ​​the corresponding person can change. As an example, the local operator of an agricultural harvester 100 may not be able to see the information corresponding to prediction map 264 or make any changes to the machine's operation. However, a supervisor (e.g., a supervisor at a remote location) may be able to see prediction map 264 on the display but is prevented from making any changes. A manager at a separate remote location may be able to see all elements on prediction map 264 and also be able to change prediction map 264. In some cases, prediction map 264, which can be accessed and changed by a manager at a remote location, can be used for machine control. This is an example of an achievable authorization hierarchy. Prediction map 264 or prediction control area map 265, or both, may also be configured in other ways, as indicated by box 297.

[0066] In box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Specifically, in box 300, the control system 214 detects and identifies the geolocation of the harvester 100 from the geolocation sensor 204. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading 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 various field sensors 208.

[0067] 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 timing of the generated control signals, the controllable subsystem 216 being controlled, and 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.

[0068] As an example, the generated prediction map 264, in the form of a predicted biomass map, can be used to control one or more subsystems 216. For example, the predicted biomass map may include biomass values ​​or biomass characteristics that are georeferenced to locations within a harvesting field. Biomass values ​​or biomass characteristics from the predicted biomass map can be extracted and used to control, for example, the steering subsystem 252 and the propulsion subsystem 250. By controlling the steering subsystem 252 and the propulsion subsystem 250, the feed rate of material moving through the harvester 100 can be controlled. Similarly, the header height can be controlled to draw in more or less material, and therefore the header height can also be controlled to control the feed rate of material through the harvester 100. In other examples, if the prediction map 264 maps biomass along a portion of the header 102 in front of the harvester 100 to be greater than biomass along another portion of the header 102, resulting in a difference in biomass entering one side of the header 102 compared to the other side, then control of the header 102 can be implemented. For example, the belt conveyor speed on one side of the cutter head 102 can be increased or decreased relative to the belt conveyor speed on the other side of the cutter head 102 to account for differences in biomass. Therefore, based on georegistered values ​​present in the predicted biomass map, the belt conveyor speed of the belt conveyor belt on the cutter head 102 can be controlled using the belt conveyor controller 240. The foregoing examples relating to biomass and the use of predicted biomass maps are provided by way of example only. Therefore, values ​​obtained from the predicted biomass map or other types of prediction maps can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0069] 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 field sensor data from geolocation sensor 204 and field sensor 208 (and possibly other sensors) are continuously read.

[0070] In some examples, at box 316, the agricultural harvester 100 may also detect learning trigger criteria to perform machine learning on one or more of the following: the prediction graph 264, the prediction control area graph 265, the model generated by the prediction model generator 210, the area generated by the control area generator 213, one or more control algorithms implemented by the controller in the control system 214, and other triggered learning.

[0071] Learning triggering criteria can include any of a 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, triggered learning may involve recreating the relationships used to generate a predictive model when a threshold amount of field sensor data is received from field sensor 208. In these examples, receiving 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 its harvesting operation, receiving a threshold amount of field sensor data from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Furthermore, a new predictive map 264, predictive control area map 265, or both can be regenerated using the new predictive model. Box 318 indicates detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0072] In other examples, the learning trigger criteria may be based on how much the field sensor data from field sensor 208 has changed over time or compared to previous or prior values. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a selected range, less than a defined amount, or below a threshold, then the prediction model generator 210 does not generate a new prediction model. As a result, the prediction map generator 212 does not generate a new prediction map 264 and / or prediction control area map 265. However, for example, if the change within the field sensor data is outside the selected range, greater than a defined amount, or above a threshold, then the prediction model generator 210 uses 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 box 320, changes in the field sensor data (e.g., the magnitude of the amount of data exceeding a selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the prior information map 258) can be used as triggers to induce the generation of new prediction models and prediction maps. Continuing with the example described above, the threshold, range, and limited quantity can be set to default values, set by an operator or user through a user interface, set by an automation system, or set in other ways.

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

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

[0075] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as desired by the operator. 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 manner different from that commanded by control system 214. Therefore, operator 260's manual change of settings may cause one or more of the following to occur based on the adjustments made by operator 260 (as shown in box 322): causing predictive model generator 210 to relearn the model, causing predictive graph generator 212 to regenerate graph 264, causing control area generator 213 to regenerate one or more control areas on predictive control area graph 265, and causing control system 214 to relearn its control algorithm or perform machine learning on one or more components of controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggered learning criteria.

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

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

[0078] If the harvesting operation is complete, 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 may be stored locally on the data storage device 202 or sent to a remote system using the communication system 206 for subsequent use.

[0079] It should be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving prior information graphs when generating predictive models and functional predictive graphs, 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 harvesting operations, when generating predictive models and functional predictive graphs, respectively.

[0080] Figure 4 Is 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 diagram also illustrates the information flow between the various components shown. The predictive model generator 210 receives a vegetation index map 332 as a priori information map. The predictive model generator 210 also receives a geographic location 334 or an indication of geographic location from the geographic location sensor 204. The field sensor 208 illustratively includes a biomass sensor (e.g., biomass sensor 336) and a processing system 338. In some cases, the biomass sensor 336 may be located on the agricultural harvester 100. The processing system 338 processes the sensor data generated from the onboard sensor 336 to generate processed data, some examples of which are described below.

