DE102021124424A1 - MACHINE CONTROL USING A PREDICTIVE MAP - Google Patents

Abstract

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural feature at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural feature sensed by the in situ sensor . The predictive map can be output and used in automated machine control.

Description

FIELD OF DESCRIPTION

The present description relates to agricultural machines, forestry, construction and lawn care machines.

BACKGROUND

There are many different types of agricultural machinery. Some agricultural machines include harvesters such as combine harvesters, sugar cane harvesters, cotton harvesters, forage harvesters, and windrowers. Some harvesters can be fitted with different types of headers to harvest different types of crops.

The height of vegetation, including crop plants and weed plants, often varies across an agricultural field. Without changing machine settings, variation in vegetation height could affect harvesting performance.

SUMMARY

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural feature at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural feature sensed by the in situ sensor . The predictive map can be output and used in automated machine control.

character list

• 1 12 is a partially pictorial, partially schematic representation of an example combine harvester.
• 2 12 is a block diagram showing in more detail some portions of an agricultural harvesting machine according to some examples of the present disclosure.
• the 3a-3b (herein together as 3 ) show a flow chart illustrating an example of the operation of an agricultural harvesting machine in generating a map.
• 4 Figure 12 is a block diagram showing an example of a predictive model generator and a predictive map generator.
• 5 Fig. 12 is a flow chart showing an example of an operation of an agricultural harvesting machine in receiving a vegetative index, detecting a vegetation height or a reel position, and generating a functional predictive vegetation height map or a functional predictive reel position map for use in controlling the agricultural harvesting machine during a harvesting operation.
• 6 Figure 12 is a block diagram showing an example of a control zone generator.
• 7 FIG. 12 is a flow chart illustrating an example of the operation of the control zone generator shown in FIG.
• 8th Figure 12 illustrates a flow chart showing an example of operation of a control system in selecting a target setting value to control an agricultural harvester.
• 9 Fig. 12 is a block diagram showing an example of an operator interface control.
• 10 Figure 12 is a flow chart illustrating an example operator interface control.
• 11 Figure 12 is a pictorial representation showing an example operator interface display.
• 12 Figure 12 is a block diagram showing an example agricultural harvester in communication with a remote server environment.
• the 13-15 show examples of mobile devices that can be used in an agricultural harvesting machine.
• 16 Figure 12 is a block diagram showing an example of a computing environment that may be used in an agricultural harvester.

DETAILED DESCRIPTION

For a better understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings and specific language will be used to describe them. It is understood, however, that no restriction of protection is intended at the beginning of the revelation. All alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are contemplated fully as would normally occur to those skilled in the art to which the disclosure pertains. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure .

The present description relates to the use of in situ data collected concurrently with a farming operation in combination with previous data to generate a predictive map, such as a predictive vegetation height map. In some examples, the predictive map may be used to control reel position of a reel on an agricultural work machine.

The height of vegetation, including crop plants and weed plants, over a field can vary. The settings of an agricultural harvester can also be varied to increase the performance of the agricultural harvester. For example, if the crop plants are short, the reel position may need to be lowered to better collect the crop. Or, for example, if there is a weed patch, the header can be raised to collect the crop plants and avoid the extra biomass of some of the weeds.

A vegetative index map illustratively maps vegetative index values (which may be indicative of vegetative growth) across various geographic locations in a field of interest. An example of a vegetative index includes a normalized difference vegetative index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected from the plants. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can be used to identify the presence and location of vegetation. In some examples, these maps allow weeds to be identified and georeferenced in the presence of bare ground, plant debris, or other vegetation, including crops or other weeds. For example, at the end of a growing season, when a crop is mature, the crop plants may have a reduced proportion of living, growing vegetation. However, weeds often persist in a growing state after the crop has matured. Therefore, if a vegetative index map is made relatively late in the growing season, the vegetative index map can indicate the position of weeds in the field.

The present discussion also includes predictive maps that predict a characteristic based on an information map and a relationship to sensed data obtained from an in situ sensor. These predictive maps include a predictive yield map and a predictive crop condition map. In one example, the predictive yield map is generated by receiving a previous vegetative index map and collected yield data obtained from an in situ yield sensor and determining a relationship between the previous vegetative index map and the collected yield data obtained from a signal from the in-situ yield sensor. situ yield sensor are obtained, and using the relationship to generate the predictive yield map based on the relationship and the previous vegetative index map generated. In one example, the predictive crop condition map is generated by receiving one or more of a vegetative index, yield, biomass, or seed map and sensing a crop condition and determining a relationship between the one or more of a vegetative index, yield, Biomass or seed map and the sensed crop condition obtained from a data signal from a crop condition sensor, and by using the relationship to generate the predictive crop condition map based on the relationship and the one or more of a vegetative index, yield, biomass or seed card generated. The predictive yield and crop status maps can be created based on other information maps or can also be generated in other ways. For example, the predictive yield or crop condition maps can be generated based on satellite imagery, growth models, weather models, etc. Or, for example, a predictive yield map or a predictive crop condition map may be based in whole or in part on a topographic map, a soil type map, a soil constituent map, or a soil health map.

The present discussion thus continues with respect to systems that receive an information map that is predicted or generated based on prior operation and also use an in situ sensor to measure a variable that is indicative of one or more of an agricultural trait, such as vegetation height or a user command during a harvesting operation. The systems create a model that models a relationship between the values on the information card and the output values from the in situ sensor. The model is used to generate a functional predictive map that predicts, for example, vegetation height or reel positions at various locations in the field. The functional predictive map generated during the harvesting process may be presented to an operator or other user, or used to automatically control an agricultural harvesting machine during the harvesting process, or both. The functional predictive map can be used to control one or more subsystems of the agricultural harvesting machine, such as reel position.

1 12 is a partially pictorial, partially schematic illustration of a self-propelled agricultural harvester 100. In the example illustrated, the agricultural harvester 100 is a combine harvester. Further, while combines are provided as examples throughout this disclosure, it should be understood that the present description is applicable to other types of harvesting machines, such as cotton harvesters, sugarcane harvesters, forage harvesters, windrowers, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various types of harvesters described and is thus not limited to combines. Additionally, the present disclosure is directed to other types of work machines, such as agricultural seeders and sprayers, construction machines, forest machines, and lawn care machines, where predictive map generation may be applicable. Accordingly, the present disclosure is intended to encompass these various types of harvesters and other work machines and is thus not limited to combines.

As in 1 As shown, agricultural harvester 100 illustratively includes an operator cab 101 that may include a variety of different operator interface mechanisms for controlling agricultural harvester 100 . Agricultural harvester 100 includes headers, such as a header 102 and a cutter, indicated generally at 104 . In the illustrated example, cutter 104 is included on header 102 . The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108 and a thresher, generally indicated at 110. The feeder housing 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotable along the pivot axis 105 with a Frame 103 of the agricultural header 100 coupled. One or more actuators 107 power movement of header 102 about axis 105 in the direction generally indicated by arrow 109 . Thus, a vertical position of the header 102 (the header height) above the ground 111 over which the header 102 travels can be controlled by actuating the actuator 107 . Although in 1 not shown, agricultural harvesting machine 100 may also include one or more actuators operable to apply a pitch angle, a roll angle, or both to the header 102 or portions of the header 102 . Pitch refers to an angle at which the cutter assembly 104 engages the crop. The angle of inclination is increased, for example, by controlling the header 102 so that a distal edge 113 of the cutter 104 is directed more towards the ground. The angle of inclination is reduced by controlling the header 102 so that the distal edge 113 of the cutter 104 is directed further away from the ground. The roll angle refers to the orientation of the header 102 about the front-to-back longitudinal axis of the agricultural harvesting machine 100.

The thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as the cleaning subsystem 118) that a cleaning fan 120, a chopper 122 and a screen 124 includes. The material handling subsystem 125 also includes a discharge drum 126, a tailings elevator 128, a clean grain elevator 130, and an unloading auger 134 and chute 136. The clean grain elevator conveys clean grain into the clean grain tank 132. The agricultural harvester 100 also includes a tailings subsystem 138 that a chopper 140 and a spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine may include more than one of the above subsystems within the scope of the present disclosure. In some examples, the farmer Economic harvester 100 have left and right cleaning subsystems, separators, etc., which in 1 can be used.

In operation and for overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147 . During movement of the agricultural harvesting machine 100, the header 102 (and the associated reel 164) engages the crop to be harvested and collects the crop towards the cutting device 104. An operator of the agricultural harvesting machine 100 can be a local human operator, a remote human operator or be an automated system. An operator command is a command from an operator. The operator of agricultural harvesting machine 100 may specify one or more height settings, pitch angle settings, or roll angle settings for header 102 . For example, the operator enters a setting or settings into a control system, described in more detail below, that controls actuator 107 . The control system may also receive a setting from the operator to set up the pitch and roll angle of the header 102 and implement the settings entered by associated actuators, not shown, that operate to change the pitch and roll angle of the header 102. The actuator 107 maintains the header 102 at a height above the ground 111 based on a height adjustment and at desired pitch and roll angles, if any. Each of the elevation, roll, and pitch settings can be implemented independently of the others. The control system responds to header errors (e.g., the difference between the elevation setting and the measured height of the header 104 above the ground 111 and, in some examples, pitch angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. When the sensitivity level is set to a higher sensitivity level, the control system will respond to smaller header position errors and attempt to reduce the detected errors faster than when the sensitivity is at a lower sensitivity level.

Returning to the description of the operation of agricultural harvesting machine 100, after the crop has been cut by cutter 104, the separated crop is moved by a conveyor in feed housing 106 toward feed accelerator 108, which accelerates the crop into thresher 110. The crop is threshed by the rotor 112 which rotates the crop against the concaves 114 . The threshed crop material is moved by a separator rotor in the separator 116 with a portion of the residue being moved towards the residue subsystem 138 by the discharge drum 126 . The portion of the tailings that is transferred to tailings subsystem 138 is shredded by tailings chopper 140 and spread by spreader 142 on the field. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, residue subsystem 138 may include weed control devices (not shown), such as seed baggers or other seed collectors or seed crushers or other seed destroyers.

The grain falls onto the cleaning subsystem 118. The chopper 122 separates a portion of coarser pieces of material from the grain and the screen 124 separates a portion of finer pieces of material from the clean grain. The clean grain falls onto an auger that moves the grain to an inlet end of the clean grain elevator 130 and the clean grain elevator 130 moves the clean grain up, thereby separating the clean grain in the clean grain tank 132 . Debris is removed from the cleaning subsystem 118 by the air flow of the cleaning fan 120 . The cleaning fan 120 directs air along an airflow path up through the screens and choppers. The airflow carries residue rearward in agricultural harvesting machine 100 toward residue handling subsystem 138.

The tailings elevator 128 returns the tailings to the thresher 110 where the tailings are re-threshed. Alternatively, the tailings can also be fed to a separate after-threshing mechanism by a tailings elevator or other transport device, where the tailings are also after-threshed.

1 Also shows that in one example, agricultural harvesting machine 100 includes machine speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151, which may be in the form of a stereo or mono camera, and one or more loss sensors 152 provided in the cleaning subsystem 118 is included.

The machine speed sensor 146 detects the ground speed of the agricultural harvesting machine 100 over the ground. The machine speed sensor 146 can determine the ground speed of the agricultural harvesting machine 100 by detecting the rotational speed of the ground engaging components (such as wheels or tracks) of a drive shaft, an axle or other components. In some cases, vehicle speed may be sensed using a positioning system, such as a global positioning system (GPS), dead reckoning system, remote navigation system (LORAN), or a variety of other systems or sensors that provide an indication of vehicle speed.

The loss sensors 152 illustratively provide an output signal indicative of the amount of grain loss occurring on both the right and left sides of the cleaning subsystem 118 . In some examples, the sensors 152 are impact sensors that count grain impacts per unit time or per unit distance to provide an indication of grain loss occurring at the cleaning subsystem 118 . The impact sensors for the right and left sides of the shoe 118 can provide individual signals or a combined or aggregated signal. In some examples, the sensors 152 can include a single sensor, as opposed to separate sensors provided for each cleaning subsystem 118 .

The separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators, not separately in 1 not shown separately. The separator loss sensors 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregated signal. In some cases, detecting grain loss in the separators can also be done using a variety of different types of sensors.

Agricultural harvester 100 may also include other sensors and measurement mechanisms. For example, the agricultural harvesting machine 100 may include one or more of the following sensors: a header height sensor that detects a height of the header 102 above the ground 111; stability sensors that detect oscillatory or bouncing motion (and amplitude) of agricultural harvesting machine 100; a residue setting sensor configured to detect whether the agricultural harvesting machine 100 is configured to shred the residue, create a windrow, etc.; a shoe box fan speed sensor to detect the speed of the fan 120; a concave gap sensor that senses the distance between the rotor 112 and the concaves 114; a threshing rotor speed sensor that detects a rotor speed of the rotor 112; a chopper gap sensor that senses the size of the openings in the chopper 122; a wire gap sensor that senses the size of the openings in the wire 124; a non-grain material (MOG) moisture sensor that detects a moisture content of the MOG passing through the agricultural harvesting machine 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvesting machine 100; a machine orientation sensor that detects the orientation of the agricultural harvesting machine 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. The crop characteristic sensors can also be configured to detect the characteristics of the separated crop during processing by the agricultural harvesting machine 100 . For example, in some cases, the crop property sensors may sense grain quality, such as broken grain, MOG values; grain components such as starches and protein; and grain feed rate as the grain moves through the feeder house 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensors may also sense the rate of advance of biomass through the feeder house 106 , through the separator 116 , or elsewhere in the agricultural harvester 100 . The crop property sensors may also sense haul rate as a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvesting machine 100, or provide other output signals indicative of other sensed quantities.

