SYSTEMS AND METHODS FOR PREDICTIVE END DIVIDER CONTROL

A map is obtained by an agricultural system. The map maps values of a characteristic to different geographic locations in a field. A control system generates a control signal to control operation of one or more end dividers on an agricultural harvester based on the map.

FIELD OF THE DESCRIPTION

The present description relates agricultural harvesters. More particularly, the present description relates to predictively controlling end dividers on a head of an agricultural harvester.

BACKGROUND

There are several different types of agricultural harvesters. One type of agricultural harvester is a combine harvester which can have different heads attached to harvest different types of crops.

In one example, a corn head can be attached to a combine harvester in order to harvest corn. A corn head may have row dividers and gathering chains. The row dividers help to divide the rows of corn and the gathering chains pull the corn stalks into a set of snap rolls that separate the ears of the corn plant from the stalks. The ears are then moved by an auger toward the center of the corn head where the ears enter the feeder house of the combine harvester. The ears are then further processed within the combine harvester to remove the kernels of corn from the cobs.

SUMMARY

A map is obtained by an agricultural system. The map maps values of a characteristic to different geographic locations in a field. A control system generates a control signal to control operation of one or more end dividers on an agricultural harvester based on the map.

Example 1 is an agricultural system comprising:

a control system that:

receives, from a geographic position sensor, a geographic position of an agricultural harvester in a field;

receives a map that maps values of a characteristic corresponding to different geographic locations in the field; and

generates a control signal to control an end divider actuator on the agricultural harvester based on the map and the geographic position of the agricultural harvester.

Example 2 is the agricultural system of any or all previous examples, wherein the map comprises one or more maps, each map, of the one or more maps, mapping values of a respective characteristic corresponding to the different geographic locations in the field, wherein the one or more maps comprise one or more of:

a harvest coverage map that maps, as values of the respective characteristic, harvest coverage values to the different geographic locations in the field;

a genotype map that maps, as values of the respective characteristic, genotype values to the different geographic locations in the field;

a crop state map that maps, as values of the respective characteristic, crop state values to the different geographic locations in the field; and

a weed map that maps, as values of the respective characteristic, weed values to the different geographic locations in the field.

Example 3 is the agricultural system of any or all previous examples, wherein the map comprises a functional predictive ear characteristic map that maps, as the values of the characteristic, predictive values of an ear characteristic, the agricultural system further comprising:

an in-situ sensor that detects a value of the ear characteristic corresponding to a geographic location in the field;

a predictive model generator that:

receives an information map that maps values of a characteristic to the different geographic locations in the field; and

generates a predictive ear characteristic model that models a relationship between the characteristic mapped in the information map and the ear characteristic based on the value of the ear characteristic detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map at the geographic location; and

a predictive map generator that generates the functional predictive ear characteristic map that maps the predictive ear characteristic values to the different geographic locations in the field based on the values of the characteristic in the information map and based on the predictive ear characteristic model.

Example 4 is the agricultural system of any or all previous examples, wherein the map comprises a functional predictive control input map that maps, as the values of the characteristic, predictive control input values, the agricultural system further comprising:

an in-situ sensor that detects a control input value corresponding to a geographic location in the field;

a predictive model generator that:

receives an information map that maps values of a characteristic to the different geographic locations in the field; and

generates a predictive control input model that models a relationship between the characteristic mapped in the information map and control input based on the value of the control input detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map at the geographic location; and

a predictive map generator that generates the functional predictive control input map that maps the predictive control input values to the different geographic locations in the field based on the values of the characteristic in the information map and based on the predictive control input model.

Example 5 is the agricultural system of any or all previous examples, wherein the map comprises a functional predictive hair pinning map that maps, as the values of the characteristic, predictive hair pinning values, the agricultural system further comprising:

an in-situ sensor that detects a hair pinning value corresponding to a geographic location in the field;

a predictive model generator that:

receives an information map that maps values of a characteristic to the different geographic locations in the field; and

generates a predictive hair pinning model that models a relationship between the characteristic mapped in the information map and hair pinning based on the hair pinning value detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map at the geographic location; and

a predictive map generator that generates the functional predictive hair pinning map that maps the predictive hair pinning values to the different geographic locations in the field based on the values of the characteristic in the information map and based on the predictive hair pinning model.

Example 6 is the agricultural system of any or all previous examples, wherein the map comprises a functional predictive wrapping map that maps, as the values of the characteristic, predictive wrapping values, the agricultural system further comprising:

an in-situ sensor that detects a wrapping value corresponding to a geographic location in the field;

a predictive model generator that:

receives an information map that maps values of a characteristic to the different geographic locations in the field; and

generates a predictive wrapping model that models a relationship between the characteristic mapped in the information map and wrapping based on the wrapping value detected by the in-situ sensor corresponding to the geographic location and a value of the characteristic in the information map at the geographic location; and

a predictive map generator that generates the functional predictive wrapping map that maps the predictive wrapping values to the different geographic locations in the field based on the values of the characteristic in the information map and based on the predictive wrapping model.

Example 7 is the agricultural system of any or all previous examples, wherein the control system generates the control signal to control the end divider actuator raise an end divider on the agricultural harvester.