[0081] In some examples, the biomass sensor 336 may be an optical sensor, such as a camera, stereo camera, monocular camera, lidar, or radar, which generates images of areas of the field to be harvested. In some cases, the optical sensor may be positioned on or attached to the harvester 100 to collect images of areas adjacent to the harvester 100 (such as areas in front of the harvester 100, areas to the side of the harvester 100, areas behind the harvester 100, or areas in another direction relative to the harvester 100) as the harvester 100 moves across the field during harvesting operations. The optical sensor may also be located on or inside the harvester 100 to obtain images of one or more portions of the harvester 100, either externally or internally. The processing system 338 processes one or more images obtained via the biomass sensor 336 to generate processed image data that determines one or more characteristics of the crop in the images. The vegetation characteristics detected by the processing system 338 may include the height of vegetation present in the image, the volume of vegetation present in the image, the mass of vegetation present in the image, or the density of crops in the image. In another example, the biomass sensor 336 may be a force sensor that generates a sensor signal indicating the force (such as fluid pressure or torque) used to drive the threshing drum 112 of the agricultural harvester 100, to indicate the biomass processed by the agricultural harvester 100 during the harvesting operation.

[0082] The field sensor 208 may be or include other types of sensors, such as a camera (hereinafter referred to as a "process camera") positioned along the path of the cut vegetation material traveling in the harvester 100. The process camera may be located inside the harvester 100 and may capture images of the vegetation material as it moves through or exits the harvester 100. For example, the process camera may be configured to detect vegetation material entering through the feed housing of the harvester 100. The process camera may acquire images of the cut vegetation material, and the image processing system 338 may be operable to detect the biomass or biomass characteristics of the vegetation material as it moves through or exits the harvester 100. In other examples, the field sensor 208 may include a material distribution sensor that measures the volume or mass of the material at two or more locations. The measurements may be absolute or relative. In some examples, electromagnetic or ultrasonic sensors can be used to measure the time of flight, phase shift, or binocular parallax of one or more signals reflected from a material surface at a distance relative to a reference surface. In other examples, backscattering, absorption, attenuation, or transmission of emitted signals or subatomic particles can be used to measure material distribution. In other examples, material properties such as dielectric constant can be used to measure distribution. Other methods may also be used. It should be noted that these are merely some examples of field sensor 208 and / or biomass sensor 336, and many other different sensors may be used.

[0083] In other examples, the biomass sensor 336 may rely on the wavelength of electromagnetic energy and the manner in which electromagnetic energy is reflected, absorbed, attenuated, or transmitted through the vegetation. The biomass sensor 336 may sense other electromagnetic properties of the vegetation, such as its dielectric constant, as cut vegetation material passes between two capacitive plates. The biomass sensor 336 may also rely on the mechanical properties of the vegetation, such as the signal generated when a portion of the vegetation (e.g., grain) strikes a piezoelectric plate or when the impact of a portion of the vegetation is detected by a microphone or accelerometer. Other material properties and sensors may also be used. In some examples, the biomass sensor 336 may be an ultrasonic sensor, a capacitive sensor, a dielectric constant sensor, or a mechanical sensor that senses vegetation inside or outside the agricultural harvester 100. In some examples, the biomass sensor 336 may be a light attenuation sensor or a reflection sensor. In some examples, raw or processed data from the biomass sensor 336 may be presented to the operator 260 via an operator interface mechanism 218. The operator 260 may be on the agricultural harvester 100 or at a remote location. The processing system 338 is operable to detect the biomass being harvested by the agricultural harvester 100, as well as the vegetation corresponding to various biomass characteristics encountered by the agricultural harvester 100 during the harvesting operation, such as vegetation height, vegetation volume, vegetation mass, or vegetation density.

[0084] This discussion is conducted with reference to examples where the biomass sensor 336 senses biomass or biomass characteristics, such as an optical sensor that generates an image indicating biomass or biomass characteristics, or where the biomass sensor 336 is a force sensor, such as a pressure sensor or torque sensor, that senses the force used to drive the threshing drum 112 as an indication of biomass. It should be understood that these are merely examples, and other examples of the sensors mentioned above, as well as the biomass sensor 336, are also considered herein. Figure 4 As shown, the example prediction model generator 210 includes one or more of the following: a vegetation height-to-vegetation index model generator 342, a vegetation density-to-vegetation index model generator 344, a vegetation mass-to-vegetation index model generator 345, a vegetation volume-to-vegetation index model generator 346, and a threshing drum driving force-to-vegetation index model generator 347. In other examples, the prediction model generator 210 may include more than Figure 4The examples show more, fewer, or different components than those shown. Therefore, in some examples, the predictive model generator 210 may also include additional items 348, which may include other types of predictive model generators for generating other types of vegetation characteristic models, such as other characteristics indicating biomass versus vegetation index model generators. In some examples, model generators 342, 344, 345, and 346 may also include crop characteristics as vegetation characteristics, such as crop height, crop density, crop mass, and crop volume. In other examples, the predictive model generator may include one or more of a crop height versus vegetation index model generator, a crop density versus vegetation index model generator, a crop mass versus vegetation index model generator, or a crop volume versus vegetation index model generator.

[0085] The vegetation height-to-vegetation index model generator 342 determines the relationship between the vegetation height detected in the processed data 340 at a geographic location corresponding to the processed data 340 and the vegetation index value from the vegetation index map 332 corresponding to the same location in the field at that vegetation height. Based on this relationship established by the vegetation height-to-vegetation index model generator 342, the vegetation height-to-vegetation index model generator 342 generates a predictive biomass model. The vegetation height map generator 352 uses this predictive biomass model to predict the vegetation height at any given location in the field based on the georegistered vegetation index value in the vegetation index map 332 corresponding to that location in the field.