Before describing how the agricultural harvesting machine 100 generates a functional predictive vegetation height map or functional predictive reel position map and uses one or more of the functional predictive maps for control, a brief description of some elements of the agricultural harvesting machine 100 and their operation is first provided. The description of 2 and 3 describes receiving a general type of information map and combining information from the information map with a georeferenced sensor signal generated by an in situ sensor, the sensor signal being indicative of a feature in the field, such as crop or weed features, that is present in the field. Field characteristics may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed species, soil moisture, surface quality; Characteristics of crop properties, such as crop height, crop moisture, crop density, crop condition; Characteristics of grain properties, such as grain moisture, grain size, grain test weight; and machine performance characteristics such as loss levels carrying quality, fuel consumption and power utilization. A relationship between the feature values derived from in situ sensor signals and the information map values is identified and this relationship is used to generate a new functional predictive map. A functional predictive map predicts values at various geographic locations in a field, and one or more of these values can be used to control a machine, such as one or more subsystems of an agricultural harvester. In some cases, a functional predictive map may be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map may be presented to a user visually, such as via a display, haptically, or audibly. The user can interact with the functional predictive map to perform editing operations and other user interface operations. In some cases, a functional predictive map can be used for one or more of controlling an agricultural work machine, such as an agricultural harvester, presenting to an operator or other user, and presenting to an operator or user for interaction by the operator or user will.

After the general approach regarding the 2 and 3 , a more specific approach to generating a functional predictive vegetation height map or functional predictive reel position map that can be presented to an operator or user or used to control the agricultural harvesting machine 100 or both is described with reference to FIG 4 and 5 , discussed. Although the present discussion continues with respect to the agricultural harvester, and more particularly the combine, other types of agricultural harvesters or other agricultural work machines are within the scope of the present disclosure.

2 FIG. 12 is a block diagram showing some portions of an exemplary agricultural harvesting machine 100. FIG. 2 shows that the agricultural harvester 100 illustratively includes one or more processors or servers 201, a data store 202, a geographic position sensor 204, a communication system 206 and one or more in situ sensors 208 that one or more agricultural features of a field at the same time record a harvesting process. An agricultural trait can include any trait that can affect the harvesting process. Some examples of agricultural traits include harvester traits, field traits, crops in the field traits, and weather. Other types of agricultural characteristics are also considered. The in situ sensors 208 generate values corresponding to the sensed features. Agricultural harvester 100 also includes a predictive model or relationship generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive map generator 212, a control zone generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The Agricultural harvester 100 may also include a variety of other agricultural harvester functions 220 . The in situ sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of a field during a farming operation. The predictive model generator 210 illustratively includes an information variable-to-in situ variable model generator 228 , and the predictive model generator 210 may include other elements 230 . The control system 214 includes the communication system control 229, the operator interface control 231, an adjustment control 232, the path planning control 234, the throng speed control 236, the header and reel control 238, the draper belt control 240, the top plate position control 242, the residue system control 244, the machine cleaning control 245, the zone control 247 , and system 214 may include other elements 246 . The controllable subsystems 216 include machine, header, and reel actuators 248, the propulsion subsystem 250, the steering subsystem 252, the residue subsystem 138, the machine cleaning subsystem 254, and the subsystems 216 may include a variety of other subsystems 256.

2 also shows that the agricultural harvesting machine 100 can receive the information card 258 . As described below, the information map 258 includes, for example, a vegetative index map, crop condition map, or a yield map. However, the information map 258 may also include other types of data obtained prior to a harvesting operation or a pre-operational map. 2 also shows that an operator 260 can operate the agricultural harvesting machine 100 . The operator 260 interacts with the operator interface mechanisms 218. In some examples, the operator interface mechanisms 218 may be joysticks, levers, a steering wheel, linkages, pedals, buttons, knobs, keypads, user actuable elements (such as icons, buttons, etc.) on a user interface voice display device, a microphone and speaker (if speech recognition and speech synthesis are provided), and a variety of other types of control devices. When a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Accordingly, other types of operator interface mechanisms 218 may be used and are within the scope of the present disclosure.

Information map 258 may be downloaded to agricultural harvester 100 and stored in data storage 202 using communication system 206 or otherwise. In some examples, communication system 206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations to communicate from networks. The communication system 206 may also include a system that facilitates the downloading or transfer of information to and from a Secure Digital (SD) card or a universal serial bus (USB) card, or both.

The geographic position sensor 204 illustratively senses or detects the geographic position or location of the agricultural harvesting machine 100. The geographic position 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 geographic position sensor 204 may also include a real-time kinematics (RTK) component configured to improve the accuracy of the position data derived from the GNSS signal. The geographic position sensor 204 may include a dead reckoning system, a cellular triangulation system, or a variety of other geographic position sensors.

The in situ sensors 208 can be any of the sensors described above with respect to FIG 1 not shown separately. The in situ sensors 208 include onboard sensors 222 mounted onboard the agricultural harvester 100 . Such sensors may include, for example, a perceptual sensor (e.g., a mono or stereo forward-looking camera system and an image processing system). The in situ sensors 208 also include remote in situ sensors 224 that collect in situ information. In situ data includes data collected by a sensor onboard the harvester or any sensor where the data is detected during the harvesting process.

The predictive model generator 210 generates a model that indicates a relationship between the values sensed by the in situ sensor 208 and a metric mapped by the information map 258 onto the field. For example, if information map 258 maps a vegetative index value to various locations in the field and in situ sensor 208 detects a value indicative of vegetation height, then information variable-to-in situ variable model generator 228 generates a predictive Vegetation height model that models the relationship between vegetative index and vegetation height. The predictive vegetation height model may also be generated based on vegetative index values from information map 258 and multiple in situ data values generated by in situ sensors 208 . Predictive map generator 212 uses the predictive vegetation height model generated by predictive model generator 210 to generate a functional predictive vegetation height map that maps predictions of the value of vegetation height at various locations in the field based on information map 258 .

In some examples, the type of values in functional predictive map 263 may be the same as the type of in situ data sensed by in situ sensors 208 . In some cases, the type of values in the functional predictive map 263 may have different units than the data collected from the in situ sensors 208 . In some examples, the type of values in functional predictive map 263 may be different than the type of in situ data sensed by in situ sensors 208 but related to the type of data type sensed by in situ sensors 208 . For example, the type of data collected from in situ sensors 208 may indicate the type of values in functional predictive map 263 in some examples. In some examples, the type of data in functional predictive map 263 may differ from the type of data in information map 258 . In some cases, the type of data in functional predictive map 263 may have different entities than the data in information map 258 . In some examples, the type of data in functional predictive map 263 may differ from the type of data in information map 258, but is related to the type of data in information map 258. At for example, the type of data in information map 258 may indicate the type of data in functional predictive map 263 in some examples. In some examples, the type of data in functional predictive map 263 differs from one or both of the in situ data type sensed by in situ sensors 208 and the data type in information map 258. In some examples, the type is the Data in functional predictive map 263 is the same as one or both of the in situ data type sensed by in situ sensors 208 and the data type in information map 258. In some examples, the data type in functional predictive map 263 is the same such as the in situ data type sensed by the in situ sensors 208 or the type of data in the information card 258 and is different from the other.

As in 2 As shown, predictive map 264 predicts the value of a detected feature (detected by in situ sensors 208) or a feature related to the detected feature at various locations across the field based on an information value in information map 258 these positions and using the predictive model. For example, if the predictive model generator 210 has generated a predictive model indicating a relationship between vegetative index and vegetation height, then given the vegetative index value at various positions across the field, the predictive map generator 212 generates a predictive map 264 that indicates the value of the Predict vegetation height at various locations across the field. The vegetative index value obtained from the vegetative index map at those positions and the relationship between the vegetative index and vegetation height obtained from the predictive model are used to generate the predictive map 264 .

Some variations in the types of data depicted in information map 258, the types of data captured by in situ sensors 208, and the types of data predicted on predictive map 264 will now be described.

In some examples, the type of data in information map 258 differs from the type of data captured by in situ sensors 208, yet the type of data in predictive map 264 is the same as the type of data captured by in situ sensors 208 will. For example, information map 258 may be a vegetative index map and the variable sensed by in situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to various geographic locations in the field. In another example, information map 258 may be a vegetative index map and the variable sensed by in situ sensors 208 may be crop height. The predictive map 264 may then be a predictive crop height map that maps predicted crop height values to various geographic locations in the field.

Also, in some examples, the type of data in information card 258 differs from the type of data sensed by in situ sensors 208, and the type of data in predictive map 264 differs from both the type of data in information card 258 and the type of data , which is detected by the in situ sensors 208 . For example, information map 258 may be a vegetative index map and the variable sensed by in situ sensors 208 may be crop height. The predictive map 264 can then be a predictive biomass map that maps predicted biomass values to various geographic locations in the field. In another example, information map 258 may be a vegetative index map and the variable sensed by in situ sensors 208 may be yield. The predictive map 264 may then be a predictive speed map that maps predicted harvester speed values to various geographic locations in the field.

In some examples, information map 258 is from a previous pass through the field during a previous operation and the data type is different than the data type captured by in situ sensors 208, but the data type in predictive map 264 is the same as the type of data collected by the in situ sensors 208 . For example, information map 258 may be a seed population map generated during planting and the variable sensed by in situ sensors 208 may be stem size. The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to various geographic locations in the field. In another example, information map 258 may be a seeding hybrid map and the variable sensed by in situ sensors 208 may be crop condition, such as stalled crop or crop kinked. The predictive map 264 may then be a predictive crop condition map that maps predicted crop condition values to various geographic locations in the field.

In some examples, the information map 258 is from a previous pass through the field during a previous operation and the data type is the same as the data type captured by in situ sensors 208 and the The type of data in the predictive map 264 is also the same as the type of data captured by the in situ sensors 208 . For example, information map 258 may be a yield map generated during a previous year and the variable sensed by in situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to various geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the previous year may be used by the predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences in the information map 258 and the yield values generated by In -situ sensors 208 are detected during the current harvesting process. The predictive model is then used by the predictive map generator 210 to create a predictive yield map.

In some examples, a predictive map 264 may be provided to the control zone generator 213 . The control zone generator 213 groups adjacent sections of an area into one or more control zones based on predictive map 264 data values associated with those adjacent sections. A control zone may include two or more contiguous sections of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time for changing a setting of controllable subsystems 216 may be insufficient to respond satisfactorily to changes in values contained in a map, such as predictive map 264. In this case, control zone generator 213 analyzes the map and identifies control zones, which have a defined size in order to take the response time of the controllable subsystems 216 into account. In another example, the control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem 216 or for groups of controllable subsystems 216. The control zones can be added to the predictive map 264 to obtain a predictive control zone map 265 . Thus, the predictive control zone map 265 may be similar to the predictive map 264, except that the predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263 as described herein may include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, functional predictive map 263 does not include control zones, such as predictive map 264. In another example, functional predictive map 263 includes control zones, such as the predictive control zone map 265. In some examples, multiple crop types may be present simultaneously in a field when a mixed crop production system is implemented. In this case, the predictive map generator 212 and the control zone generator 213 can identify the position and characteristics of the two or more crop types and then generate the predictive map 264 and the predictive control zone map 265 accordingly.

It should also be noted that the control zone generator 213 can group values to generate control zones and the control zones can be added to a predictive tax zone map 265 or a separate map showing only the generated control zones. In some examples, the control zones may be used to control or calibrate agricultural harvester 100, or both. In other examples, the control zones may be displayed to operator 260 and used to control or calibrate agricultural harvester 100, and in other examples, the control zones may be displayed to operator 260 or another user, or saved for later use.

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

Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218 . The operator interface control 231 is also operable to provide the operator 260 with the predictive map 264 or the predictive control zone map 265 or other information generated by or based on the predictive map 264, the predictive control zone map 265 or both derived. The operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 to operator 260 . Controller 231 may create operator actuable mechanisms that are displayed and operable by the operator to interact with the displayed map. The operator can edit the map by correcting, for example, a height of vegetation displayed on the map based on the operator's observation. The adjustment controller 232 may generate control signals to control various adjustments on the agricultural harvesting machine 100 based on the predictive map 264, the predictive control zone map 265, or both. For example, the adjustment controller 232 may generate control signals to control the machine and header actuators 248 . In response to the generated control signals, the machine and header actuators 248 operate to adjust, for example, one or more of the sieve and chopper settings, the concave gap, the rotor settings, the cleaning fan speed settings, the header height, the header functionality, the reel speed, the reel position, the draper functionality (wherein the agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control, and other actuators 248 that affect the other functions of the agricultural harvester 100 to control. Path planning controller 234 illustratively generates control signals to control steering subsystem 252 to steer agricultural harvester 100 according to a desired path. Path planning controller 234 may control a path planning system to generate a route for agricultural harvester 100 and may control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along that route. Feedrate controller 236 may control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control feedrate based on predictive map 264 or predictive control zone map 265, or both. For example, when agricultural harvester 100 approaches a weed patch having an intensity value above a selected threshold, feed rate controller 236 may reduce the speed of machine 100 to maintain a constant feed rate of biomass through the machine. The header and reel controller 238 may generate control signals to control a header or reel or other header functionality. For example, reel position or header height may be adjusted, draper belt controller 240 may generate control signals to control a draper belt or other draper functionality based on predictive map 264, predictive control zone map 265, or both. The top plate position controller 242 may generate control signals to control a position of a top plate included in a header based on a predictive map 264 or a predictive control zone map 265 or both. The backlog system controller 244 may generate control signals to control a backlog subsystem 138 based on the predictive map 264 or the predictive control zone map 265 or both. The machine cleaning controller 245 may generate control signals to control the machine cleaning subsystem 254 . Other controls included in agricultural harvesting machine 100 may also control other subsystems based on predictive map 264 or predictive control zone map 265 or both.

the 3A and 3B (herein together as 3 ) show a flow chart illustrating an example of the operation of the agricultural harvesting machine 100 in generating a predictive map 264 and a predictive control zone map 265 based on the information map 258 .