Example 8 is the agricultural system of any or all previous examples, wherein the control system generates the control signal to control the end divider actuator to lower an end divider on the agricultural harvester.

Example 9 is the agricultural system of any or all previous examples, wherein the control system generates the control signal to control the end divider actuator to adjust rotation of an end divider on the agricultural harvester.

Example 10 is a method of controlling an agricultural harvester comprising:

receiving a map that maps values of a characteristic to different geographic locations in a field;

identifying a geographic position of the agricultural harvester at the field; and

controlling an end divider actuator on the agricultural harvester based on the map and the geographic position of the agricultural harvester.

Example 11 is the method of any or all previous examples, wherein receiving the map comprises receiving one or more maps, each map, of the one or more maps, mapping values of a respective characteristic corresponding to the different geographic locations in the field, wherein the one or more maps comprise one or more of:

a harvest coverage map that maps, as values of the respective characteristic, harvest coverage values to the different geographic locations in the field;

a genotype map that maps, as values of the respective characteristic, genotype values to the different geographic locations in the field;

a crop state map that maps, as values of the respective characteristic, crop state values to the different geographic locations in the field; and

a weed map that maps, as values of the respective characteristic, weed values to the different geographic locations in the field.

Example 12 is the method of any or all previous examples, wherein receiving the map comprises receiving a functional predictive map that maps, as the values of the characteristic, predictive values of the characteristic to the different geographic locations in the field, wherein the method further comprises:

receiving an information map that maps values of an additional characteristic corresponding to the different geographic locations in the field;

detecting, with an in-situ sensor, a value of the characteristic corresponding to a geographic location;

generating a predictive model that models a relationship between the additional characteristic and the characteristic; and

controlling a predictive map generator to generate the functional predictive map that maps the predictive values of characteristic to the different locations in the field based on the values of the additional characteristic in the information map and the predictive model.

Example 13 is the method of any or all previous examples, wherein controlling the end divider actuator comprises controlling the end divider actuator to adjust a position of an end divider on the agricultural machine.

Example 14 is the method of any or all previous examples, wherein controlling the end divider actuator comprises controlling the end divider actuator adjust rotation of an end divider on the agricultural machine.

Example 15 is the method of any or all previous examples, wherein controlling the end divider actuator comprises controlling:

a first end divider actuator to control a first end divider on the agricultural harvester; and

a second end divider actuator to control a second end divider on the agricultural harvester.

Example 16 is the method of any or all previous examples and further comprising controlling an interface mechanism to display the map.

Example 17 is an agricultural harvester comprising:

a geographic position sensor that detect a geographic position of the agricultural harvester in a field;

a control system that:

receives a map that maps values of a characteristic to different geographic locations in the field; and

generates a control signal to control an end divider subsystem of the agricultural harvester based on the map and the geographic location of the agricultural harvester.

Example 18 is the agricultural harvester of any or all previous examples, wherein the map comprises one of:

a harvest coverage map that maps, as values of the characteristic, harvest coverage values to the different geographic locations in the field;

a genotype map that maps, as values of the characteristic, genotype values to the different geographic locations in the field;

a crop state map that maps, as values of the characteristic, crop state values to the different geographic locations in the field; and

a weed map that maps, as values of the characteristic, weed values to the different geographic locations in the field.

Example 19 is the agricultural harvester of any or all previous examples, wherein the map comprises a functional predictive map that maps, as the values of the characteristic, predictive values of the characteristic to the different geographic location;

wherein the functional predictive map is generated based on a predictive model that models a relationship between the characteristic and an additional characteristic; and

wherein the predictive model models the relationship based on an in-situ value of the characteristic, detected by an in-situ sensor, corresponding to a geographic location and a value of the additional characteristic at the geographic location in an information map.

Example 20 is the agricultural harvester of any or all previous examples, wherein the control system generates the control signal to control an end divider actuator of the end divider subsystem to drive movement of an end divider of the agricultural harvester.

DETAILED DESCRIPTION

For the purposes of promoting an 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 the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure.

In one example, the present description relates to using in-situ data taken concurrently with an operation, in combination with prior or predicted data, such as prior or predicted data represented in a map, to generate a predictive model and a predictive map, such as a predictive ear characteristic model and predictive ear characteristic map. In some examples, the predictive ear characteristic map can be used to control a mobile machine.

As discussed above, agricultural harvesting machines, such as combine harvesters, can be attached to different types of heads for harvesting different types of crops. One such head is a corn head which can be attached to the combine harvester in order to harvest corn. A corn head may have row dividers and gathering chains. The row dividers help to divide the rows of corn and the gathering chains pull the corn stalks into a set of snap rolls that separate the ears of the corn plant from the stalks. The ears are then moved by an auger toward the center of the corn head where the ears enter the feeder house of the combine harvester. The ears are then further processed within the combine harvester to remove the kernels of corn from the cobs.

During the harvesting operation, after the ears of corn are separated from the stalk, the ears can bounce around on the head and can bounce off of the head onto the field and be lost. In order to address this type of loss, the corn head can be equipped with end dividers disposed on the ends of the corn head. The end dividers can be controllably positioned (e.g., raised, lowered, etc.) to inhibit ear loss over the sides of the corn head. In some examples, the end dividers can be controllably rotated (e.g., at variable speeds and/or variable directions of rotation).