[0086] The vegetation density-to-vegetation index model generator 344 determines the relationship between the vegetation density level represented in the processed data 340 at a geographic location corresponding to the processed data 340 and the vegetation index value corresponding to the same geographic location. Similarly, the vegetation index value is a georegistered value included in the vegetation index map 332. Based on this relationship established by the vegetation density-to-vegetation index model generator 344, the vegetation density-to-vegetation index model generator 344 generates a predictive biomass model. The vegetation density map generator 354 uses this predictive biomass model to predict the vegetation density at any given location in the field based on the georegistered vegetation index value included in the vegetation index map 332 corresponding to that location in the field.

[0087] The vegetation quality-to-vegetation index model generator 345 determines the relationship between the vegetation quality represented in the processed data 340 at a geographic location in the field corresponding to the processed data 340 and the vegetation index value from the vegetation index map 332 corresponding to the same location. Based on this relationship established by the vegetation quality-to-vegetation index model generator 345, the vegetation quality-to-vegetation index model generator 345 generates a predictive biomass model. The vegetation quality map generator 355 uses this predictive biomass model to predict the vegetation quality at any given location in the field based on the georegistered vegetation index value included in the vegetation index map 332 corresponding to the location in the field.

[0088] The vegetation volume-to-vegetation index model generator 346 determines the relationship between the vegetation volume represented in the processed data 340 at a geographic location in the field corresponding to the processed data 340 and the vegetation index value from the vegetation index map 332 corresponding to the same location. Based on this relationship established by the vegetation volume-to-vegetation index model generator 346, the vegetation volume-to-vegetation index model generator 346 generates a predictive biomass model. The vegetation volume map generator 356 uses this predictive biomass model to predict the vegetation volume at any given location in the field based on the georegistered vegetation index value in the vegetation index map 332 corresponding to the location in the field.

[0089] The threshing drum drive force to vegetation index model generator 347 determines the relationship between the threshing drum drive force, as represented in the processed data 340, at a geographic location in the field corresponding to the processed data 340, and the vegetation index value from the vegetation index map 332 corresponding to the same location. Based on this relationship established by the threshing drum drive force to vegetation index model generator 347, the threshing drum drive force to vegetation index model generator 347 generates a predictive biomass model. The threshing drum drive force map generator 357 uses this predictive biomass model to predict the threshing drum drive force at any given location in the field based on the georegistered vegetation index value in the vegetation index map 332 corresponding to the location in the field.

[0090] In view of the foregoing, the prediction model generator 210 is operable to generate multiple prediction biomass models, such as one or more prediction biomass models generated by model generators 342, 344, 345, 346, 347, and 348. In another example, two or more of the above-described prediction biomass models can be combined into a single prediction biomass model that predicts two or more biomass characteristics based on vegetation index values ​​at different locations in the field, such as vegetation height (e.g., crop height, weed height, etc.), vegetation density (e.g., crop density, weed density, etc.), vegetation mass (e.g., crop mass, weed mass, etc.), vegetation volume (e.g., crop volume, weed volume, etc.), or threshing drum driving force. Any one of these biomass models or combinations thereof in Figure 4 The values ​​are jointly represented by the predictive biomass model 350.

[0091] The predicted biomass model 350 is provided to the prediction map generator 212. Figure 4 In one example, the prediction map generator 212 includes a vegetation height map generator 352, a vegetation density map generator 354, a vegetation mass map generator 355, a vegetation volume map generator 356, and a threshing drum drive force map generator 357. In other examples, the prediction map generator 212 may include more, fewer, or different map generators. Therefore, in some examples, the prediction map generator 212 may include other items 358, which may include other types of map generators to generate biomass maps for other types of characteristics. For example, the prediction map generator 212 may include one or more of a crop height map generator, a crop density map generator, a crop mass map generator, or a crop volume generator. Furthermore, in other examples, map generators 352, 354, 355, or 356 may map crop characteristics as vegetation characteristics, such as crop height, crop density, crop mass, or crop volume. The vegetation height map generator 352 receives the predictive biomass model 350 and generates a prediction map based on the predictive biomass model 350 and on the vegetation index values ​​at different locations in the field contained in the vegetation index map 332, the prediction map predicting the vegetation height at said different locations in the field.

[0092] A vegetation density map generator 354 receives a predicted biomass model 350 and generates a prediction map based on the predicted biomass model 350 and vegetation index values ​​at different locations in the field, included in a vegetation index map 332. The prediction map predicts the vegetation density at these different locations in the field. A vegetation quality map generator 355 receives the predicted biomass model 350 and generates a prediction map based on the predicted biomass model 350 and vegetation index values ​​at different locations in the field, included in a vegetation index map 332. The prediction map predicts the vegetation quality at these different locations in the field. A vegetation volume map generator 356 receives the predicted biomass model 350 and generates a prediction map based on the predicted biomass model 350 and vegetation index values ​​at different locations in the field, included in a vegetation index map 332. The prediction map predicts the vegetation volume at these different locations in the field. Threshing drum drive force map generator 352 receives a predicted biomass model 350 and generates a prediction map based on the predicted biomass model 350 and vegetation index values ​​at different locations in the field, included in a vegetation index map 332. This prediction map predicts the threshing drum drive force at said different locations in the field. Other map generator 358 generates a prediction map based on the vegetation index values ​​at different locations in the field and the predicted biomass model 350. This prediction map predicts other characteristics at said different locations in the field, such as crop characteristics, like crop height, crop density, crop mass, or crop volume.