At 280, agricultural harvester 100 receives information card 258. Examples of information card 258 or receiving information card 258 are discussed with respect to blocks 281, 282, 284, and 286. As discussed above, information map 258 maps values of a variable corresponding to a first feature to various locations in the field, as indicated at block 282 . As indicated at block 281, receiving the information card 258 may include selecting one or more of a plurality of possible information cards that are available. For example, an information map can be a vegetative index map generated from aerial photographs. Another map of information may be a map generated during a previous pass through the field that may have been performed by another machine that performed a previous operation on the field, such as a sprayer or other machine. The process of selecting one or more information cards can be manual, semi-automated, or automated. Information map 258 is based on data collected prior to a current harvest operation. This is indicated by block 284 displayed. For example, the data may be collected based on aerial photographs taken during a previous year or earlier in the current growing season or at other times. The data may be based on data detected by means other than aerial photography. For example, agricultural harvester 100 may be equipped with a sensor, such as an internal optical sensor, that identifies weed seeds exiting agricultural harvester 100 . Weed seed data collected by the sensor during a previous year's harvest may be used as data used to generate information map 258 . The collected weed data can be combined with other data to generate information map 258 . For example, based on a size of the weed seeds exiting the agricultural harvester 100 at various locations and other factors such as whether the seeds are spread through a spreader or dropped in a windrow; weather conditions such as wind when seeds are dropped or spread; drainage conditions that can move seeds around the field; or other information, the positions of these weed seeds are predicted such that the information map 258 depicts the predicted seed positions in the field. The data for the information card 258 can be transmitted to the agricultural harvesting machine 100 using the communication system 206 and stored in the data storage device 202 . The data for the information card 258 can also be provided in other ways using the communication system 206 of the agricultural harvesting machine 100, as indicated by block 286 in the flow chart of FIG 3 is shown. In some examples, information card 258 may be received by communication system 206 .

At the beginning of a harvesting operation, in situ sensors 208 generate sensor signals indicative of one or more in situ data values indicative of a feature, such as vegetation height, as indicated by block 288 . Examples of in situ sensors are discussed with respect to blocks 222, 290 and 226. As discussed above, the in situ sensors 208 include onboard sensors 222, such as a camera; remote in situ sensors 224, such as UAV based sensors flown at a time to collect in situ data as shown in block 290; or other types of in situ sensors, denoted by in situ sensors 226 . In some examples, data from onboard sensors is georeferenced using position, heading, or speed data from geographic location sensor 204 .

The predictive model generator 210 controls the information variable-to-in situ variable model generator 228 to generate a model representing a relationship between the mapped values contained in the information map 258 and the in situ values that by the in situ sensors 208 are modeled as indicated by block 292 . The features or data types represented by the mapped values in information map 258 and the in situ values sensed by in situ sensors 208 may be the same features or data types or different features or data types.

The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212 . The predictive map generator 212 generates a predictive map 264 that represents a value of the feature sensed by the in situ sensors 208 at various geographic locations in a field to be harvested or another feature that is consistent with the sensed by the in situ sensors 208 feature is predicted using the predictive model and information card 258, as indicated by block 294.

Note that in some examples, the information map 258 may include two or more different maps or two or more different map layers of a single map. Each map layer may represent a different type of data than the data type of another map layer, or the map layers may have the same type of data obtained at different points in time. Each map in the two or more different maps, or each layer in the two or more different map layers of a map, maps a different type of variable to the geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the in situ data and each of the various variables depicted by the two or more different maps or the two or more different map layers. Likewise, the in situ sensors 208 may include two or more sensors, each sensing a different type of variable. Thus, the predictive model generator 210 creates a predictive model that models the relationships between each variable type represented by the information map 258 and each variable type sensed by the in situ sensors 208 . The predictive map generator 212 may generate a functional predictive map 263 that includes a value for each sensed feature sensed by the in situ sensors 208 (or a trait related to the detected trait) at various locations in the field being harvested using the predictive model and each of the maps or map planes in information map 258.

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

The control zone generator 213 may divide the predictive map 264 into control zones based on the values on the predictive map 264 . Continuously geolocated values that are within a threshold of each other can be grouped into a tax zone. The threshold may be a default threshold, or the threshold may be set based on operator input, based on input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system 214, the controllable subsystems 216 based on wear considerations, or other criteria, as indicated by block 295. The predictive map generator 212 configures the predictive map 264 for presentation to an operator or other user. The control zone generator 213 can configure a predictive control zone map 265 for presentation to an operator or other user. Block 299 indicates this. When presented to an operator or other user, the representation of predictive map 264 or predictive control zone map 265, or both, may include one or more of the predictive values on predictive map 264 that are correlated to the geographic location, the control zones on the predictive Control zone map 265 correlated to geographic location and containing adjustment values or control parameters used based on the predicted values on map 264 or the zones on predictive control zone map 265. In another example, the presentation may include more abstracted information or more detailed information. The representation may also include a confidence level that indicates an accuracy with which the predicted values on the predictive map 264 or the zones on the predictive control zone map 265 match measured values that can be measured by sensors on the agricultural harvesting machine 100 when moving the agricultural harvesting machine 100 through the field. Furthermore, an authentication and authorization system can be provided that implements authentication and authorization processes when information is presented at multiple locations. For example, there may be a hierarchy of people who are authorized to view and change maps and other presented information. For example, an onboard display device may display the maps locally on the machine in near real time, or the maps may also 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 what indicator marks are visible on the physical display device and what values that person can change. For example, a local operator of machine 100 may not be able to view the information corresponding to predictive map 264 or make changes to machine operation. However, a supervisor, such as a supervisor at a remote location, can see the predictive map 264 on the display but be prevented from making changes. A manager, who may be at a separate remote location, may be able to see all of the items on the predictive map 264 and may also be able to change the predictive map 264. In some cases, the predictive map 264, accessible and modifiable by a remote manager, can be used in machine control. This is an example of an authorization hierarchy that can be implemented. The predictive map 264 or the predictive control zone map 265 or both can also be configured in other ways, as indicated by block 297 .

At block 298, inputs from the geographic position sensor 204 and other in situ sensors 208 are received by the control system. In particular, at block 300 the control system 214 detects an input from the geographic position sensor 204 identifying a geographic position of the agricultural harvester 100 . Block 302 represents the receipt of sensor inputs by the control system 214 indicative of the trajectory or heading of the agricultural harvester 100, and block 304 represents the receipt by the control system 214 of a speed of the agricultural harvester 100. Block 306 represents the receipt of other information from various in situ sensors 208 by the control system 214.

At block 308 the control system 214 generates control signals to control the controllable subsystems 216 based on the predictive map 264 or the predictive control zone map 265 or both and the input from the geographic position sensor 204 and any other in situ sensors 208 . At block 310, the control system 214 applies the control signals to the controllable subsystems. It is understood that the particular control signals that are generated and the particular controllable subsystems 216 that are controlled may vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 that are controlled may be based on the type of predictive map 264 or predictive control zone map 265 or both used. Likewise, the control signals generated, the controllable subsystems 216 that are controlled, and the timing of the control signals may be based on various latencies of crop flow through the agricultural harvesting machine 100 and the responsiveness of the controllable subsystems 216.

For example, a generated predictive map 264 in the form of a predictive vegetation height map may be used to control one or more subsystems 216 . For example, the predictive vegetation height map may include crop plant height, weed plant height, or both values georeferenced to positions within the field to be harvested. The vegetation height values from the predictive vegetation height map can be extracted and used to control the header and reel subsystems. A variety of other control signals may be generated using values obtained from a predictive vegetation height map or other type of predictive map to control one or more of the controllable subsystems 216 .

At block 312, it is determined whether the harvesting operation has been completed. If harvesting is not complete, processing continues at block 314 where in situ sensor data from geographic location sensor 204 and in situ sensors 208 (and possibly other sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 may also recognize learning trigger criteria to perform machine learning on one or more of predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by generated by control zone generator 213, one or more control algorithms implemented by controllers in control system 214, and other triggered learning.

The learning trigger criteria can contain a large number of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322, and 324. For example, in some examples, the triggered learning may include restoring a relationship used to generate a predictive model when a threshold amount of in situ sensor data is obtained from the in situ sensors 208 . In such examples, receipt of an amount of in situ sensor data from in situ sensors 208 that exceeds a threshold triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212 will. Thus, as agricultural harvester 100 continues a harvesting operation, receipt of the threshold amount of in situ sensor data from in situ sensors 208 triggers the generation of a new relationship represented by a predictive model generated by predictive model generator 210 is produced. Furthermore, the new predictive map 264, the predictive control zone map 265, or both can be regenerated using the new predictive model. Block 318 represents detecting a threshold set of in situ sensor data used to trigger creation of a new predictive model.

In other examples, the learning trigger criteria may be based on how much the in situ sensor data from the in situ sensors 208 is changing, such as over time or compared to previous values. For example, if deviations within the in situ sensor data (or the relationship between the in situ sensor data and the information in information card 258) are within a selected range or are less than a defined amount or below a threshold, then no new predictive model generated by the predictive model generator 210 . As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if deviations within the in situ sensor data are outside the selected range, greater than the defined amount, or at e.g. above the threshold, the predictive model generator 210 generates a new predictive model using all or part of the newly received in situ sensor data, which the predictive map generator 212 uses to generate a new predictive map 264. At block 320, variations in the in situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation in the relationship between the in situ sensor data and the information in information map 258, used as a trigger to cause the generation of a new predictive model and map. Keeping the examples described above, the threshold, range and defined amount can be set to default values; set by an operator or user interaction via a user interface; set by an automated system; or adjusted in any other way.

Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different information card (different from the originally selected information card 258), switching to the different information card may require relearning by the predictive model generator 210, the predictive card generator 212, the control zone generator 213, the Trigger control system 214 or other elements. In a further example, the transition of the agricultural harvesting machine 100 to a different topography or to a different control zone can also be used as learning trigger criteria.

In some cases, operator 260 may also edit predictive map 264 or predictive control zone map 265, or both. The edits may change a value on predictive map 264, a size, shape, location, or presence of a control zone on predictive control zone map 265, or both. Block 321 shows that processed information can be used as learning trigger criteria.

Also, in some cases, the operator 260 may observe that automated control of a controllable subsystem is not what the operator desires. In such cases, the operator 260 may provide the controllable sub-system with a manual adjustment that reflects the operator 260 wanting the controllable sub-system to operate differently than is commanded by the control system 214 . Thus, a manual change of a setting by the operator 260 may cause one or more of the predictive model generator 210 to relearn a model, the predictive map generator 212 to regenerate the map 264, the control zone generator 213 to create one or more control zones on the regenerate predictive control zone map 265, and control system 214 to relearn a control algorithm or perform machine learning on one or more of control components 232-246 in control system 214 based on adjustment by operator 260, as shown in block 322. Block 324 represents the use of other triggered learning criteria.

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

When relearning is triggered, whether based on learning trigger criteria or based on the passage of a time interval, as indicated by block 326, one or more of the predictive model generator 210, the predictive map generator 212, the control zone generator 213 and the control system 214 performs machine learning to generate a new predictive model, map, control zone, or control algorithm based on the learning trigger criteria. The new predictive model, map and control algorithm are generated using additional data collected since the last learning. Performing the relearn is indicated by block 328 .

When the harvesting process has been completed, the process proceeds from block 312 to block 330 where one or more of the predictive map 264, the predictive control zone map 265, and the predictive model generated by the predictive model generator 210 are stored. The predictive map 264, predictive control zone map 265, and predictive model may be stored locally on data storage 202 or sent to a remote system using communication system 206 for later use.

It should be noted that while some examples herein describe the predictive model generator 210 and the predictive map generator 212 showing an information map when generating a predictive model or receiving a functional predictive map, the predictive model generator 210 and the predictive map generator 212 in other examples different types of maps when creating a predictive model and a functional predictive map eventually predictive maps, such as a functional predictive map generated during the harvesting process.

4 is a block diagram of a portion of the in 1 shown agricultural harvesting machine 100. In particular 4 among other things, examples of the predictive model generator 210 and the predictive map generator 212 in more detail. 4 also illustrates the flow of information between the various components that are shown. The predictive model generator 210 receives one or more of a vegetative index map 331, a crop condition map 332, and a yield map 333 as an information map. The predictive model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The in situ sensors 208 include, by way of example, a vegetation height sensor, such as the vegetation height sensor 336, the operator command input sensor 337, and a processing system 338. In some In some cases, vegetation height sensor 336 may be onboard agricultural harvester 100 . In other examples, vegetation height sensor 336 is remote from agricultural harvester 100 . The processing system 338 processes sensor data generated by the onboard vegetation height sensor 336 to generate processed data, some examples of which are described below.