In one example, the present description relates to obtaining a map such as a genotype map. The genotype map includes geolocated values of crop genotype across different locations at a field of interest. The crop genotype values may indicate genotype information for the crop at the field, for instance, in a corn field, there may be several different types (e.g., hybrids, cultivar, etc.) of corn plants. The genotype map may be derived from operator or user inputs that indicate the crop genotype information at different areas of the field. The genotype map may be derived from information, including sensor readings, from a planting operation occurring on the field earlier in the year. For instance, the planting machine (e.g., planter, seeder, etc.) may have data, including sensor data, that indicates the genotype of seed planted and the locations at which the seeds were planted. These are merely some examples. The genotype map can be generated in a variety of other ways.

In one example, the present description relates to obtaining a map such as a crop state map. The crop state map includes geolocated crop state values across different geographic locations at a field of interest. Without limitation, the crop state values may indicate a degree (magnitude) of downing (e.g., not down, partially down, fully down), as well as the direction (e.g., compass direction) in which the crops are downed. The crop state map may be derived from sensor readings taken during a prior operation, performed by a machine, at the field of interest (e.g., a prior spraying operation) or taken during an aerial survey of the field of interest (e.g., drone survey, plane survey, satellite survey, etc.). These machines may be outfitted with one or more different types of sensors, such as imaging systems (e.g., cameras), optical sensors, ultrasonic sensors, as well as sensors that detect one or more bands of electromagnetic radiation reflected by the plants on the field of interest. These are merely some examples. The crop state map can be generated in a variety of other ways.

In one example, the present description relates to obtaining a map such as a weed map. The weed map includes geolocated weed values across different geographic locations at a field of interest. The weed values may indicate one or more of weed intensity and weed type. Without limitation, weed intensity may include at least one of weed presence, weed population, weed growth stage, weed biomass, weed moisture, weed density, a height of weeds, a size of weed plants, an age of weeds, and health condition of weeds at locations in the field of interest. Without limitation, weed type may include weed genotype information (e.g., weed species) or more broad categorization of type, such as vine type weed and non-vine type weed. The weed map may derived from sensor readings taken during a prior operation, performed by a machine, at the field of interest (e.g., a prior spraying operation) or taken during an aerial survey of the field of interest (e.g., drone survey, plane survey, satellite survey, etc.). These machines may be outfitted with one or more different types of sensors, such as imaging systems (e.g., cameras), optical sensors, ultrasonic sensors, as well as sensors that detect one or more bands of electromagnetic radiation reflected by the plants on the field of interest. Alternatively, or additionally, the weed map may be derived from vegetative index values at the field of interest (such as vegetative index values in a vegetative index map). One example of a vegetative index is a normalize difference vegetation index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure, including, but not limited to, a leaf area index (LAI). These are merely some examples. The weed map can be generated in a variety of other ways.

In one example, the present description relates to obtaining a map such as a harvest coverage map. The harvest coverage map includes geolocated harvest coverage values across different locations at a field of interest. The harvest coverage values indicate locations (or areas) of the field which have been harvested and locations (or areas) of the field which have not been harvested. The harvest coverage map may derived from sensor readings from sensors on the harvester taken during a harvesting operation, such as geographic position sensor readings, heading sensor readings, as well as sensor readings from sensors that sense operation of components of the harvester (such as the threshing rotor, cleaning system, clean grain elevator, the head auger, etc.) to determine locations (or areas) of the field which have been harvested. In other examples, the harvest coverage map may be derived from operator or user inputs that indicate locations (or areas) of the field that have been harvested. In other examples, the harvest coverage map may be derived from sensor readings of adjacent crop rows as the agricultural harvester100operates at the field, for instance images of adjacent crop rows may be captured to identify whether the crop in the adjacent rows are harvested or unharvested. These are merely some examples. The harvest coverage map can be generated in a variety of other ways.

In one example, the present description relates to obtaining in-situ data from in-situ sensors on the mobile agricultural machine taken concurrently with an operation. The in-situ sensor data can include ear characteristic data generated by ear characteristic sensors. The ear characteristic data and corresponding ear characteristic sensors can include one or more of: in-situ ear loss data generated by in-situ ear loss sensors; in-situ ear orientation data generated by in-situ ear orientation sensors; and other in-situ ear characteristic data generated by other in-situ ear characteristic sensors. In other examples, the in-situ sensor data can include in-situ wrapping data generated by in-situ wrapping sensors. In other examples, the in-situ sensor data can include in-situ hair pinning data generated by in-situ hair pinning sensors. In other example, the in-situ sensor data can include in-situ control input data generated by in-situ control input sensors. The various in-situ data is derived from various in-situ sensors on the mobile machine, as will be described in further detail herein. These are merely some examples of the in-situ data and in-situ sensors contemplated herein.