[0093] The prediction map generator 212 outputs one or more prediction biomass maps 360 capable of predicting biomass or biomass characteristic values ​​at different geographic locations in the field. In one example, the one or more prediction biomass maps 360 predict one or more of vegetation height, vegetation density, vegetation mass, vegetation volume, or threshing drum drive force. In another example, the one or more prediction biomass maps 360 predict one or more of crop height, crop density, crop mass, or crop volume. In other examples, vegetation height, vegetation density, vegetation mass, or vegetation volume may respectively include an indication of crop height, an indication of crop density, an indication of crop mass, or an indication of crop volume. Each of the prediction biomass maps 360 predicts the corresponding characteristic at different locations in the field. Each generated prediction biomass map 360 may be provided to a control zone generator 213, a control system 214, or both. The control zone generator 213 generates control zones and incorporates those control zones into the functional prediction map (i.e., prediction biomass map 360) to provide a functional prediction biomass map 360 with control zones. A functional predicted biomass map 360 (with or without a control region) can be provided to a control system 214, which generates control signals based on the functional predicted biomass map 360 (with or without a control region), a predicted control region map 265 (prediction map 264), or both, to control one or more of the controllable subsystems 216.

[0094] Figure 5 This 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 biomass model 350 and the prediction biomass map 360, respectively. At block 362, the prediction model generator 210 and the prediction map generator 212 receive a priori vegetation index map 332. At block 364, the processing system 338 receives one or more sensor signals from the field sensor 208 (e.g., biomass sensor 336). As discussed above, the field sensor 208 (e.g., biomass sensor 336) can be: an optical sensor 368, such as a camera (e.g., a forward-looking camera), lidar, radar, or another optical sensing or sensing device viewing the interior or exterior of the combine harvester; a threshing drum drive force sensor 369, such as a pressure sensor sensing the fluid pressure used to drive the threshing drum, or a torque sensor sensing the torque used to drive the threshing drum. Furthermore, other types of field sensors, such as another type of biomass sensor (as shown in block 370), fall within the scope of this disclosure.

[0095] At box 372, processing system 338 processes one or more received sensor signals to generate sensor data indicating biomass characteristics sensed by field sensor 208 (e.g., biomass sensor 336). At box 374, the sensor data may indicate vegetation height (e.g., crop height) that may be present at a location (e.g., in front of a combine harvester). In some cases, as indicated by box 376, the sensor data may indicate vegetation density, such as the density of crop in front of agricultural harvester 100. In some cases, as indicated by box 377, the sensor data may indicate the mass of vegetation being processed by agricultural harvester 100, such as the mass of crop or crop component. Crop component may include parts of the crop plant that comprise less than the whole crop plant, such as stems or stalks, leaves, heads or ears, rachis, grains, oils, proteins, water, or starch, and therefore, the mass of crop component may be the mass of components of the crop plant, such as stem mass, leaf mass, ear mass, grain mass, oil mass, protein mass, water mass, or starch mass, as well as the mass of many other different crop components. The mass of crop components can be used as an indicator of biomass. In some cases, as indicated in box 378, sensor data can indicate vegetation volume, such as the volume of crop in front of the agricultural harvester 100. In some cases, as indicated in box 379, sensor data can indicate threshing drum drive force, such as the fluid pressure or torque used to drive the threshing drum 112 when the agricultural harvester 100 processes plant material. Sensor data may also include other data, as indicated in box 380.

[0096] At box 382, ​​the prediction model generator 210 obtains the geographic location corresponding to the sensor data. For example, the prediction model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location from which the sensor signal or sensor data 340 was generated based on machine delay, machine speed, etc. For example, in an example where the sensor data indicates the driving force of the threshing drum, a time offset can be determined, for example, based on the position, heading, or speed data of the agricultural harvester 100, to determine the location of the vegetation being processed by the threshing drum in the field. Thus, the driving force of the threshing drum can be associated with the appropriate location in the field.

[0097] At box 384, prediction model generator 210 generates one or more prediction biomass models (e.g., biomass model 350) that model the relationship between vegetation index values ​​obtained from a priori infographic (e.g., priori infographic 258) and characteristics or related characteristics sensed by field sensor 208. For example, prediction model generator 210 may generate prediction biomass models that model the relationship between vegetation index values ​​and sensed characteristics indicated by sensor data obtained from field sensor 208, including vegetation height (e.g., crop height), vegetation density (e.g., crop density), vegetation mass (e.g., crop mass or crop component mass), vegetation volume (e.g., crop volume), or threshing drum drive force.