In some examples, vegetation height sensor 336 may be an optical sensor, such as a camera, that generates images of an area of a field to be harvested. In some cases, the optical sensor may be arranged on the agricultural harvester 100 to collect images of an area in front of the agricultural harvester 100 as the agricultural harvester 100 moves through the field during a harvesting operation. Processing system 338 processes one or more sensor signals obtained via vegetation height sensor 336 to generate processed sensor data identifying vegetation height in the image.

In other examples, vegetation height sensor 336 may be other types of sensors. For example, vegetation height sensor 336 could include an ultrasonic sensor. For example, vegetation height sensor 336 could include an electromagnetic sensor such as lidar.

In some examples, operator command input sensor 337 may sense operator commands indicative of an operator commanded reel position. Reel position can include both reel height and reel forward-backward positions. The operator command input sensor 337 can detect commands from a human operator as well as from some type of semi or fully automatic operator.

As in 4 As shown, the example predictive model generator 210 includes one or more of a vegetation height to vegetation index model generator 342, an operator command to vegetation index model generator 343, an operator command to yield model generator 345, an operator command to yield model generator 347, a vegetation height-to-yield model generator 349, and a vegetation height-to-yield model generator 351. In other examples, the predictive model generator 210 may include additional, fewer, or different components than those in the example of FIG 4 provided. Accordingly, in some examples, the predictive model generator 210 may also include other elements 348, which may include other types of predictive model generators to generate other types of vegetation height models.

The model generator 342 identifies a relationship between a vegetation height value in the sensor data 340 at a geographic location that corresponds to the location at which the sensor data 340 was obtained and vegetative index values from the vegetative index map 332 that correspond to the same location in the field at which the vegetation height values were recorded. Based on this relationship established by the model generator 342, the model generator 342 generates a predictive model 350. The predictive vegetation height model is used by the vegetation height map generator 352 to generate vegetation height values at various locations in the field based on the georeferenced vegetative index values contained in the vegetative index map 331 contained at the same positions in the field.

The model generator 343 identifies a relationship between operator command values indicative of a reel position recognized in processed data 340 at a geographic location corresponding to where the processed data 340 was geolocated and vegetative index values from the vegetative index map 331 corresponding to the same correspond to the position on the field where the operator command values were detected. Based on this relationship established by the model generator 343, the model generator 343 generates a predictive model 350. The predictive model 350 is used by the reel position map generator 353 to generate reel position values at various positions in the field based on the georeferenced vegetative index values contained in the vegetative index map 331 at the same positions in the field.

The model generator 345 identifies a relationship between operator command values indicative of a reel position recognized in processed data 340 at a geographic location corresponding to where the processed data 340 was geolocated and yield values from yield map 333 corresponding to the same position correspond to the field at which the operator command values were recognized. Based on this relationship established by the model generator 345, the model generator 345 generates a predictive model 350. The predictive model 350 is used by the reel position map generator 353 to generate reel position values at various positions in the field based on the georeferenced yield values stored in the Yield map 333 included at the same positions in the field.

The model generator 347 identifies a relationship between operator command values indicative of a reel position recognized in processed data 340 at a geographic location corresponding to where the processed data 340 was geolocated and crop condition values from the crop condition map 332 corresponding to the same location correspond to the field at which the operator command values were recognized. Based on this relationship established by the model generator 347, the model generator 347 generates a predictive model 350. The predictive model 350 is used by the reel position map generator 353 to generate reel position values at various positions in the field based on the georeferenced crop condition values stored in the Crop condition map 332 are contained at the same positions in the field to predict.

The model generator 349 identifies a relationship between a vegetation height value in sensor data 340 at a geographic location that corresponds to the location where the sensor data 340 was obtained and yield values from the yield map 333 that correspond to the same location on the field where the vegetation height values were detected became. Based on this relationship established by the model generator 349, the model generator 349 generates a predictive model 350. The predictive vegetation height model is used by the vegetation height map generator 352 to generate vegetation height values at various locations in the field based on the georeferenced yield values contained in the yield map 333 contained at the same positions in the array.

The model generator 351 identifies a relationship between a vegetation height value in the sensor data 340 at a geographic location that corresponds to where the sensor data 340 was obtained and crop condition values from the crop condition map 332 that correspond to the same location on the field where the vegetation height values were detected became. Based on this relationship established by the model generator 351, the model generator 351 generates a predictive model 350. The predictive vegetation height model is used by the vegetation height map generator 352 to generate vegetation height values at various locations in the field based on the georeferenced crop condition values contained in the crop condition map 332 contained at the same positions in the array.

The predictive model 350 is provided to the predictive map generator 212 . In the example of 4 predictive map generator 212 includes vegetation height map generator 352 and reel position map generator 353. In other examples, predictive map generator 212 may include additional or different map generators. Thus, in some examples, the predictive map generator 212 may include other elements 358, which may include other types of map generators to generate maps for other types of features. The vegetation height map generator 352 receives the predictive vegetation height model 350 and generates a predictive map that predicts vegetation height at various locations based on values from the vegetative index map 331 in the field.

The predictive map generator 212 may output a vegetation height map 360 that predicts vegetation height. The generated predictive vegetation height map 360 may be provided to control zone generator 213, control system 214, or both. The control zone generator 213 can generate control zones and integrate these control zones into the functional predictive map 360 . One or more functional predictive maps may be provided to the control system 214 that generates control signals to control one or more of the controllable subsystems 216 based on the one or more functional predictive maps 360 .

The predictive map generator 212 may output a reel position map 361 that predicts a reel position. The generated predictive reel position map 360 can be provided to the control zone generator 213, the control system 214, or both to be provided. The control zone generator 213 generates control zones and integrates these control zones into the functional predictive map, ie predictive map 361, to generate the predictive control zone map 265. One or both of functional predictive map 361 and predictive control zone map 265 may be provided to control system 214, which generates control signals to control one or more of controllable subsystems 216 based on functional predictive map 361, predictive control zone map 265, or both.

5 12 is a flow chart of an example of the operation of the predictive model generator 210 and the predictive map generator 212 in generating the predictive model 350 and the predictive vegetation height map 360 or the predictive reel position map 361 or both. At block 362, the predictive model generator 210 and the predictive map generator 212 receive one or more of a vegetative index map 331, a crop state map 332, and a predictive yield map 333. At block 364, the processing system 338 receives one or more sensor signals from a vegetation height sensor 336, an operator command input sensor 337 or both. As discussed above, vegetation height sensor 336 may be a camera, such as a forward-facing camera; or another type of vegetation height sensor 370. For example, a vegetation height sensor 336 could include lidar or radar sensor systems. At block 372, the processing system 338 processes the one or more received sensor signals to generate data indicative of vegetation height or reel position. The sensor data may also include other data, as indicated by block 376.

At block 382, the predictive model generator 210 also obtains the geographic position corresponding to the sensor data. For example, the predictive model generator 210 may obtain the geographic position from the geographic position sensor 204 and determine an accurate geographic position to which the vegetation height data or reel position data corresponds based on machine delays, machine speed, camera field of view, etc.

At block 384, the predictive model generator 210 generates one or more predictive models, such as predictive model 350, that establish a relationship between a value obtained from an information map, such as information map 258, and a vegetation height value measured by the in situ sensor 208 is detected or a related feature. For example, the predictive model generator 210 may generate a predictive vegetation height model that models the relationship between a vegetative index value and a detected vegetation height indicated by the sensor data obtained from the in situ sensor 208 .

At block 386, the predictive model, such as the predictive model 350, is provided to the predictive map generator 212, which generates a predictive vegetation height map 360 depicting a predictive vegetation height based on the predictive model 350 and the vegetative index map 331 or the predictive reel position map 361. mapping a predictive reel position based on the predictive model 350 and one or more of the vegetative index map 331, crop condition map 332, and yield map 333. The predictive vegetation height map 360 or the predictive reel position map 361 may be generated during a farming operation. Thus, when an agricultural harvester moves through a field in which a farming operation is being performed, the predictive vegetation height map 360 or the predictive reel position map 361 is generated while the farming operation is being performed.

At block 394 , predictive map generator 212 outputs predictive vegetation height map 360 or predictive reel position map 361 . At block 391, the predictive vegetation height map generator 212 outputs the predictive vegetation height map or the predictive reel position map for presentation to and possible interaction by the operator 260. At block 393, the predictive map generator 212 may configure the map for the control system 214 to consume. At block 395, the predictive map generator 212 may also provide the map 360 to the control zone generator 213 for generating control zones. At block 397, the predictive map generator 212 configures the predictive vegetation height map 360 or the predictive reel position map 361 in other ways as well. The predictive vegetation height map 360 (with or without the control zones) or predictive reel position map 361 (with or without the control zones) is provided to the control system 214 .

At block 396 , the control system 214 generates control signals to control the controllable subsystems 216 based on the predictive vegetation height map 360 or the predictive reel position map 361 . As indicated in block 400, reel height may be adjusted. For example, the reel height can be lowered to collect short crop plants. For example, the reel height can be increased to better collect tall crop plants.

As indicated by block 401, the reel forward-reverse position can be adjusted. For example, the reel can be moved forward to pick up sloping or twisted crops. For example, the reel can be moved backwards to avoid knocking over tall plants before they hit the cutter bar or to help short plants onto the draper belt.

A controllable subsystem 216 can also be controlled in other ways, as indicated by block 402 . For example, the reel speed can be controlled.

6 FIG. 12 is a block diagram illustrating an example control zone generator 213. FIG. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 may include other elements 492 as well. Control zone generation system 488 includes control zone criteria identification component 494, control zone boundary definition component 496, target setting identification component 498, and other elements 520. Regime zone generation system 490 includes regime zone criteria identification component 522, regime zone boundary definition component 524, setting resolver identification component 526, and other elements 528 , a brief description of some of the elements of the control zone generator 213 and their respective operation is first provided.

The agricultural harvesting machine 100 or other work machine may include a variety of different types of controllable actuators that perform different functions. The controllable actuators on agricultural harvesting machine 100 or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA can be independently controllable based on values on a functional predictive map, or the WMAs can be controlled as sets based on one or more values on a functional predictive map. Therefore, the control zone generator 213 can generate control zones corresponding to each individually controllable WMA or to the sets of WMAs that are controlled in concert with one another.

The WMA selector 486 selects a WMA or set of WMAs for which to create corresponding control zones. The control zone creation system 488 then creates the control zones for the selected WMA or set of WMAs. Different criteria may be used in identifying control zones for each WMA or group of WMAs. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how badly a particular actuator or mechanism wears as a result of its movement) may be used as a criterion for identifying control zone boundaries. The control zone criteria identification component 494 identifies certain criteria to be used in defining control zones for the selected WMA or set of WMAs. The control zone boundary definition component 496 processes the values on a functional predictive map being analyzed to define the boundaries of the control zones on that functional predictive map based on the values in the functional predictive map being analyzed and based on the control zone criteria for the selected WMA or define a set of WMAs.

The target setting identification component 498 sets a value of the target setting used to control the WMA or set of WMAs in different control zones. For example, if the selected WMA is the propulsion system 250 and the functional predictive map analyzed is a functional predictive speed map 438, the target setting in each control zone may be a target speed setting based on speed values contained in the functional predictive speed map 238 within the identified control zone .

In some examples where the agricultural harvester 100 is to be controlled based on a current or future position of the agricultural harvester 100, multiple target settings for a WMA at a particular position may be possible. In this case, the target settings can have different values and compete. Therefore, the target settings must be resolved so that only a single target setting can be used to control the WMA. For example, if the WMA is an actuator in drive system 250 that is being controlled to control the speed of agricultural harvesting machine 100, there may be several different competing sets of criteria used by control zone generation system 488 in identifying the control zones and target settings for the selected one WMA are taken into account in the control zones. For example, different target settings can help control machine speed for example, based on a detected or predicted feed rate value, a detected or predicted fuel efficiency value, a detected or predicted grain loss value, or a combination thereof. However, the agricultural harvester 100 cannot travel across the ground at multiple speeds simultaneously at any given time. Rather, the agricultural harvesting machine 100 travels at a single speed at all times. Thus, one of the competing target settings is selected to control the speed of the agricultural harvesting machine 100 .

In some examples, regime zone generation system 490 generates regime zones to resolve multiple different competing target settings. The regimen zone criteria identification component 522 identifies the criteria used to identify regimen zones for the selected WMA or set of WMAs on the functional predictive map to be analyzed. Some criteria that can be used to identify or define regime zones include, for example, crop type or crop variety based on a stand map or other source of crop type or crop variety, weed type, weed intensity, or crop condition, such as whether the crop is overturned, in part is twisted or standing. Just as each WMA or group of WMAs may have a corresponding control zone, different WMAs or groups of WMAs may have a corresponding regime zone. The regime zone boundary definition component 524 identifies the boundaries of regime zones on the functional predictive map being analyzed based on the regime zone criteria identified by the regime zone criteria identification component 522 .

In some examples, regime zones may overlap. For example, a crop variety regime zone may overlap with some or all of a crop variety regime zone. In such an example, the various regime zones may be assigned a precedent hierarchy such that when two or more regime zones overlap, the regime zone that has been assigned a greater hierarchical position or importance in the precedent hierarchy takes precedence over the regime zones that have a lower hierarchical position position or importance in the precedent hierarchy. The regime zone priority hierarchy may be set manually or automatically using a rule-based system, a model-based system, or some other system. As an example, if a kinked crop regime zone overlaps a crop variety regime zone, the kinked crop regime zone may be assigned greater importance in the hierarchy of precedence than the crop variety regime zone, such that the kinked crop regime zone takes precedence.