The present discussion proceeds, in some examples, with respect to systems that obtain one or more maps of a field, such as one or more of a genotype map, a crop state map, a weed map, a harvest coverage map, as well as various other types of maps and also use an in-situ sensor to detect a variable indicative of an agricultural characteristic value, such as an ear characteristic value (e.g., ear loss value, ear orientation value, etc.). The systems generate a model that models a relationship between the values on the obtained map(s) and the output values from the in-situ sensor. The model is used to generate a predictive map that predicts agricultural characteristic values, such as ear characteristic values, for example, one or more of ear loss values and ear orientation values. The predictive map, generated during an operation, can be presented to an operator or other user or used in automatically controlling a mobile agricultural machine during an operation or both. In some examples, the predictive map can be used to control one or more of a position of end dividers, a speed of rotation of end divers, a direction of rotation of end dividers, as well as various other parameters.

In some examples, the present discussion proceeds with respect to systems that obtain one or more maps of a field such as one or more of a genotype map, a crop state map, a weed map, a harvest coverage map, as well as various other types of maps and also use an in-situ sensor to detect a variable indicative of an agricultural characteristic value, such as wrapping value or a hair pinning value. The systems generate a model that models a relationship between the values on the obtained map(s) and the output values from the in-situ sensor(s). The model is used to generate a predictive map that predicts agricultural characteristic values, such as wrapping values or hair pinning values. The predictive map, generated during an operation, can be presented to an operator or other user or used in automatically controlling a mobile agricultural machine during an operation or both. In some examples, the predictive map can be used to control one or more of a position of end dividers, a speed of rotation of end divers, a direction of rotation of end dividers, as well as various other parameters.

In some examples, the present discussion proceeds with respect to systems that obtain one or more maps of a field such as one or more of a genotype map, a crop state map, a weed map, a harvest coverage map, as well as various other types of maps and also use an in-situ sensor to detect a variable indicative of an agricultural characteristic value, such as a control input value. The systems generate a model that models a relationship between the values on the obtained map(s) and the output values from the in-situ sensor(s). The model is used to generate a predictive map that predicts agricultural characteristic values, such as control input values. The predictive map, generated during an operation, can be presented to an operator or other user or used in automatically controlling a mobile agricultural machine during an operation or both. In some examples, the predictive map can be used to control one or more of a position of end dividers, a speed of rotation of end dividers, a direction of rotation of end dividers, as well as various other parameters.

The present discussion proceeds, in some examples, with respect to systems that obtain one or more maps of a field such as one or more of a genotype map, a crop state map, a weed map, a harvest coverage map, as well as various other types of maps and also use in-situ sensor data indicative of a geographic position of the agricultural harvester on the field. Based on the position of the agricultural harvester and the values on the one or more obtained maps, one or more parameters of the agricultural harvester can be controlled, such as one or more of a position of end dividers, a speed of rotation of end dividers, a direction of rotation of end dividers, as well as various other parameters.

While the various examples described herein proceed with respect to agricultural harvesters, particularly agricultural harvesters with a corn head, it will be appreciated that the systems and methods described herein are applicable to various other types of agricultural harvesters including, but not limited to, agricultural harvesters with other types of heads.

FIG.1s a pictorial illustration of one example of an agricultural harvester100. Agricultural harvester100includes combine harvester102and head104. Combine harvester102includes an operator's compartment103that has operator interface mechanisms that can be used by an operator to control combine harvester102and head104. Some examples of operator interface mechanisms are described below (e.g.,218inFIG.4).

As shown, head104is a rigid head, meaning that head104is not foldable. Head104has a plurality of row dividers106and augers108and110. Row dividers106separate the corn rows as agricultural harvester100moves through a field. The stalks are guided between row dividers106where gathering chains move the stalks into a set of snap rolls that remove the ears from the stalks. The ears are then moved toward a central portion105of head104by augers108and110, where the ears enter a feeder house, which feeds the ears into the combine harvester102for further processing.

As discussed above, after the ears are separated from the stalks, the ears can bounce around on head104and bounce over the end112of head104in the direction indicated by arrow116. The ears can also bounce over end114of head104in the direction indicated by arrow118. If the ears bounce over either end112or end114, the ears fall to the ground and are lost.

As illustrated inFIG.1, agricultural harvester100can include an observation sensor system180. Observation sensor system180illustratively detects one or more characteristics at the field. For example, observation sensor system180may detect one or more of one or more ear characteristics, such as ear loss and/or ear orientation, wrapping, hair pinning, as well as various other characteristics. In some examples, observation sensor system180includes one or more sensors, such as one or more different types of sensors or one or more different types of sensors, or both. For example, observation sensor system180may include one or more of one or more imaging systems (e.g., cameras, such as stereo cameras), one or more optical sensors (e.g., lidar, radar, etc.), one or more ultrasonic sensors, as well as various other sensors. Observation sensor system180may have a field of view that includes an area of the field around (e.g., ahead of, to the sides of, and/or behind) head104or that includes head104, or both. While observation sensor system180is shown at a particular location inFIG.1, it will be appreciated that observation sensor system180can be at various other locations, including on combine 102 or head104. In other examples, agricultural harvester100can include more than one observation sensor system180, with each disposed at a different location.

FIG.2is a pictorial illustration of another example of an agricultural harvester100. In the example ofFIG.2, agricultural harvester100includes combine harvester102attached to a head122. In the example shown inFIG.2, head122is a foldable corn head. Therefore, the head includes opposite end sections124and126which can be moved between a deployed position and a folded position. In one example, end portion124is foldable about a pivot128. End portion124folds about pivot128in the direction indicated by arrow130. The movement of end portion124is driven by an actuator132which, in the example shown inFIG.2, is illustrated as a hydraulic actuator. End portion126can be moved between a deployed position and a folded position. Similarly, end portion126can rotate about pivot134generally in the direction indicated by arrow136. The movement of end portion126can be driven by actuator138. In the example shown inFIG.2, actuator138is a hydraulic actuator.