[0098] At box 386, a predictive biomass model (e.g., predictive biomass model 350) is provided to a prediction map generator 212, which generates a predictive biomass map 360 based on the vegetation index map and the predictive biomass model 350. This predictive biomass map 360 maps predicted biomass values ​​or biomass characteristic values. For example, in some examples, the predictive biomass map 360 predicts biomass values, such as predicted biomass levels (e.g., high, medium, low) or more specifically defined examples, such as weight (e.g., kilograms, pounds, etc.). In some examples, the predictive biomass map 360 predicts biomass characteristic values, such as predicted vegetation height, such as crop height, as indicated by box 387. In some examples, the predictive biomass map 360 predicts vegetation density, such as crop density, as indicated by box 388. In some examples, the predictive biomass map 360 predicts vegetation quality, such as crop quality or crop component quality, as indicated by box 389. In some examples, the predicted biomass map 360 predicts vegetation volume, such as crop volume, as indicated by box 390. In some examples, the predicted biomass map predicts threshing drum drive force, as indicated by box 391, and in other examples, the predicted biomass map 360 predicts other items, as indicated by box 392. It should be noted that, at box 386, the predicted biomass map 360 can predict any combination of characteristics, such as vegetation height along with vegetation density, vegetation mass, vegetation volume, or threshing drum drive force. Furthermore, the predicted biomass map 360 can be generated during agricultural operations. Therefore, when an agricultural harvester moves through a field where an agricultural operation is being performed, the predicted biomass map 360 is generated during that operation.

[0099] At box 394, the prediction map generator 212 outputs a predicted biomass map 360. At box 391, the prediction biomass map generator 212 outputs a predicted biomass map to be presented to the operator 260 for possible interaction. At box 393, the prediction map generator 212 can configure the predicted biomass map 360 for use by the control system 214. At box 395, the prediction map generator 212 can also provide the predicted biomass map 360 to the control area generator 213 for control area generation. At box 397, the prediction map generator 212 also configures the predicted biomass map 360 in other ways. The predicted biomass map 360 (with or without a control area) is provided to the control system 214. At box 396, the control system 214 generates control signals based on the predicted biomass map 360 to control the controllable subsystem 216.

[0100] This demonstrates that the system employs prior information maps, such as those mapping characteristics like vegetation index values ​​to different locations in the field, or mapping characteristic information generated during prior or previous operations to different locations in the field; and one or more field sensors using field sensor data to sense indicative characteristics (such as vegetation height, vegetation density, vegetation mass, vegetation volume, or threshing drum drive force); and a model that models the relationship between characteristics or related characteristics sensed by field sensors and those mapped in the prior information maps. Therefore, the system uses the model, field data, and prior information maps to generate a functional prediction map, and the generated functional prediction map can be configured for use by the control system and / or presented to local or remote operators or other users. For example, the control system can use this map to control one or more systems of a combine harvester.

[0101] 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 parts of the system or device to which they belong, and are activated by and facilitate the function of other components or items in these systems.

[0102] Furthermore, numerous user interface displays have been discussed. These displays can take various forms and can have various user-actuable operator interface mechanisms mounted on them. For example, user-actuable operator interface mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-actuable operator interface mechanisms can also be actuated in various ways. For example, they can be actuated using operator 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. Furthermore, if the screen displaying the user-actuable operator interface mechanism is a touch-sensitive screen, touch gestures can be used to actuate the mechanism. Moreover, voice recognition functionality can be used to actuate the mechanism using voice commands. Voice recognition can be implemented using voice detection devices (e.g., microphones) and software for recognizing the detected voice and executing commands based on the received voice.

[0103] Many data storage devices are also discussed. It should 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 the data storage device; one or more data storage devices may all be located remotely from the system utilizing the data storage device; or one or more data storage devices may be local while the others are remote. This disclosure considers all of these configurations.

[0104] Furthermore, the accompanying diagram shows multiple boxes, with functionality belonging to each box. It should be noted that fewer boxes can be used to illustrate that functionality attributed to multiple different boxes is performed by fewer components. Moreover, more boxes can be used to show that the functionality can be distributed across more components. In different examples, some functionality can be added, and some functionality can be removed.

[0105] It should be noted that the foregoing discussion has described various different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware projects, such as processors, memory, or other processing components, that perform the functions associated with those systems, components, logic, or interactions, some of which are described below. Furthermore, any or all of the systems, components, logic, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing component, as described below. Any or all of the systems, components, logic, and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures may also be used.

[0106] 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 can provide 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 many different examples, the remote server can deliver the services over a wide area network (such as the Internet) using appropriate protocols. For example, the remote server can deliver applications over a wide area network and can be accessed through a web browser or any other computing component. Figure 2 The software or components shown herein, along with associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they 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, the components and functionalities described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, the components and functionalities can be provided from a server, or they can be installed directly or otherwise on client devices.

[0107] exist Figure 6 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 6 Specifically, a prediction model generator 210 or a prediction graph generator 212, or both, can be located at a server location 502, away 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.

[0108] Figure 6 Another example of a remote server architecture is also described. Figure 6 It shows Figure 2 Some components can be located at a remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be located at a separate location from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the agricultural harvester 600 via a network (such as a wide area network or local area network); these components can be hosted as a service at a remote site; or they can be provided as a service or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to an operator, user, or system. For example, a physical carrier wave can be used instead of an electromagnetic carrier wave, or a physical carrier wave can be used in addition to an electromagnetic carrier wave. In some examples, where wireless telecommunications service coverage is poor or nonexistent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches a machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. The collected information can then be forwarded to another network when the machine containing the received information reaches a location with wireless telecommunications service coverage or other available wireless coverage. For example, when the fuel truck travels to a location to refuel other machines or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, information can be stored on the combine harvester 600 until it enters an area with wireless communication coverage. The combine harvester 600 itself can transmit the information to another network.