In addition, each regime zone may have a unique setting resolver for a specific WMA or set of WMAs. The adjustment resolver identification component 526 identifies a particular adjustment resolver for each regimen zone identified on the functional predictive map to be analyzed and a particular adjustment resolver for the selected WMA or set of WMAs.

• Wenn vorhergesagte Biomassewerte innerhalb von 6 Metern (20 Fuß) vom Erntevorsatz der landwirtschaftlichen Erntemaschine 100 größer als x Kilogramm sind (wobei x ein ausgewählter oder vorbestimmter Wert ist), dann ist der Zieleinstellwert zu verwenden, der auf Grundlage der Vorschubgeschwindigkeit gegenüber anderen konkurrierenden Zieleinstellungen ausgewählt wird, andernfalls ist der Zieleinstellwert auf Grundlage des Kornverlusts gegenüber anderen konkurrierenden Zieleinstellwerten zu verwenden.

Once the attitude resolver is identified for a particular regime zone, that attitude resolver can be used to resolve competing target attitudes, identifying more than one target attitude based on the control zones. The different types of setting resolvers can have different shapes. For example, the setting resolvers identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the attitude resolver may include a neural network or other artificial intelligence or machine learning system. In such cases, the setting resolvers may resolve the competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. For example, an increased vehicle speed setting can reduce the time to harvest a field and reduce corresponding time-based labor costs and equipment costs, but can increase grain losses. A reduced vehicle speed setting can increase the time to harvest a field and increase the corresponding time-based labor and equipment costs, but can reduce grain losses. If grain loss or harvest time is selected as the quality metric, given the two competing vehicle speed setting values, the predicted or historical value for the selected quality metric can be used to resolve the speed setting. In some cases, the adjustment resolvers can be a set of threshold rules that can be used instead of or in addition to the regime zones. An example of a threshold rule can be expressed as follows:

• If predicted biomass values within 6 meters (20 feet) of the header of agricultural harvester 100 are greater than x kilograms (where x is a selected or predetermined value), then use the target setting selected based on feed rate versus other competing target settings otherwise use the target setting based on grain loss versus other competing target setting.

The attitude resolvers can be logical components that execute logical rules in identifying a target attitude. For example, the attitude resolver may resolve target settings while attempting to minimize harvest time, or minimize total harvest cost, or maximize grain harvested, or based on other variables calculated depending on the various target settings in question. Harvest time can be minimized when an amount to complete a harvest is reduced to a selected threshold or below. Total harvesting costs can be minimized when the total harvesting costs are reduced to or below a selected threshold. Crop can be maximized when the amount of crop is increased to or above a selected threshold.

7 12 is a flowchart illustrating an example of the operation of control zone generator 213 in generating control zones and regime zones for a map that control zone generator 213 receives for zone processing (e.g., for a map that is being analyzed).

At block 530, the control zone generator 213 receives a map that is analyzed for processing. In one example, as shown at block 532, the map analyzed is a functional predictive map. For example, the map analyzed may be one of functional predictive maps 436, 437, 438, or 440. Block 534 indicates that the card to be analyzed can be other cards.

At block 536, the WMA selector 486 selects a WMA or set of WMAs for which to create control zones on the analyzed map. At block 538, the control zone criteria identification component 494 obtains control zone definition criteria for the selected WMAs or set of WMAs. Block 540 provides an example where the control zone criteria is or includes wear characteristics of the selected WMA or set of WMAs. Block 542 provides an example where the control zone definition criteria is or includes magnitude and variation of input source data, such as magnitude and variation of values on the analyzed map or magnitude and variation of inputs from various in situ sensors 208. Block 544 gives an example where the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which various subsystems operate, or other physical machine characteristics. Block 546 provides an example where the control zone definition criteria is or includes a responsiveness of the selected WMA or set of WMAs in meeting newly commanded adjustment values. Block 548 provides an example where the control zone definition criteria are or include machine performance metrics. Block 550 provides an example where the control zone definition criteria is or includes operator preferences. Block 552 provides an example where the tax zone definition criteria is or includes other elements. Block 549 provides an example where the tax zone definition criteria are time-based, meaning that agricultural harvester 100 does not cross the boundary of a tax zone until a selected period of time has elapsed since agricultural harvester 100 entered a particular tax zone. In some cases, the selected length of time may be a minimum length of time. Thus, in some cases, the control zone definition criteria may prevent agricultural harvester 100 from crossing a boundary of a control zone until at least the selected period of time has elapsed. Block 551 provides an example where the tax zone definition criteria is based on a selected size value. For example, a tax zone definition criteria based on a selected size value may preclude the definition of a tax zone that is smaller than the selected size. In some cases, the size selected may be a minimum size.

At block 554, regimen zone criteria identification component 522 obtains regimen zone definition criteria for the selected WMA or set of WMAs. Block 556 provides an example where the regime zone definition criteria are based on manual input from operator 260 or another user. Block 558 illustrates an example where the regime zone definition criteria are based on crop type or crop variety. Block 560 illustrates an example where the regimen zone definition criteria are based on weed species or weed intensity or both. Block 562 illustrates an example where the regime zone definition criteria are based on or include crop status. Block 564 gives an example in which the regimes zone definition criteria are or include other criteria.

At block 566, the tax zone boundary definition component 496 creates the boundaries of tax zones on the map to be analyzed based on the tax zone criteria. The regime zone boundary definition component 524 creates the boundaries of regime zones on the map to be analyzed based on the regime zone criteria. Block 568 provides an example in which the zone boundaries for the control zones and regime zones are identified. Block 570 shows that the target setting identification component 498 identifies the target settings for each of the zones of control. The control zones and regime zones may be created in other ways, as indicated by block 572.

At block 574 , the adjustment resolver identification component 526 identifies the adjustment resolver for the selected WMAs in each regime zone defined by the regime zone boundary definition component 524 . As discussed above, the regimen zone resolver may be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a predicted or historical quality-based resolver 580 for each competing target setting, a rule-based resolver 582, a performance criteria-based resolver 584, or other resolver 586 be.

At block 588, the WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs remain to be processed, processing returns to block 436 where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. If no additional WMAs or sets of WMAs remain for which to generate control zones or regime zones, processing moves to block 590 where the control zone generator 213 outputs a map of control zones, target settings, regime zones, and setting resolvers for each of the WMAs or sets of WMAs . As discussed above, the issued card may be presented to operator 260 or another user; the card issued may be provided to the control system 214; or the issued card may be otherwise issued.

8th 12 illustrates an example of the operation of the control system 214 in controlling the agricultural harvesting machine 100 based on a map output from the control zone generator 213 . Thus, at block 592, the control system 214 receives a map of the work location. In some cases, as represented by block 594, the map may be a functional predictive map that may include control zones and regime zones. In some cases, the received map may be a functional predictive map excluding tax zones and regime zones. Block 596 provides an example where the received worksite map may be an information map with tax zones and regime zones identified thereon. Block 598 provides an example where the received map may include multiple different maps or multiple different map layers. Block 610 provides an example where the received map may take other forms as well.

At block 612, the control system 214 receives a sensor signal from the geographic position sensor 204. The sensor signal from the geographic position sensor 204 may include data indicating the geographical position 614 of the agricultural harvester 100, the speed 616 of the agricultural harvester 100, the heading 618 of the agricultural harvester 100 or provide other information 620 . At block 622, the zone controller 247 selects a regime zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geographic position sensor signal. At block 626, the zone controller 247 selects a WMA or set of WMAs to control. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from different sources. For example, block 630 depicts an example where one or more of the target settings for the selected WMA or set of WMAs is based on input from the tax zones on the worksite map. Block 632 depicts an example where one or more of the target settings are obtained from human input from the operator 260 or another user. Block 634 shows an example where the target settings are obtained from an in situ sensor 208 . Block 636 depicts an example where the one or more target settings are obtained from one or more sensors on other machines operating in the same field, either concurrently with agricultural harvesting machine 100 or from one or more sensors on machines operating in the past worked in the same field. Block 638 shows an example where the target settings are also obtained from other sources.

At block 640, the zone controller 247 accesses the setting resolver for the selected one Regimezone and controls the setting resolver to resolve competing target settings into one resolved target setting. As discussed above, in some cases, the setting resolver may be a human resolver, with zone controller 247 controlling operator interface mechanisms 218 to present the competing target settings to operator 260 or another user for resolution. In some cases, the attitude resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 communicates the competing target attitudes to the neural network, artificial intelligence or machine learning system for selection. In some cases, the attitude resolver may be based on a predicted or historical quality metric, threshold rules, or logical components. In one of these latter examples, the zone controller 247 executes the adjustment resolver to obtain a resolved target adjustment based on the predicted or historical quality metric, based on the threshold rules, or using the logical components.

At block 642, if the zone controller 247 has identified the resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214, which generate and apply control signals based on the resolved target setting to the selected WMA or set of WMAs. For example, if the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the adjustment controller 232 or the header/reel controller 238 or both to generate control signals based on the resolved target setting, and these generate control signals are applied to the machine or header actuators 248. If at block 644 additional WMAs or additional sets of WMAs are to be controlled at the current geographic position of agricultural harvester 100 (as detected at block 612), processing returns to block 626 where the next WMA or set of WMAs is selected. The processes represented by blocks 626 through 644 continue until all WMAs or sets of WMAs that are to be controlled at the current geographic position of agricultural harvesting machine 100 have been addressed. If there are no additional WMAs or sets of WMAs to control at the current geographic location of agricultural harvester 100, processing proceeds to block 646 where zone controller 247 determines whether there are additional control zones to consider in the selected regime zone. If there are additional tax zones to consider, processing returns to block 624 where a next tax zone is selected. If there are no more additional control zones to consider, processing transfers to block 648 which determines whether there are any additional regime zones to consider. The zone controller 247 determines whether additional regimen zones are to be considered. If there are more regimen zones to consider, processing returns to block 622 where a next regimen zone is selected.

At block 650, zone controller 247 determines whether the operation that agricultural harvester 100 is performing is complete. If not, as indicated by block 652, the zone controller 247 determines whether a control zone criteria has been met to continue processing. For example, as noted above, the control zone definition criteria may include criteria that define when a control zone boundary may be crossed by agricultural harvesting machine 100 . For example, a selected period of time may define whether a control zone boundary may be crossed by agricultural harvester 100, meaning that agricultural harvester 100 is prevented from crossing a zone boundary until a selected period of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has expired. In addition, the zone controller 247 can perform the processing continuously. Thus, the zone controller 247 does not wait a certain period of time before proceeding to determine whether operation of the agricultural harvesting machine 100 is complete. At block 652, zone controller 247 determines that it is time to continue processing and then continues processing at block 612 where zone controller 247 receives input from geographic position sensor 204 again. It should also be noted that the zone controller 247 may control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller, rather than sequentially controlling the WMAs and sets of WMAs.

9 FIG. 14 is a block diagram showing an example of an operator interface controller 231. FIG. In an illustrated example, operator interface control 231 includes operator input command processing system 654, another control interaction system 656, voice processing system 658, and action signal generator 660. Operator input command processing system 654 includes voice processing system 662 that handles touch gestures Training system 664 and other elements 666. The other control interaction system 656 includes the control input processing system 668 and the control output generator 670. The language processing system 658 includes the trigger detector 672, the recognition component 674, the synthesis component 676, the natural language understanding system 678, the dialogue management system 680 and others Elements 682. The action signal generator 660 includes the visual control signal generator 684, the audio control signal generator 686, the haptic control signal generator 688, and other elements 690. Before the operation of the in 9 While the operator interface control 231 shown is described in handling various operator interface actions, a brief description of some of the elements of the operator interface control 231 and the operation associated therewith is first provided.

Operator input command processing system 654 recognizes operator inputs at operator interface mechanisms 218 and processes those inputs for commands. Speech processing system 662 recognizes speech input and processes the interactions with speech processing system 658 to process the speech input for commands. Touch gesture handler system 664 recognizes touch gestures on touch-sensitive elements in operator interface mechanisms 218 and processes those inputs for commands.

The other controller interaction system 656 handles interactions with other controllers of the control system 214. The controller input processing system 668 recognizes and processes inputs from other controllers in the control system 214, and the controller output generator 670 generates outputs and provides those outputs to other controllers in the control system 214. Speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides an output that indicates the meaning of the spoken inputs. For example, voice processing system 658 may recognize voice input from operator 260 as a setting change command, in which operator 260 commands control system 214 to change a setting for controllable subsystem 216 . In such an example, voice processing system 658 recognizes the content of the spoken command, identifies the meaning of that command as a setting change command, and provides the meaning of that input back to voice processing system 662. Voice processing system 662, in turn, interacts with control output generator 670 to provide the commanded output to the provide appropriate control in control system 214 to comply with the spoken setting change command.

The language processing system 658 can be invoked in a number of ways. For example, voice processing system 662 continuously provides input from a microphone (which is one of operator interface mechanisms 218) to voice processing system 658 in one example. The microphone detects speech from the operator 260 and the speech processing system 662 provides the speech processing system 658 with the recognized speech. Trigger detector 672 detects a trigger indicating that language processing system 658 is invoked. In some cases, when speech processing system 658 receives continuous speech input from speech processing system 662, speech recognition component 674 performs continuous speech recognition on all speech spoken by operator 260. In some cases, the language processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the speech processing system 658 may be initiated based on recognition of a selected spoken word, referred to as a wake-up word. In such an example, where the detection component 674 detects the wakeup word, the detection component 674 provides an indication that the wakeup word was detected to trigger the detector 672 . The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, language processing system 658 may be invoked by an operator 260 operating an actuator on a user interface mechanism, such as by touching an actuator on a touch-sensitive display screen, pressing a button, or providing another trigger input. In such an example, trigger detector 672 may detect that language processing system 658 has been invoked when a trigger input is detected via a user interface mechanism. Trigger detector 672 may also detect that speech processing system 658 has been invoked in other ways.