Head122has opposite ends140and142. Once ears of corn are separated from the stalks by the head122shown inFIG.2, the ears can bounce around on head122and bounce over the ends140or142and thus be lost.

As illustrated inFIG.2, agricultural harvester100can include an observation sensor system180. Observation sensor system180illustratively detects one or more characteristics at the field. For example, observation sensor system180may detect one or more of one or more ear characteristics, such as ear loss and/or ear orientation, wrapping, hair pinning, as well as various other characteristics. In some examples, observation sensor system180includes one or more sensors, such as one or more different types of sensors or one or more different types of sensors, or both. For example, observation sensor system180may include one or more of one or more imaging systems (e.g., cameras, such as stereo cameras), one or more optical sensors (e.g., lidar, radar, etc.), one or more ultrasonic sensors, as well as various other sensors. Observation sensor system180may have a field of view that includes an area of the field ahead of head122or that includes head122, or both. While observation sensor system180is shown at a particular location inFIG.2, it will be appreciated that observation sensor system180can be at various other locations, including on combine 102 or head122. In other examples, agricultural harvester100can include more than one observation sensor system180, with each disposed at a different location.

FIG.3Ashows another view of a head144. Head144can be a part of agricultural harvester100. In one example, head144can be a rigid head, and can thus be similar to head104. In another example, head144can be a foldable head, and can thus be similar to head122. In order to address the problem of ears of corn being lost over the ends of the head of agricultural harvester100, head144is fitted with a first end divider146disposed at a first end148of head144. Head also has a second end divider150(shown in phantom inFIG.3A) disposed at a second end152of head144. End dividers146and150are movable between a retracted position, and a raised position. In the example shown inFIG.3A, end divider146is shown in the raised position and end divider150is shown in phantom in the raised position. When end divider146is in the retracted position, end divider146is retracted within a housing154. When end divider150is in the retraced position, end divider150is retracted within a housing156.

The end dividers146and150are automatically or semi-automatically (such as by operator input into an operator input mechanism) movable between the fully retracted position and the fully raised position. In some examples, positions of the end dividers146and150are selectable to any of a plurality of different positions between the fully retracted position and the fully raised position. Also, in some examples, the end dividers146and150are movable to a position based upon an operator input, such as an operator input made from within the operator compartment103of the combine harvester102. Also, in some examples, the position of the end dividers146and150is automatically controlled based upon maps (and values therein), sensor inputs, operator inputs, or other inputs.

End divider146is moveable between the retracted position and the raised position by an actuator (e.g.,342shown inFIG.4). End divider150is moveable between the retracted position and the raised position by an actuator (e.g.,342shown inFIG.4). Example actuators within the scope of the present description include a linear actuator, a rotary actuator, a hydraulic actuator, an electric actuator, or a pneumatic actuator. In other implementations, the actuators may other types of actuators.

An end divider controller (e.g.,335shown inFIG.4) generates control signals to control actuator(s) based upon inputs from one or more input mechanisms. In some examples, each end divider146,150may have a respective end divider controller. An operator may provide an input through operator interface mechanism(s) to command end divider146, or end divider150, or both end dividers146and150, to move to a desired position. The operator input mechanisms may detect the command from the operator and provide an indication of the command to the end divider controller(s). The end divider controller(s) generate control signals to control actuator(s) to control the position of end divider146or end divider150, or both, in response to the provided command indication. In some instances, end divider146and end divider150are independently controllable relative to one another. Therefore, in some implementations, the position of end divider146is independently controllable relative to the position of end divider150.

Also, in some examples, the end divider controller(s) can automatically generate control signals to control actuator(s) to control the position of end divider146or end divider150, or both, based on maps, as will be discussed in more detail below.

FIG.3Bshows a view of another example head144. In the example ofFIG.3B, head144includes an active end divider147instead of end divider146. This type of end divider147is rotationally driven by an actuator that is controlled by an end divider controller. The actuators may include motors, such as hydraulic motors or electric motors. Other types of actuators are also within the scope of the present description. The rotational speed and rotational direction of end divider147can be controlled via the end divider controller. While only one end divider147is shown, there may be one or more active end dividers on both sides of head144, such as another active end divider147on the opposite side of head144in place of end divider150.

An end divider controller (e.g.,335shown inFIG.4) generates control signals to control actuator(s) based upon inputs from one or more input mechanisms. In some examples, each end divider147may have a respective end divider controller. An operator may provide an input through operator interface mechanism(s) to command the rotational speed or rotational direction, or both, of one or more end dividers147. The operator input mechanisms may detect the command from the operator and provide an indication of the command to the end divider controller(s). The end divider controller(s) generate control signals to control actuator(s) to control the rotational speed or the rotational direction, or both, of one or more end dividers147in response to the provided command indication. In some instances, each end divider147is independently controllable relative to one another. Therefore, in some implementations, the position of end divider146is independently controllable relative to the position of end divider150.