[0109] It will also be noted that Figure 2 The components or parts thereof can be arranged on a variety of different devices. One or more of these devices may include an airborne computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

[0110] In some examples, the remote server architecture 500 may include network security measures. These measures, without limitation, include encryption of data on storage devices, encryption of data transmitted 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).

[0111] Figure 7 This is a simplified block diagram illustrating a schematic example of a handheld computing device or mobile computing device 16 that can be used as a user's or customer's handheld device, in which the system (or a portion thereof) can be deployed. For example, a mobile device could be deployed in the operator's compartment of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 8 to 9 Examples are handheld or mobile devices.

[0112] Figure 7 A general block diagram of the components of client device 16, which can run... is provided. Figure 2 Some of the components shown in the diagram, the client device 16 can be connected to Figure 2 Some components shown interact, or both. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, a channel is provided for automatically receiving information (e.g., by scanning). Examples of communication link 13 include those allowing communication via one or more communication protocols, such as wireless services for providing cellular access to a network and protocols for providing local wireless connectivity to a network.

[0113] In other examples, applications 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 from other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and positioning system 27.

[0114] In one example, I / O component 23 is provided to facilitate input and output operations. Various examples of I / O component 23 in device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speaker and / or printer ports). Other I / O components 23 may also be used.

[0115] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, clock 25 may also provide timing functionality for processor 17.

[0116] Positioning system 27 schematically includes components that output the current geographic location of device 16. Positioning system 27 may include, for example, a Global Positioning System (GPS) receiver, a LoRAN system, a dead reckoning system, a cellular triangulation system, or other positioning systems. Positioning system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0117] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage device 37, communication driver 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 facilitate the function of those components.

[0118] 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-enabled interface that receives input from a pen or stylus. Tablet computer 600 may also use an on-screen virtual keyboard. Of course, computer 600 may also be attached to a keyboard or other user input device, for example, via a suitable attachment mechanism (such as a wireless link or USB port). Computer 600 may also schematically receive voice input.

[0119] Figure 9 Similar to Figure 8 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, tiles, or other user input mechanisms 75. Users can use these mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than feature phones.

[0120] Note that other forms of device 16 are possible.

[0121] Figure 10 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference 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 from the previous figures), system memory 830, and a system bus 821 that connects various system components, including the 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 different bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 10 In the corresponding part.

[0122] Computer 810 typically includes a variety of different 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. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any way or by any technique 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 devices, magnetic tape, magnetic tape, disk storage devices 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 implement 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 that has one or more characteristics set or changed in a manner that encodes information in the signal.

[0123] 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. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. By way of example and not limitation, Figure 10 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0124] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 10 A hard disk drive 841 is shown that reads from or writes to a non-removable non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0125] 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 (ASICs), application-specific standard products (e.g., ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), etc.

[0126] 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 computer 810. For example, in Figure 10 In this diagram, hard disk drive 841 is shown storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0127] Users can input commands and information to computer 810 through input devices such as keyboard 862, microphone 863, and pointing devices 861 (such as mouse, trackball, or touchpad). Other input devices (not shown) may include joysticks, game controllers, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860, which is connected to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0128] Computer 810 operates in a networked environment using a logical connection (such as a controller local area network (CAN), local area network (LAN), or wide area network (WAN)) of one or more remote computers (such as remote computer 880).

[0129] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 10 This demonstrates that remote application 885 can reside on remote computer 880.

[0130] It should also be noted that the different examples described in this paper can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is considered in this paper.

[0131] Example 1 is an agricultural operating machine, comprising:

[0132] A communication system that receives a priori information map, the priori information map including agricultural characteristics corresponding to different geographical locations in the field;

[0133] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0134] A field sensor that detects values ​​of biomass characteristics corresponding to the geographical location;

[0135] A predictive model generator generates a predictive agricultural model based on the values ​​of the agricultural characteristics in the prior information map at the geographical location and the values ​​of the biomass characteristics sensed by the field sensors corresponding to the geographical location. The predictive agricultural model models the relationship between the agricultural characteristics and the biomass characteristics.

[0136] A prediction map generator generates a functional predictive agriculture map of the field based on the values ​​of the agricultural characteristics in the prior information map and based on the predictive agriculture model. The functional predictive agriculture map maps the predicted values ​​of the biomass characteristics to the different geographical locations in the field.

[0137] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator configures the functional predictive agricultural map for use by a control system, which generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural operating machine.

[0138] Example 3 is any or all of the agricultural machinery described in the foregoing examples, wherein the field sensors on the agricultural machinery are configured to detect values ​​of vegetation characteristics corresponding to the geographic location, which are values ​​of the biomass characteristics.

[0139] Example 4 is any or all of the agricultural machinery described in the foregoing examples, wherein the field sensors include:

[0140] An image detector configured to detect images indicative of the vegetation characteristics.

[0141] Example 5 is any or all of the agricultural machinery described in the foregoing examples, wherein the image detector is oriented to detect an image of at least a portion of the field, and the field sensor further comprises:

[0142] An image processing system configured to process the image to determine indicative values ​​of the vegetation characteristics in the image.

[0143] Example 6 is any or all of the agricultural machinery described in the foregoing examples, wherein the field sensor generates a sensor signal indicating a value indicative of the vegetation characteristic, and the field sensor further comprises:

[0144] A processing system that receives the sensor signal and is configured to determine a vegetation height value corresponding to the geographic location, which is an indication of the vegetation height and serves as a value of the vegetation characteristic.