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

In some examples, voice processing system 658 may also generate outputs that navigate operator 260 through a user experience based on voice input. For example, the dialog management system 680 can create and manage a dialog with the user to identify what the user wants to do. Dialogue can make a user's command unmistakable; identify one or more specific values required to execute the user's command; or receive other information from the user or provide other information to the user or both. Synthesis component 676 may generate speech synthesis that may be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialogue managed by dialogue management system 680 may be spoken dialogue only or a combination of both visual dialogue and spoken dialogue.

Action signal generator 660 generates action signals to control operator interface mechanisms 218 based on outputs from one or more of operator input command processing system 654, other control interaction system 656, and language processing system 658. Visual control signal generator 684 generates control signals to control visual elements in operator interface mechanisms 218 . The visual elements can be lights, a display screen, warning indicators, or other visual elements. Audio control signal generator 686 generates outputs that control audio elements of operator interface mechanisms 218 . The audio elements include a speaker, audible alarm mechanisms, horns, or other audible elements. The haptic control signal generator 688 generates control signals that are output to control haptic elements of the operator interface mechanisms 218 . The haptic elements include vibration elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals or joysticks used by the operator. The haptics may include tactile feedback or force feedback elements that provide a tactile feedback or force feedback signal to the operator via operator interface mechanisms. The haptics may also include a variety of other haptics.

10 12 is a flow chart illustrating an example of the operation of operator interface controller 231 in generating an operator interface display on operator interface mechanism 218, which may include a touch-sensitive display screen. 10 also illustrates an example of how operator interface controller 231 may recognize and process operator interactions with the touch-sensitive display screen.

At block 692, operator interface controller 231 receives a card. Block 694 gives an example where the map is a functional predictive map and block 696 gives an example where the map is another map type. At block 698 , operator interface controller 231 receives input from geographic position sensor 204 identifying the geographic position of agricultural harvester 100 . As indicated in block 700 , the input from geographic position sensor 204 may include heading along with the position of agricultural harvester 100 . Block 702 provides an example where the input from geographic location sensor 204 includes the speed of agricultural harvester 100, and block 704 provides an example where the input from geographic location sensor 204 includes other items.

At block 706, visual control signal generator 684 in operator interface controller 231 controls the touch-sensitive display screen in operator interface mechanisms 218 to generate a display showing all or part of a field represented by the received card. Block 708 indicates that the displayed field may include a current position marker showing a current position of agricultural harvesting machine 100 relative to the field. Block 710 provides an example where the displayed field includes a next unit of work marker that identifies a next unit of work (or area in the field) in which agricultural harvesting machine 100 is operating. Block 712 provides an example in which the displayed field includes an upcoming area display section indicating areas that are yet to be processed by the agricultural harvesting machine 100 and block 714 indicates an example where the displayed field includes previously visited display sections representing areas of the field that agricultural harvesting machine 100 has already processed. Block 716 provides an example in which the displayed panel displays various features of the georeferenced location panel on the map. For example, if the received map is a vegetation height map, the displayed field may show the various vegetation heights present in the field that have been georeferenced within the displayed field. For example, if the received map is a reel location map, the field displayed may show the various positions of a reel georeferenced to positions within the field displayed. The mapped features may be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 provides an example where the displayed field also includes other items.

11 FIG. 7 is a pictorial representation showing an example of a user interface display 720 that may be generated on a touch-sensitive display screen. In other implementations, user interface display 720 may be generated on other types of displays. The touch-sensitive screen can be mounted in the cab of the agricultural harvesting machine 100 or on the mobile device or elsewhere. The user interface display 720 will be described prior to the description of the in 10 to move.

in the in 11 In the example shown, the user interface display 720 illustrates that the touch-sensitive display screen includes a display feature for operating a microphone 722 and a speaker 724 . Thus, the touch-sensitive display may be communicably coupled to microphone 722 and speaker 724 . Block 726 indicates that the touch-sensitive display screen may include a variety of operator interface control actuators, such as buttons, keyboards, soft keyboards, links, icons, switches, etc. The operator 260 may manipulate the operator interface control actuators to perform various functions.

in the in 11 In the example shown, user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which agricultural harvesting machine 100 is operating. Field display portion 728 is shown with a current position marker 708 corresponding to a current position of agricultural harvesting machine 100 in the portion of the field shown in field display portion 728 . In one example, the operator can control the touch-sensitive display to zoom into portions of panel display portion 728 or to pan or scroll panel display portion 728 to view different portions of the panel. A next unit of work 730 is shown as the area of the field just ahead of the current position marker 708 of the agricultural harvester 100 . The current position marker 708 may also be configured to identify the direction of travel of the agricultural harvesting machine 100, a travel speed of the agricultural harvesting machine 100, or both. In 13 the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 within the field, which can be used as an indication of a direction of travel of the agricultural harvester 100 .

The size of the next work unit 730 highlighted on the panel display portion 728 may vary based on a variety of different criteria. For example, the size of the next work unit 730 may vary depending on the ground speed of the agricultural harvester 100 . Thus, if the agricultural harvester 100 is moving faster, the range of the next working unit 730 may be larger than the range of the next working unit 730 if the agricultural harvester 100 is moving slower. Panel display portion 728 is also shown displaying previously visited area 714 and upcoming areas 712 . Previously visited areas 714 represent areas that have already been harvested, while upcoming areas 712 represent areas that have yet to be harvested. The panel display portion 728 is also shown, displaying various features of the panel. in the in 11 In the example shown, the map displayed is a reel position map. Therefore, a variety of different reel position marks are displayed on the panel display section 728 . There is a set of reel position indicator markers 732 that are displayed in the areas 714 already visited. There is also a set of reel position indicator marks 732 shown in the upcoming areas 712 and there is a set of reel position indicator marks 732 shown in the next work unit 730 . 11 Figure 12 shows that the reel position indicator markings 732 consist of different symbols indicating an area of similar reel position. in the in 3 example shown the ! symbol represents areas of high reel position; the * symbol represents areas of medium reel position; and the # symbol represents a low reel position area. Thus, the panel display portion 728 displays various measured or predicted reel positions located at various areas within the panel. As previously described, the indicator markings 732 may consist of various symbols, and as described below, the symbols may be any display feature, such as different colors, shapes, patterns, intensities, text, symbols, or other display features. In some cases, each position of the field may have an indicator mark associated with it. Thus, in some cases, an indicator mark may be provided at each location of the tile display portion 728 to identify the type of feature being imaged for any particular location of the tile. Accordingly, the present disclosure contemplates providing an indication marker, such as loss level indicator marker 732 (as described in the context of the present example in FIG 11 ) at one or more positions on the field display portion 728 to identify the type, degree, etc. of the displayed feature, thereby identifying the feature at the corresponding position in the displayed field.

In the example of 11 the user interface display 720 also has a control display section 738 . Control display portion 738 allows the operator to display information and interact with the display of user interface 720 in various ways.

For example, the actuators and indicator markers in section 738 may be displayed as individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. in the in 3 In the example shown, the display section 738 displays information for the three different reel positions corresponding to the three symbols mentioned above. The display portion 738 also includes a set of touch-sensitive actuators that the operator 260 can interact with by touch. For example, the operator 260 may touch the touch-sensitive actuators with a finger to activate the respective touch-sensitive actuator.

A flags column 739 shows flags that have been set automatically or manually. Flag actuator 740 allows operator 260 to mark a location and then add information indicating the reel location found at the current location. For example, when the operator 260 actuates the flag actuator 740 by touching the flag actuator 740, the touch gesture handler system 664 in the operator interface controller 231 identifies the position as a position where the agricultural harvester 100 was commanded to a high reel position. When the operator 260 touches the button 742, the touch gesture handler system 664 identifies the current location as a location where the agricultural harvesting machine 100 encountered a middle reel location. When the operator 260 touches the button 744, the touch gesture handler system 664 identifies the current location as a location where the agricultural harvesting machine 100 encountered a low reel position. The touch gesture handler system 664 also controls the visual control signal generator 684 to add an icon corresponding to the identified reel position on the field display portion 728 at a position that the user identifies before, after, or during actuation of the button 740, 742, or 744.

Column 746 displays the symbols corresponding to each category of reel position tracked on panel display portion 728 . The identifier column 748 shows the identifier (which may be a textual identifier or other identifier) that identifies the reel position category. Without limitation, the reel position symbols in column 746 and the identifiers in column 748 can include any indicator such as different colors, shapes, patterns, intensities, text, symbols, or other indicators. The values displayed in column 750 may be predicted reel position values or reel position values measured by in situ sensors 208 . In one example, operator 260 can select the particular portion of field display portion 728 for which the values in column 750 are to be displayed. Thus, the values in column 750 may correspond to values in display sections 712, 714, or 730. Column 752 indicates action thresholds. The action thresholds in column 752 may be thresholds corresponding to the measured values in column 750. If the measured values in column 750 meet the corresponding action thresholds in column 752, then the control system 214 takes the action identified in column 754. In some cases, a measured value may meet a corresponding action threshold by meeting or exceeding the corresponding action threshold. For example, in one example, operator 260 may select a threshold to change the threshold by touching the threshold in column 752 . Once selected, the operator 260 can change the threshold. The thresholds in Columns 752 may be configured to take the designated action if the measured value 750 exceeds the threshold, equals the threshold, or is below the threshold.

Likewise, the operator 260 can touch the action identifiers in column 754 to change the action to be taken. When a threshold is met, multiple actions can be taken. For example, at the bottom of column 754, "raise reel" and "lower reel" are identified as actions to be taken when the measured value in column 750 meets the threshold in column 752.

The actions that can be specified in column 754 can be any of a variety of different types of actions. For example, the actions may include a keep away action that, when performed, prevents the agricultural harvester 100 from continuing to harvest in an area. The actions may include a “change speed” action that, when performed, changes the ground speed of the agricultural harvesting machine 100 through the field. The actions may include a change setting action to change a setting of an internal actuator or another WMA or set of WMAs, or to implement a change setting action that changes a setting of a reel position. These are just examples, and a variety of other actions are contemplated here.

The indicator markers shown on the user interface display 720 can be controlled visually. Visually controlling the interface display 720 may be performed to capture the operator's 260 attention. For example, the indicator marks can be controlled to modify the intensity, color, or pattern with which the indicator marks are displayed. Additionally, the indicator markers can be controlled to blink. The changes described in the visual appearance of the indicator markings are provided by way of example. Consequently, other aspects of the visual appearance of the indicia may be altered. Thus, in various circumstances, the indicator markings may be modified in a desired manner, for example, to attract the operator's 260 attention.

Returning to the flow chart of 10 the description of the operation of the operator interface controller 231 continues. At block 760 , operator interface controller 231 recognizes an input setting of a flag and controls touch-sensitive user interface display 720 to display the flag on panel display portion 728 . The recognized input may be operator input, as indicated at 762 , or input from another controller, as indicated at 764 . At block 766 , operator interface controller 231 recognizes an in situ sensor input indicative of a measured feature of the field from one of in situ sensors 208 . At block 768, visual control signal generator 684 generates control signals to control user interface display 720 to display actuators, to modify user interface display 720, and to modify machine controls. For example, block 770 represents that one or more of the actuators for setting or modifying the values in columns 739, 746, and 748 may be displayed. Thus, the user can set flags and change the properties of those flags. For example, a user can change the reel position types and reel position identifiers according to the flags. Block 772 represents that action thresholds in column 752 are displayed. Block 776 represents the actions in column 754 being displayed and block 778 represents the measured in situ data in column 750 being displayed. Block 780 indicates that a variety of other information and actuators may be displayed on user interface display 720 as well.

At block 782 , operator input command processing system 654 captures and processes operator inputs corresponding to interactions with user interface display 720 performed by operator 260 . When the user interface mechanism on which user interface display 720 is displayed is a touch-sensitive display screen, interaction inputs with the touch-sensitive display screen by operator 260 may be touch gestures 784 . In some cases, the operator interaction inputs may be inputs using a point and click device 786 or other operator interaction inputs 788 .

At block 790, operator interface controller 231 receives signals indicative of an alarm condition. For example, block 792 indicates that signals may be received from control input processing system 668 indicating sensed values in column 750 meet threshold conditions present in column 752 . As previously explained, the threshold conditions may include values that are below a threshold, at a threshold, or above a threshold. Block 794 shows that the action signal generator 660 as In response to receiving an alarm condition, operator 260 may alert by using visual control signal generator 684 to generate visual alarms, by using audio control signal generator 686 to generate audio alarms, by using haptic control signal generator 688 to generate haptic alarms generate, or by using any combination thereof. Likewise, as indicated by block 796, controller output generator 670 may generate outputs to other controllers in control system 214 so that those controllers perform the appropriate action identified in column 754. Block 798 indicates that operator interface controller 231 may recognize and process alarm conditions in other ways.

Block 900 shows that language processing system 662 can recognize and process input that calls into language processing system 658 . Block 902 shows that performing language processing may include using dialog management system 680 to perform a dialog with operator 260 . Block 904 shows that speech processing may include providing signals to control output generator 670 so that control operations are performed automatically based on the speech inputs.