Also, in some examples, the end divider controller(s) can automatically generate control signals to control actuator(s) to control the rotational speed or rotational direction, or both, of one or more end dividers147based on maps, as will be discussed in more detail below.

FIG.4is a block diagram showing some portions of an agricultural harvesting system architecture300.FIG.4shows that agricultural harvesting system architecture300includes agricultural harvester100, one or more remote computing systems368, an operator360, one or more remote users366, one or more remote user interfaces364, network359, and one or more information maps358. Agricultural harvester100, itself, illustratively includes one or more processors or servers301, data store302, geographic position sensor304, communication system306, one or more in-situ sensors308that sense one or more characteristics of a worksite concurrent with an operation, and a processing system338that processes the sensors data (e.g., sensor signals, images, etc.) generated by in-situ sensors308to generate processed sensor data. The in-situ sensors generate values corresponding to the sensed characteristics. Mobile machine100also includes a predictive model or relationship generator (collectively referred to hereinafter as “predictive model generator310”), predictive model or relationship (collectively referred to hereinafter as “predictive model311”), predictive map generator312, control zone generator313, control system314, one or more controllable subsystems316, and an operator interface mechanism318. The mobile machine can also include a wide variety of other machine functionality320.

The in-situ sensors308can be on-board mobile machine100, remote from mobile machine, such as deployed at fixed locations on the worksite or on another machine operating in concert with mobile machine100, such as an aerial vehicle, and other types of sensors, or a combination thereof. In-situ sensors308sense characteristics of a worksite during the course of an operation. In-situ sensors308illustratively include ear sensors380, wrapping sensors386, hair pinning sensors388, control input sensors, heading/speed sensors325, and can include various other sensors328. Ear sensors380illustratively include one or more ear loss sensors382, one or more ear orientation sensors384, and can include other types of ear sensors385. Ear sensors380illustratively detect ear characteristics, such as ear orientation or ear loss, or both, at the field.

Ear loss sensors382illustratively detect ear loss, that is ears bouncing out of a head (e.g.,144) of agricultural harvester100as well as ears being dislodged from plants in rows adjacent to agricultural harvester100, such as ears knocked off of adjacent plants by the head, or a component of the head. Ear loss sensors382can include imaging systems, such as cameras (e.g., stereo camera, mono camera, etc.), optical sensors, such as radar or lidar, ultrasonic sensors, as well as various other sensors, including but not limited to, sensors that detect one or more wavelengths of electromagnetic radiation emitted (or reflected) from the plants. In some examples, observation sensor system180is or includes ear loss sensors382. In some examples, ear loss sensors382have a field of view that includes the head, and area(s) around the head (e.g., one or more of area(s) adjacent to head, such as to the side(s) of head, as well ahead of the head). In some examples, one ear loss sensor may observe one end of the head of agricultural harvester100while another ear loss sensor observes the opposite side of the head agricultural harvester100.

Ear orientation sensors384illustratively detect the orientation of ears on corn plants at the field. Corn ears on corn plants stand erect on the corn plant, that is, they are pointing more upwards (e.g., closer to parallel with the corn stalk, closer to perpendicular with the ground), that is, the angle between the corn stalk and the corn ear is a smaller acute angle (e.g., 20°). For a variety of reasons, corn ears may not be erect or may “droop” that is, the corn ears point out from the stalk such that the angle between the ear and the stalk is a greater degree of acuteness (e.g., 60°), or perhaps even a right angle or an obtuse angle. In such scenarios, the ear may jut out from its crop row and into an adjacent row and/or sticks out from the stalk enough to be hit by the head of the agricultural harvester100when the agricultural harvester100is harvesting an adjacent row. Ear orientation sensors384thus illustratively detect the magnitude of and direction of leaning of the ears on the corn plants at the field. Ear orientation sensors384can include imaging systems, such as cameras (e.g., stereo camera, mono camera, etc.), optical sensors, such as radar or lidar, ultrasonic sensors, as well as various other sensors, including but not limited to, sensors that detect one or more wavelengths of electromagnetic radiation emitted (or reflected) from the plants. In some examples, observation sensor system180is or includes ear orientation sensors384. In some examples, ear orientation sensors384have a field of view that includes the head, and area(s) around the head (e.g., one or more of area(s) adjacent to head, such as to the side(s) of head, as well ahead of the head). In some examples, one ear orientation sensor may observe one end of the head of agricultural harvester100while another ear orientation sensor observes the opposite side of the head of agricultural harvester100.

Wrapping sensors386illustratively detect wrapping of plant material (e.g., crop material, weed material, etc.) around active end dividers, such as active end dividers147. As the active end dividers rotate it may be that plant material from adjacent rows or from the row being harvested gets caught in active end dividers147. Such wrapping can not only affect the performance of end dividers147(e.g., affect rotation speed, power requirements, etc.), but also can have a deleterious effect on the harvesting operation, such as by knocking ears off of plants in adjacent rows, knocking ears off prior to reaching the snap rolls, pulling plants down in adjacent rows, damaging the crop plants, including damaging the ears, impeding material flow, as well as various other effects. Thus, wrapping of plant material on end dividers147may indicate undesirable contact between end dividers147and plants on the field. Wrapping sensors386can include imaging systems, such as cameras (e.g., stereo camera, mono camera, etc.), optical sensors, such as radar or lidar, ultrasonic sensors, as well as various other sensors, including but not limited to, sensors that detect one or more wavelengths of electromagnetic radiation emitted (or reflected) from plant material on end dividers147. In some examples, observation sensor system180is or includes wrapping sensors386. In some examples, wrapping sensors386have a field of view that includes the head, and area(s) around the head (e.g., one or more of area(s) adjacent to head, such as to the side(s) of head, as well ahead of the head). In some examples, one wrapping sensor may observe one end of the head of agricultural harvester100while another wrapping sensor observes the opposite side of the head of agricultural harvester100.