[0145] Example 7 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes a prior vegetation index map that maps vegetation index values ​​as the agricultural characteristics to the different geographical locations in the field, and

[0146] The predictive model generator is configured to determine the relationship between the vegetation index value and the vegetation height based on the vegetation height detected by the field sensors corresponding to the geographical location and the vegetation index value at the geographical location in the vegetation index map. The predictive agriculture model is configured to receive the vegetation index value as model input and generate a predicted value of the vegetation height as model output based on the determined relationship.

[0147] Example 8 is an agricultural operating machine of any or all of the foregoing examples, wherein the field sensor generates a sensor signal indicating a value indicative of the vegetation characteristic, and the field sensor further comprises:

[0148] A processing system that receives the sensor signal and is configured to determine a vegetation density value corresponding to the geographic location, which is an indication of the vegetation density and serves as a value of the vegetation characteristic.

[0149] Example 9 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes a prior vegetation index map that maps vegetation index values ​​as the agricultural characteristics to the different geographical locations in the field, and

[0150] The predictive model generator is configured to determine the relationship between the vegetation index value and the vegetation density based on the vegetation density value corresponding to the geographical location detected by the field sensors and the vegetation index value at the geographical location in the vegetation index map. The predictive agriculture model is configured to receive the vegetation index value as model input and generate a predicted value of the vegetation density as model output based on the identified relationship.

[0151] Example 10 is any or all of the agricultural machinery described in the foregoing examples, wherein the field sensor generates a sensor signal indicating a value indicative of the vegetation characteristics, and the field sensor further comprises:

[0152] A processing system that receives the sensor signal and is configured to determine a vegetation volume value corresponding to the geographic location, which is an indicator of the vegetation volume and serves as a value of the vegetation characteristic.

[0153] Example 11 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes a prior vegetation index map that maps vegetation index values ​​as the agricultural characteristics to the different geographical locations in the field, and

[0154] The predictive model generator is configured to determine the relationship between the vegetation index value and the vegetation volume based on the value of the vegetation volume detected by the field sensors corresponding to the geographical location and the vegetation index value at the geographical location in the vegetation index map. The predictive agriculture model is configured to receive the vegetation index value as model input and generate a predicted value of the vegetation volume as model output based on the determined relationship.

[0155] Example 12 is any or all of the agricultural operating machines of the foregoing examples, wherein the field sensors on the agricultural machine are configured to detect machine operating characteristics corresponding to the geographic location, which are values ​​of the biomass characteristics, and to generate sensor signals indicating the values ​​of the machine operating characteristics, and wherein the operating machine further includes:

[0156] A processing system that receives the sensor signals and is configured to determine a value corresponding to the geographical location of a threshing drum driving force, which is a value indicating the force used to drive the threshing drum, as a value of the machine's operating characteristics.

[0157] Example 13 is any or all of the agricultural machinery described in the foregoing examples, wherein the prior information map includes a prior vegetation index map that maps vegetation index values, which are the agricultural characteristics, to the different geographical locations in the field, and

[0158] The predictive model generator is configured to determine the relationship between the vegetation index value and the threshing drum driving force based on the detected value of the threshing drum driving force corresponding to the geographical location and the vegetation index value at the geographical location in the vegetation index map. The predictive agriculture model is configured to receive the vegetation index value as model input and generate a predicted value of the threshing drum driving force as model output based on the determined relationship.

[0159] Example 14 is a computer-implemented method for generating functional predictive agricultural maps, comprising:

[0160] A priori information map is received at the agricultural machinery, the priori information map indicating the value of an agricultural characteristic corresponding to different geographical locations in the field;

[0161] Detect the geographical location of the agricultural machinery;

[0162] The biomass characteristics corresponding to the geographical location were detected using on-site sensors;

[0163] Generate a predictive agricultural model that models the relationship between the agricultural characteristics and the biomass characteristics; and

[0164] The control prediction map generator generates the functional predictive agriculture map of the field based on the values ​​of the agricultural characteristics in the prior information map and the predictive agriculture model. The functional predictive agriculture map maps the predicted values ​​of the biomass characteristics to the different geographical locations in the field.

[0165] Example 15 is a computer-implemented method of any or all of the foregoing examples, further comprising:

[0166] The functional predictive agriculture map is configured for use by a control system, which generates control signals based on the functional predictive agriculture map to control controllable subsystems on the agricultural machinery.

[0167] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein detecting the value of the biomass characteristic with field sensors includes detecting the value of the vegetation characteristic corresponding to the geographic location; and

[0168] The prior information map includes a prior vegetation index map, which maps the vegetation index values, which are the agricultural characteristics, to different geographical locations in the field.

[0169] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein detecting the value of the biomass characteristic with field sensors includes detecting a value of the threshing drum drive force corresponding to the geographical location; and

[0170] The prior information map includes a prior vegetation index map, which maps the vegetation index values, which are the agricultural characteristics, to different geographical locations in the field.

[0171] Example 18 is an agricultural operating machine, comprising:

[0172] A communication system that receives a priori vegetation index map, the priori vegetation index map indicating vegetation index values ​​corresponding to different geographical locations in the field;

[0173] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0174] A field sensor, wherein the field sensor detects biomass characteristic values ​​corresponding to the geographical location;

[0175] A predictive model generator generates a predictive biomass model based on vegetation index values ​​at a given geographic location in the prior vegetation index map and biomass characteristic values ​​corresponding to that geographic location detected by the field sensors. The predictive biomass model models the relationship between the vegetation index values ​​and the biomass characteristics.