Table 1 below shows an example dialog between operator interface control 231 and operator 260 . In the example shown in Table 1, the wake-up word is "Johnny".

table 1

Operator: "Johnny, inform me of the vegetation height"

Operator interface control: "Current vegetation height is 900 centimeters."

Table 2 shows an example where speech synthesis component 676 provides an output to audio control signal generator 686 to provide audible updates on an intermittent or periodic basis. The interval between updates may be time based, such as every five minutes, or coverage or distance based, such as every two hectares, or exception based, such as when a measured value is greater than a threshold.

Table2

Operator interface control: "In the last 10 minutes, vegetation has averaged 900 centimeters."

Operator Interface Control: "The next 1 hectare is predicted to average 1200 centimeters"

The example shown in Table 3 illustrates that some actuators or user input mechanisms on touch-sensitive display 720 may be supplemented with voice dialogue. The example in Table 3 illustrates that the action signal generator 660 can generate action signals to automatically mark a high vegetation spot in the field to be harvested.

Table 3

Human: "Johnny, mark a tall vegetation area."

Operator interface control: "High vegetation area highlighted."

The example shown in Table 4 illustrates that the action signal generator 660 can dialogue with the operator 260 to start and stop marking a high crop plant location.

Table 4

Human: "Johnny, start marking the large crop plant spot."

Operator interface control: "High crop plant location marker."

Human: "Johnny, finish marking a high crop plant spot."

Operator interface control: "High crop plant location marking stopped."

The example shown in Table 5 illustrates that action signal generator 160 can generate signals to mark an area of low height weeds in a manner different from those shown in Tables 3 and 4.

Table 5

Human: "Johnny, mark the next 100 m as low height weed patches."

Operator interface control: "Next 100 m are marked as low height weed patches."

With reference to 10 Block 906 illustrates that operator interface controller 231 may otherwise detect and process conditions for outputting a message or other information. For example, the other controller interaction system 656 may recognize input from other controllers indicating that the operator 260 should be presented with alarms or output messages. Block 908 shows that the outputs can be audio messages. Block 910 shows that the outputs can be visual messages and block 912 shows that the outputs can be haptic messages. Until the operator interface controller 231 determines that the current harvesting operation is complete, as indicated by block 914, processing returns to block 698 where the geographical position of the harvester 100 is updated and processing continues as described above to the user interface display 720 To update.

Once the process is complete, any desired values that are displayed or have been displayed on the user interface display 720 can be saved. These values can also be used in machine learning to improve various sections of the predictive model generator 210, predictive map generator 212, control zone generator 213, control algorithms, or other elements. Block 916 indicates storage of the desired values. The values can be stored locally on the agricultural harvester 100 or the values can be stored at a remote server location or sent to another remote system.

Thus, it can be seen that an information map is obtained from an agricultural harvester showing vegetation index, crop condition or yield values at various geographic positions of a field to be harvested. An in situ sensor on the harvester detects vegetation height or reel position as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for various locations based on the values of vegetative index, crop condition or yield in the information map and vegetation height or reel position detected by the in situ sensor. A control system controls the controllable subsystem based on vegetation height or reel position in the predictive map.

A control value is a value on which an action can be based. A control value, as described herein, may include any value (or characteristics indicated by or derived from the value) that can be used in controlling agricultural harvesting machine 100 . A control value can be any value that indicates an agricultural characteristic. A control value can be a predictive value, a measured value, or a recognized value. A control value may include any of the values provided by a card, such as any of the cards described herein, for example, a control value may be a value provided by an information card, a value provided by a preliminary information card, or a Value provided by a predictive map, such as a functional predictive map. A control value may also include any of the characteristics indicated by or derived from the values sensed by any of the sensors described herein. In other examples, a control value may be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

Processors and servers have been mentioned in this discussion. In one example, the processors and servers include computer processors with associated memory and timing circuitry, not shown separately. The processors and servers are functional parts of the systems or devices that the processors and servers belong to and are activated by, and facilitate the functionality of the other components or elements in those systems.

A number of user interface displays were also discussed. The displays can take a number of different forms and can have a number of different user controlled operator interface mechanisms thereon. For example, the user-actuable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, and so on. The user actuatable operator interface mechanisms can also be actuated in a variety of ways. For example, the user actuable operator interface mechanisms may be via operator interface mechanisms such as a point and click device, trackball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, when the screen on which the user actuable operator interface mechanisms are displayed is a touch-sensitive screen, the user actuable operator interface mechanisms can be actuated with touch gestures. User actuatable operator interface mechanisms can also be actuated using voice commands with speech recognition functionality. Speech recognition can be implemented using a speech recognition device, such as a microphone, and software operable to recognize speech and execute commands based on the received speech.

A number of data stores were also discussed. It is pointed out that the data stores can each be divided into several data stores. In some examples, one or more of the data stores may be local to systems accessing the data stores, one or more of the data stores may be remote from a system using the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.

In addition, the figures show a series of blocks with functions associated with each block. It is noted that fewer blocks may be used to illustrate that functionality assigned to multiple different blocks is performed by fewer components. More blocks can also be used, illustrating that functionality can be distributed across multiple components. In different examples, some functionality may be added and some may be removed.

It should be noted that the discussion above has described a variety of different systems, components, logic and interactions. It is understood that any or all such systems, components, logic, and interactions are powered by hardware elements, such as processors, memory, or other processing components, including but not limited to artificial intelligence components, such as neural networks, some of which are described below can be implemented that perform the functions associated with those systems, components, logics or interactions. In addition, any or all of the systems, components, logic, and interactions may be implemented by software loaded into memory and then executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions may also be implemented through various combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of various structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

12 12 is a block diagram of agricultural harvester 600 shown in FIG 2 shown agricultural harvesting machine 100 may be similar. Agricultural harvester 600 communicates with elements in a remote server architecture 500. In some examples, remote server architecture 500 provides computing, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that provides the Services. In various examples, remote servers may provide the services over a wide area network, such as the Internet, using appropriate protocols. For example, remote server applications can be deployed over a wide area network and accessed through a web browser or other computer component. Software or components included in 2 shown and related data may be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center site or distributed to a variety of remote data centers. Remote server infrastructures can provide services across shared data centers, although the services appear to the user as a single point of entry. Thus, the components and functions described herein can be provided from a remote server at a remote site via a remote server architecture. Alternatively, the components and functions may be provided by a server, or the components and functions may be installed directly or otherwise on end devices.

in the in 3 illustrated example 12 illustrated example, some elements are those in 2 shown and these items are similarly numbered. 12 indicates in particular that the predictive model generator 210 or the predictive map generator 212 or both are indicated a server location 502 that is remote from the agricultural harvester 600. Therefore, the one in the example applies 12 shown agricultural harvester 600 via remote server location 502 to systems.

12 also illustrates another example of a remote server architecture. 12 shows that some elements of 2 may be located at a remote server location 502 while others may be located elsewhere. For example, data store 202 may be located at a separate site from site 502 and accessed through the remote server at site 502 . Regardless of where the elements are located, the elements can be accessed directly by the agricultural harvester 600 over a network, such as a wide area network or a local area network; the items may be hosted at a remote location by a service; or the items may be provided as a service or accessed via a connection service located at a remote location. In addition, data can be stored at any location and the stored data can be retrieved or forwarded to operators, users or systems. For example, physical carriers can be used instead of or in addition to electromagnetic radiation carriers. In some examples where network coverage is poor or non-existent, another machine, e.g. a tank truck or other mobile machine or vehicle, have an automatic, semi-automatic or manual information collection system. When the combine harvester apparatus 600 gets close to the machine containing the information gathering system, such as a tanker truck, prior to refueling, the information gathering system collects the information from the combine harvester 600 via any ad hoc wireless link. The information collected may then be relayed to another network when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available. For example, a fuel truck may enter an area that has wireless communications coverage when it is traveling to a location to fuel other machines, or when it is at a main fuel depot. All of these architectures are considered herein. Furthermore, the information may be stored in the agricultural harvester 600 until the agricultural harvester 600 reaches an area of wireless communication coverage. The agricultural harvester 600 itself can send the information to another network.

It is also noted that the elements of 2 or portions thereof may be located on a variety of different devices. One or more of these devices may include an on-board computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile device, such as a palmtop computer, a mobile phone, a smartphone , a multimedia player, a personal digital assistant, etc.

In some examples, the remote server architecture 500 may include cyber security measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of individuals or processes accessing data, and use of ledgers to record metadata, data, data transfers, data access and involve data transformations. In some examples, the ledgers may be distributed and immutable (eg, implemented as a blockchain).

13 16 is a simplified block diagram of an illustrative example of a portable or mobile computing device that may be used as a user's or customer's handheld device 16 in which the present system (or portions thereof) may be employed. For example, a mobile device may be deployed in the cab of agricultural harvester 100 to generate, process, or display the maps discussed above. the 14-15 are examples of portable or mobile devices.

13 shows a general block diagram of the components of a terminal 16, showing some of the 2 components shown can run, interact with them, or both. A communications link 13 is provided in device 16, enabling the portable device to communicate with other computing devices and, in some examples, providing a channel for automatically receiving information, such as by scanning. Examples of communication links 13 include allowing communication over one or more communication protocols, such as wireless services, used to provide cellular access to a network enable, as well as protocols that provide local wireless connections to networks.

In other examples, applications may be received on a removable Secure Digital (SD) card connected to an interface 15 . Interface 15 and communications links 13 communicate with a processor 17 (which may also embody the processors or servers of the other FIGS.) via a bus 19 which is also connected to memory 21 and input/output (I/O) components 23 and the clock 25 and the locating system 27 is connected.

I/O components 23 are provided in one example to facilitate input and output operations. I/O components 23 for various examples of terminal device 16 may include input components such as buttons, tactile sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometer sensors, orientation sensors, and output components such as a display, speaker, and/or printer port . Other I/O components 23 can also be used.

Clock 25 illustratively includes a real-time clock component that outputs a time and date. This may also provide timing functions for processor 17, illustratively.

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

Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. Memory 21 may include all types of tangible volatile and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (see below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions in accordance with the instructions. The processor 17 can be activated by other components to enhance their functionality as well.

14 FIG. 12 shows an example where the device 16 is a tablet computer 600. FIG. In 14 computer 601 with user interface screen 602 is shown. The screen 602 can be a touch screen or a pen-enabled interface that receives input from a pen or stylus. The tablet computer 600 can also use a virtual on-screen keyboard. Of course, the computer 601 can also be connected to a keyboard or other user input device via any suitable attachment mechanism, such as a wireless connection or a USB port. Computer 601 may illustratively also receive voice input.

15 is similar to 14 except that the device is a smartphone 71 . The smartphone 71 has a touch-sensitive display 73 that displays icons or graphics or other user input mechanisms 75 . The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smartphone 71 is built on top of a mobile operating system and offers more advanced computing power and connectivity than a feature phone.

It should be noted that other shapes of devices 16 are possible.

16 is an example of a computing environment in which elements of 2 can be used. With reference to 16 An exemplary system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. The components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include processors or servers from the preceding FIGURES), a system memory 830, and a system bus 821 that connects various system components including the system memory to the processing unit 820 couple. The system bus 821 can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus with a variety of bus architectures. Memory and programs related to 2 may be described in appropriate parts of 16 .

Computer 810 typically includes multiple computer-readable media. Computer-readable media can be any available media that can be accessed by the computer 810 and includes both 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 communications media. Computer storage media is distinct from and does not include a modulated data signal or carrier wave. Computer-readable media includes hardware storage media, having volatile and non-volatile, removable and non-removable media, implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computational storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridge, tape, disk storage or other magnetic storage device, or any other medium that can be used to store the desired information accessible via computer 810. Communication media may contain computer-readable instructions, data structures, program modules, or other data in a transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics adjusted or altered to encode information in the signal.

System memory 830 includes computer storage media in the form of volatile and/or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS) that contains the basic programs that help To transfer information between elements within the computer 810, such as at startup, is typically stored in ROM 831. RAM 832 typically contains data or program modules, or both, that are immediately accessible to processing unit 820 and/or are currently operating. Illustrated by way of example and not limitation 16 the operating system 834, the application programs 835, further program modules 836 and program data 837.

Computer 810 may also include other removable/non-removable volatile/non-volatile computer storage media. For example shows 16 a hard disk drive 841 that reads from or writes to non-removable, non-volatile magnetic media, an optical disk drive 855, and a non-volatile optical disk 856. Hard disk drive 841 is typically connected to system bus 821 via a non-removable storage interface, such as interface 840, and optical disk drive 855 is typically connected to system bus 821 via a removable storage interface, such as interface 850.

Alternatively or additionally, the functionality described herein may be performed at least in part by one or more hardware logic components. Illustrative types of hardware logic components that may be used include, for example, field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), off-the-shelf application-specific products (e.g., ASSPs), system -on-a-chip systems (SOCs), "Complex Programmable Logic Devices" (CPLDs), etc.

The drives and associated computer storage media discussed above and in 16 , provide storage of computer-readable instructions, data structures, program modules, and other data for computer 810. In 16 For example, the hard disk drive 841 is shown as storing operating system 844, application programs 845, other program modules 846 and program data 847. It should be noted that these components may be either the same as or different from the operating system 834, application programs 835, other program modules 836, and program data 837.

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

Computer 810 operates in a network environment via logical connections (such as CAN, LAN, or WAN) to one or more remote computers, such as remote computer 880.