Hair pinning sensors388illustratively detect hair pinning on end dividers, that is the accumulation of plant material (e.g., crop material, weed material, etc.) on an edge of the end dividers (e.g., front edge, etc.). As the agricultural harvester100moves through the field, the end dividers may contact plant material from the row which agricultural harvester100is harvesting or form adjacent rows. This plant material may accumulate on an edge of the end dividers. Such hair pinning may not only affect the performance of the end dividers (e.g., prevent movement of the end dividers, require more power to move the end dividers, etc.) but also can have deleterious effect(s) on the harvesting operation. For instance, hair pinning may result in undesired contact with crop plants at the field which may result in knocking ears off of plants in adjacent rows, knocking ears off prior to reaching snap rolls, pulling plants down in adjacent rows, damaging the crop plants, including damaging the ears, impeding material flow, as well as various other effects. Hair pinning sensors388can include imaging systems, such as cameras (e.g., stereo camera, mono camera, etc.), optical sensors, such as radar or lidar, ultrasonic sensors, as well as various other sensors, including but not limited to, sensors that detect one or more wavelengths of electromagnetic radiation emitted (or reflected) from plant material on the end dividers. In some examples, observation sensor system180is or includes hair pinning sensors388. In some examples, hair pinning sensors388have a field of view that includes the head, and area(s) around the head (e.g., one or more of area(s) adjacent to head, such as to the side(s) of head, as well ahead of the head). In some examples, one hair pinning sensor may observe one end of the head of agricultural harvester100while another hair pinning sensor observes the opposite side of the head of agricultural harvester100.

As described above, observation sensor system180may include or comprise one or more of ear sensors380, wrapping sensors386, and hair pinning sensors388. It will be noted that one or more of ear loss, ear orientation, wrapping, and hair pinning can be detected by one or more of the same type of sensor. For instance, an imaging system, such as a camera, or an optical sensor, may generate sensor data that is indicative of one or more of ear loss, ear orientation, wrapping, and hair pinning. For example, an image (or a set of images) captured by an imaging system may show one or more of ear loss, ear orientation, wrapping, and hair pinning. Thus it will be understood that each of ear loss, ear orientation, wrapping, and hair pinning may be detected by respective sensors or one or more of ear loss, ear orientation, wrapping, and hair pinning may be detected by one or more of the same type of sensor.

Control input sensors390illustratively detect control inputs that are used to control one or more items of agricultural harvester100, such as one or more controllable subsystems316. Control input sensors390may detect, as a control input, an operator input into an operator input mechanism218. For example, agricultural harvester100may include one or more input mechanisms, actuatable/interactable by the operator360, to control operation of a controllable subsystem316, for instance to control end divider subsystem340to control movement of end dividers of agricultural harvester100. Control input sensors390may detect, as a control input, a user input into a user interface mechanism364. For example, user interface mechanisms364may include one or more input mechanisms, actuatable/interactable by a user366, to control operation of a controllable subsystem316, for instance to control end divider subsystem340to control movement of end dividers of agricultural harvester100. Control input sensors390may detect, as a control input, a control signal generated by control system314. For example, control system314may generate control signals to control one or more controllable subsystems316, for instance to control end divider subsystem340to control movement of end dividers of agricultural harvester100. The control inputs may command an operation setting of a controllable subsystem, such as a position end divider(s), a speed of rotation of end divider(s), a direction of rotation of end divider(s), a travel speed of agricultural harvester100, a heading of agricultural harvester100, as well as various other operation settings. Control input sensors390thus generate control input values indicative of the operation settings commanded by the control inputs.

Heading/speed sensors325detect a heading and speed at which agricultural harvester100is traversing the worksite during the operation. This can include sensors that sense the movement of ground-engaging elements (e.g., wheels or tracks of agricultural harvester100) or can utilize signals received from other sources, such as geographic position sensors304, thus, while heading/speed sensors325as described herein are shown as separate from geographic position sensors304, in some examples, machine heading/speed is derived from signals received from geographic positions sensors304and subsequent processing. In other examples, heading/speed sensors325are separate sensors and do not utilize signals received from other sources.

Other in-situ sensors328may be any of a wide variety of other sensors, including, but not limited, sensors that detect various other characteristics at the field, such as characteristics of the field, characteristics of the plants at the field, and characteristics of the agricultural harvester100, such as operating parameters. Other in-situ sensors328can be on-board agricultural harvester100or can be remote from agricultural harvester100, such as other in-situ sensors328on-board another mobile machine that capture in-situ data of the worksite or sensors at fixed locations throughout the worksite. The remote data from remote sensors can be obtained by agricultural harvester100via communication system206over network359.