[0176] A prediction map generator generates a functional predicted biomass map of the field based on the vegetation index values ​​in the prior vegetation index map and the predicted biomass model, the functional predicted biomass map mapping the predicted biomass characteristics to the different geographical locations in the field.

[0177] Example 19 is any or all of the agricultural operating machines of the foregoing examples, wherein the biomass characteristic is a vegetation characteristic, and the field sensors include:

[0178] An image detector, configured to detect images indicative of the vegetation characteristics; and

[0179] An image processing system configured to process an image indicating the vegetation characteristics to determine vegetation characteristic values ​​in the image that indicate the vegetation characteristics, which are biomass characteristic values.

[0180] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the biomass characteristic is a machine operating characteristic, and the field sensors include:

[0181] A threshing drum drive force sensor, configured to detect the force used to drive the threshing drum and generate a sensor signal indicating the force used to drive the threshing drum; and

[0182] The processing system is configured to determine the value of the threshing drum driving force, which is a biomass characteristic value, based on the sensor signal.

[0183] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural system comprising: A communication system (206) receives a priori information map (258), the priori information map including agricultural characteristics 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 values ​​of biomass characteristics corresponding to the geographical location; A predictive model generator (210) generates a predictive agricultural model based on the value of the agricultural characteristic in the prior information map (258) at the geographic location and the value of the biomass characteristic sensed by the field sensor (208) corresponding to the geographic location. The predictive agricultural model models the relationship between the agricultural characteristic and the biomass characteristic. and A prediction map generator (212) generates a functional predictive agricultural map of the field based on the values ​​of the agricultural characteristics in the prior information map (258) and based on the predictive agricultural model. The functional predictive agricultural map predicts the values ​​of the biomass characteristics and maps the predicted values ​​of the biomass characteristics to the different geographical locations in the field.

2. The agricultural system according to claim 1, wherein, The predictive map generator configures the functional predictive agricultural map for use by the control system, which generates control signals based on the functional predictive agricultural map to control controllable subsystems on the agricultural machinery.

3. The agricultural system according to claim 1, wherein, The field sensor is configured to detect vegetation characteristics corresponding to the geographic location, and the values ​​of the vegetation characteristics are used as values ​​of the biomass characteristics.

4. The agricultural system according to claim 3, wherein, The field sensors include: An image detector configured to detect images indicative of the vegetation characteristics.

5. The agricultural system according to claim 4, wherein, The image detector is oriented to detect an image of at least a portion of the field, and the field sensor further includes: An image processing system configured to process the image to identify values ​​of the vegetation characteristics in the image, the values ​​of which indicate the vegetation characteristics.

6. The agricultural system according to claim 3, wherein, The field sensor generates a sensor signal indicating a value that indicates the vegetation characteristics, and the field sensor further includes: A processing system that receives the sensor signal and is configured to identify a vegetation height value corresponding to the geographic location that indicates the vegetation height, the vegetation height value being used as a value of the vegetation characteristic.

7. The agricultural system according to claim 6, wherein, The prior information map includes a prior vegetation index map, which maps vegetation index values, which are the agricultural characteristics, to the different geographical locations in the field. The predictive model generator is configured to determine the relationship between the vegetation index value and the vegetation height based on the value of the vegetation height detected by the field sensors corresponding to the geographical location and the vegetation index value at the geographical location in the vegetation index map. The predictive agriculture model is configured to receive the vegetation index value as model input and generate a predicted value of the vegetation height as model output based on the determined relationship.

8. The agricultural system according to claim 3, wherein, The field sensor generates a sensor signal indicating a value that indicates the vegetation characteristics, and the field sensor further includes: A processing system that receives the sensor signal and is configured to identify a vegetation density value corresponding to the geographic location that indicates vegetation density, the vegetation density value being used as a value of the vegetation characteristic.

9. A computer-implemented method for generating functional predictive agricultural maps, comprising: Receive a priori information map (258), the priori information map indicating the value of an agricultural characteristic corresponding to different geographical locations in the field; Detect the geographical location of agricultural machinery (100); The biomass characteristics corresponding to the geographical location were detected using a field sensor (208); Generate a predictive agricultural model that models the relationship between the agricultural characteristics and the biomass characteristics; and The control prediction map generator generates the functional predictive agriculture map of the field based on the values ​​of the agricultural characteristics in the prior information map (258) and the predictive agriculture model. The functional predictive agriculture map predicts the values ​​of the biomass characteristics and maps the predicted values ​​of the biomass characteristics to the different geographical locations in the field.

10. An agricultural system comprising: A communication system (206) receives a priori vegetation index map (258), the priori vegetation index map indicating vegetation index values ​​corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of agricultural machinery; A field sensor (208) detects biomass characteristic values ​​corresponding to the geographical location; A prediction model generator (210) generates a prediction biomass model based on the vegetation index value at the geographic location in the prior vegetation index map (258) and the biomass characteristic value corresponding to the geographic location detected by the field sensor (208). The prediction biomass model models the relationship between the vegetation index value and the biomass characteristic. and A prediction map generator (212) generates a functional predicted biomass map of the field based on the vegetation index values ​​in the prior vegetation index map (258) and the predicted biomass model. The functional predicted biomass map predicts the values ​​of the biomass characteristics and maps the predicted values ​​of the biomass characteristics to the different geographical locations in the field.

Images