When used in a LAN network environment, the computer 810 is connected to the LAN 871 via a network interface or adapter 870 . When used in a WAN network environment, the computer 810 typically includes a modem 872 or other means for establishing communication over the WAN 873, such as the Internet. In a networked environment, program modules may be stored on an external storage device. 16 illustrates, for example, that remote application programs 885 can reside on remote computer 880 .

It should also be noted that the various examples described here can be combined in various ways. That is, portions of one or more examples may be combined with portions of one or more other examples. All of these are contemplated herein.

• ein Kommunikationssystem, das eine Informationskarte empfängt, die Werte eines ersten landwirtschaftlichen Merkmals beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erfasst;
• einen In-situ-Sensor, der einen Wert eines zweiten landwirtschaftlichen Merkmals erkennt, das der geografischen Position entspricht;
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte und auf Grundlage des Wertes des zweiten landwirtschaftlichen Merkmals auf die verschiedenen geografischen Positionen in dem Feld abbildet;
• eine Haspel; und
• ein Steuersystem, das ein Steuersignal erzeugt, um die Haspel auf Grundlage der geografischen Position der landwirtschaftlichen Arbeitsmaschine und auf Grundlage der prädiktiven Werte des zweiten landwirtschaftlichen Merkmals in der funktionellen prädiktiven landwirtschaftlichen Karte zu steuern.

Example 1 is an agricultural working machine comprising:

• a communication system that receives an information map that includes values of a first agricultural characteristic that correspond to different geographic locations in a field;
• a geographic position sensor that detects a geographic position of the agricultural working machine;
• an in situ sensor that detects a value of a second agricultural characteristic that corresponds to the geographic location;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural feature to the different geographic locations in the field based on the values of the first agricultural feature in the information map and based on the value of the second agricultural feature ;
• a reel; and
• a control system that generates a control signal to control the reel based on the geographical position of the agricultural work machine and based on the predicted values of the second agricultural feature in the functional predictive agricultural map.

• eine Erntevorsatz- / Haspelsteuerung, die ein Haspelsteuersignal auf der Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte erzeugt und die Haspel auf der Grundlage des Haspelsteuersignals steuert, um eine Haspel der landwirtschaftlichen Arbeitsmaschine zu steuern.

Example 2 is the agricultural work machine of any or all of the preceding examples, wherein the control system includes:

• a header/reel controller that generates a reel control signal based on the detected geographic position and the functional predictive agricultural map and controls the reel based on the reel control signal to control a reel of the agricultural work machine.

Example 3 is the agricultural work machine of any or all of the preceding examples, wherein the control system controls a reel height of the reel.

Example 4 is the agricultural work machine of any or all of the preceding examples, wherein the control system controls a fore-aft position of the reel.

Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the information map includes values of crop status as the first agricultural characteristic.

Example 6 is the agricultural work machine of any or all of the preceding examples, wherein the information card includes vegetative index values as the first agricultural characteristic.

Example 7 is the agricultural working machine of one or all of the preceding examples, where the information card includes yield values as the first agricultural characteristic.

Example 8 is the agricultural work machine of any or all of the preceding examples, wherein the in situ sensor detects vegetation height as the second agricultural characteristic.

Example 9 is the agricultural work machine of any or all of the preceding examples, wherein the in situ sensor recognizes an operator command of reel height as the second agricultural characteristic.

Example 10 is the agricultural work machine of any or all of the preceding examples, wherein the in situ sensor recognizes an operator command of reel forward-backward position as the second agricultural characteristic.

• erhalten einer Informationskarte, die Werte eines ersten landwirtschaftlichen Merkmals beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• erkennen einer geografischen Position der landwirtschaftlichen Arbeitsmaschine;
• erkennen eines Werts eines zweiten landwirtschaftlichen Merkmals, das einer geografischen Position entspricht, mit einem In-situ-Sensor;
• erzeugen einer funktionellen prädiktiven landwirtschaftlichen Karte des Feldes, die prädiktive Steuerwerte auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte und auf Grundlage des Werts des zweiten landwirtschaftlichen Merkmals, der der geografischen Position entspricht, auf die verschiedenen geografischen Positionen in dem Feld abbildet;
• steuern einer Haspel auf Grundlage der geografischen Position der landwirtschaftlichen Arbeitsmaschine und auf Grundlage der Steuerwerte in der funktionellen prädiktiven landwirtschaftlichen Karte.

Example 11 is a computer-implemented method for controlling an agricultural work machine, comprising:

• obtaining an information map including values of a first agricultural characteristic corresponding to different geographic positions in a field;
• detecting a geographic position of the agricultural work machine;
• detecting a value of a second agricultural characteristic corresponding to a geographic location with an in situ sensor;
• generating a functional predictive agricultural map of the field that maps predictive control values to the different geographic positions in the field based on the values of the first agricultural characteristic in the information map and based on the value of the second agricultural characteristic corresponding to the geographic position;
• control a reel based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

Example 12 is the computer-implemented method of any or all of the preceding examples, wherein detecting with an in situ sensor includes detecting vegetation height as the second agricultural characteristic, and wherein generating a functional predictive agricultural map includes generating a functional predictive vegetation height map that maps predictive vegetation height values as the control values to the various geographic locations in the field.

• erzeugen eines Haspelsteuersignals auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte; und
• steuern des steuerbaren Teilsystems auf Grundlage des Haspelsteuersignals, um eine Höhe der Haspel zu steuern.

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

• generating a reel control signal based on the detected geographic position and the functional predictive agricultural map; and
• controlling the controllable subsystem based on the reel control signal to control a height of the reel.

• erzeugen eines Haspelsteuersignals auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte; und
• steuern des steuerbaren Teilsystems auf Grundlage des Haspelsteuersignals, um eine Vorwärts-Rückwärts-Position der Haspel zu steuern.

Example 14 is the computer-implemented method of any or all of the preceding examples, wherein controlling a controllable subsystem comprises:

• generating a reel control signal based on the detected geographic position and the functional predictive agricultural map; and
• controlling the controllable subsystem based on the reel control signal to control a fore and aft position of the reel.

Example 15 is the computer-implemented method of any or all of the preceding examples, wherein detecting with an in situ sensor includes detecting an operator command input indicative of a commanded reel position as the second agricultural feature, and generating a functional predictive agricultural map that generating a functional predictive reel position map that maps predictive reel position values as the control values to the various geographic positions in the field.

• erhalten einer oder mehrerer der folgenden Informationen als Informationskarte: eine vegetative Indexkarte, eine Erntegutzustandskarte und eine Ertragskarte.

Example 16 is the computer-implemented method of any or all of the preceding examples, wherein obtaining the information card comprises:

• receive one or more of the following information as an information map: a vegetative index map, a crop condition map, and a yield map.

• erzeugen eines Haspelsteuersignals auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte; und
• steuern des steuerbaren Teilsystems auf Grundlage des Haspelsteuersignals, um eine Höhe der Haspel zu steuern.

Example 17 is the computer-implemented method of any or all of the preceding examples, wherein controlling a controllable subsystem comprises:

• generating a reel control signal based on the detected geographic position and the functional predictive agricultural map; and
• controlling the controllable subsystem based on the reel control signal to control a height of the reel.

• erzeugen eines Haspelsteuersignals auf Grundlage der erkannten geografischen Position und der funktionellen prädiktiven landwirtschaftlichen Karte; und
• 0237] steuern des steuerbaren Teilsystems auf Grundlage des Haspelsteuersignals, um eine Vorwärts-Rückwärts-Position der Haspel zu steuern.

Example 18 is the computer-implemented method of any or all of the preceding examples, wherein controlling a controllable subsystem comprises:

• generating a reel control signal based on the detected geographic position and the functional predictive agricultural map; and
• 0237] controlling the controllable subsystem based on the reel control signal to control a fore-aft position of the reel.

• ein Kommunikationssystem, das eine Informationskarte empfängt, die Werte eines ersten landwirtschaftlichen Merkmals beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erfasst;
• einen In-situ-Sensor, der einen Wert eines zweiten landwirtschaftlichen Merkmals erkennt, das der geografischen Position entspricht;
• einen prädiktiven Modellgenerator, der ein prädiktives landwirtschaftliches Modell erzeugt, das eine Beziehung zwischen dem zweiten landwirtschaftlichen Merkmal und dem ersten landwirtschaftlichen Merkmal auf Grundlage eines Werts des ersten landwirtschaftlichen Merkmals in der Informationskarte an der geografischen Position und eines Werts des zweiten landwirtschaftlichen Merkmals, das durch den In-situ-Sensor an der geografischen Position erkannt wird, modelliert;
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Steuerwerte auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte und auf Grundlage des prädiktiven landwirtschaftlichen Modells auf die verschiedenen geografischen Positionen in dem Feld abbildet;
• eine Haspel; und
• ein Steuersystem, das ein Steuersignal erzeugt, um die Haspel auf Grundlage der geografischen Position der landwirtschaftlichen Arbeitsmaschine und auf Grundlage der Steuerwerte in der funktionellen prädiktiven landwirtschaftlichen Karte zu steuern.

Example 19 is an agricultural work machine comprising:

• a communication system that receives an information map that includes values of a first agricultural characteristic that correspond to different geographic locations in a field;
• a geographic position sensor that detects a geographic position of the agricultural working machine;
• an in situ sensor that detects a value of a second agricultural characteristic that corresponds to the geographic location;
• a predictive model generator that generates a predictive agricultural model that shows a relationship between the second agricultural feature and the first agricultural feature based on a value of the first agricultural feature in the information map at the geographical position and a value of the second agricultural feature determined by the In situ sensor is detected at the geographic position modeled;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the first agricultural feature in the information map and based on the predictive agricultural model;
• a reel; and
• a control system that generates a control signal to control the reel based on the geographical position of the agricultural work machine and based on the control values in the functional predictive agricultural map.

Example 20 is the agricultural work machine of any or all of the preceding examples, wherein the in situ sensor recognizes an operator command input indicative of a commanded reel position as the second agricultural characteristic.

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

Claims (15)

Agricultural working machine (100), comprising: a communication system (206) receiving an information map (258) including values of a first agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine; an in situ sensor (208) that detects a value of a second agricultural feature that corresponds to the geographic location; a predictive map generator (212) that generates a functional predictive agricultural map of the field that assigns predictive values of the second agricultural feature to the different ones based on the values of the first agricultural feature in the information map (258) and based on the value of the second agricultural feature maps geographic locations in the field; a reel (164); and a control system (214) that generates a control signal to control the reel (164) based on the geographical position of the agricultural work machine (100) and based on the predicted values of the second agricultural feature in the functional predictive agricultural map. Agricultural working machine after claim 1 , wherein the control system comprises: a header/reel controller that generates a reel control signal based on the detected geographic position and the functional predictive agricultural map and controls the reel based on the reel control signal to control a reel of the agricultural work machine. Agricultural working machine after claim 2 , wherein the control system controls a reel height of the reel. Agricultural working machine after claim 2 , wherein the control system controls a fore and aft position of the reel. Agricultural working machine after claim 1 , wherein the information card contains values of the crop condition as the first agricultural characteristic. Agricultural working machine after claim 1 , where the information card values of the vegetative index as the first agricultural trait. Agricultural working machine after claim 1 , whereby the information map contains yield values as the first agricultural characteristic. Agricultural working machine after claim 1 , wherein the in situ sensor detects vegetation height as the second agricultural feature. Agricultural working machine after claim 1 wherein the in situ sensor recognizes an operator command of reel height as the second agricultural characteristic. Agricultural working machine after claim 1 wherein the in situ sensor recognizes an operator command of reel forward-backward position as the second agricultural characteristic. A computer-implemented method for controlling an agricultural working machine (100), comprising: obtaining an information map (258) including values of a first agricultural characteristic corresponding to different geographic locations in a field; detecting a geographical position of the agricultural working machine (100); detecting a value of a second agricultural characteristic with an in situ sensor (208) that corresponds to a geographic location; creating a functional predictive agricultural map of the field applying predictive control values to the different geographic positions in the field based on the values of the first agricultural feature in the information map (258) and based on the value of the second agricultural feature corresponding to the geographic location depicts and controlling a reel (164) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive agricultural map. Computer-implemented method claim 11 , wherein the detecting with an in situ sensor comprises detecting a vegetation height as the second agricultural characteristic and wherein the generating a functional predictive agricultural map comprises generating a functional predictive vegetation height map having predictive vegetation height values as the control values at the various geographic locations in the field depicts. Computer-implemented method claim 11 wherein controlling a controllable subsystem comprises: generating a reel control signal based on the detected geographic position and the functional predictive agricultural map; and controlling the controllable subsystem based on the reel control signal to control a height of the reel. Computer-implemented method claim 11 wherein controlling a controllable subsystem comprises: generating a reel control signal based on the detected geographic position and the functional predictive agricultural map; and controlling the controllable subsystem based on the reel control signal to control a fore and aft position of the reel. Agricultural working machine (100), comprising: a communication system (206) receiving an information map (258) including values of a first agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine; an in situ sensor (208) that detects a value of a second agricultural feature that corresponds to the geographic location; a predictive model generator (210) that generates a predictive agricultural model that models a relationship between the second agricultural feature and the first agricultural feature based on a value of the first agricultural feature in the information map at the geographic location and a value of the second agricultural feature , which is detected by the in situ sensor (208) at the geographic position; a predictive map generator (212) that generates a functional predictive agricultural map of the field mapping predictive control values to the various geographic locations in the field based on the values of the first agricultural feature in the information map and based on the predictive agricultural model; a reel (164); and a control system (214) that generates a control signal to control the reel (164) based on the geographical position of the agricultural work machine (100) and based on the control values in the functional predictive agricultural map.

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