In-situ data includes data taken from a sensor on-board the agricultural harvester100or taken by any sensor where the data are detected during the operation of agricultural harvester100at a worksite.

Processing system338processes the sensor data (e.g., sensor signals, images, etc.) generated by in-situ sensors308to generate processed sensor data indicative of one or more characteristics. For example, processing system generates processed sensor data indicative of characteristic values based on the sensor data generated by in-situ sensors308, such as ear characteristic values based on sensor data generated by ear sensors380, for instance one or more of ear loss values based on sensor data generated by ear loss sensors382, ear orientation values based on sensor data generated by ear orientation sensors384, well as various other ear characteristic values based on sensor data generated by various other ear sensors385. Processing system338also processes sensor signals generated by other in-situ sensors308to generate processed sensor data indicative of other characteristic values, for instance one or more of wrapping values based on sensor data generated by wrapping sensors386, hair pinning values based on sensor data generated by hair pinning sensors388, control input values based on sensor data generated by control input sensors390, machine speed (travel speed, acceleration, deceleration, etc.) values based on sensor data generated by heading/speed sensors325, machine heading values based on sensor data generated by heading/speed sensors325, as well as various other values based on sensors data generated by various other in-situ sensors328.

It will be understood that processing system338can be implemented by one or more processers or servers, such as processors or servers301. Additionally, processing system338can utilize various filtering techniques, noise filtering techniques, sensor signal categorization, aggregation, normalization, as well as various other processing functionality. Similarly, processing system338can utilize various image processing techniques such as, sequential image comparison, RGB, edge detection, black/white analysis, machine learning, neural networks, pixel testing, pixel clustering, shape detection, as well any number of other suitable image processing and data extraction functionality.

FIG.4also shows remote users366interacting with agricultural harvester100or remote computing systems368, or both, through user interfaces mechanisms364over network359. Remote computing systems368can be a wide variety of different types of systems, or combinations thereof. For example, remote computing systems368can be in a remote server environment. Further, remote computing systems368can be remote computing systems, such as mobile devices, a remote network, a farm manager system, a vendor system, or a wide variety of other remote systems. In one example, agricultural harvester100can be controlled remotely by remote computing systems368or by remote users366, or both. As will be described below, in some examples, one or more of the components shown being disposed on agricultural harvester can be located elsewhere, such as at remote computing systems368.

FIG.4also shows that an operator360may operate agricultural harvester100. The operator360interacts with operator interface mechanisms218. In some examples, operator interface mechanisms218may include joysticks, levers, a steering wheel, linkages, pedals, buttons, dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, operator360may interact with operator interface mechanisms218using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types of operator interface mechanisms218may be used and are within the scope of the present disclosure. Operator360may be local to agricultural harvester100, such as in an operator compartment103or may be remote from agricultural harvester100.

FIG.4also shows that agricultural harvester100can obtain one or more information maps358. As described herein, the information maps358include, for example, a genotype map, a crop state map, a weed map, a harvest coverage map, as well as various other maps. However, information maps358may also encompass other types of data, such as other types of data that were obtained prior to a harvesting operation or a map from a prior operation. In other examples, information maps358can be generated during a current operation, such a map generated by predictive map generator312based on a predictive model311generated by predictive model generator310or map generated based on sensor data generated during the current operation, such as harvest coverage map generated during the current harvesting operation that indicates areas of the field that have been harvested and areas of the field that have not yet been harvested.

Information maps358may be downloaded onto mobile machine100over network359and stored in data store302, using communication system306or in other ways. In some examples, communication system306may 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 of networks. Network359illustratively represents any or a combination of any of the variety of networks. Communication system306may also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both.

Geographic position sensors304illustratively sense or detect the geographic position or location of agricultural harvester100. Geographic position sensors304can include, but are not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensors304can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensors304can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.

Predictive model generator310generates a model that is indicative of a relationship between the values sensed by the in-situ sensors308and values mapped to the field by the information maps358. For example, if the information map358maps genotype values to different locations in the worksite, and the in-situ sensors308are sensing values indicative of ear characteristics (e.g., one or more of ear loss and ear orientation), then model generator310generates a predictive ear characteristic model that models the relationship between the genotype values and the ear characteristic values. In another example, if the information map358maps weed values to different locations in the worksite, and the in-situ sensors308are sensing values indicative of wrapping, then model generator310generates a predictive model that models the relationship between the weed values and the wrapping values. These are merely some examples.

In some examples, the predictive map generator312uses the predictive models generated by predictive model generator310to generate functional predictive maps that predict the value of a characteristic, such as an ear characteristic (e.g., ear loss or ear orientation), wrapping, hair pinning, or control input, sensed by the in-situ sensors308at different locations in the worksite based upon one or more of the information maps358. For example, where the predictive model is a predictive ear characteristic model that models a relationship between one or more ear characteristics (e.g., ear loss, ear orientation, etc.) sensed by in-situ sensors308and one or more of genotype values from a genotype map, crop state values from a crop state map, and weed values from a weed map, then predictive map generator312generates a functional predictive ear characteristic map that predicts values of one or more ear characteristics at different locations at the worksite based on one or more of the mapped values at those locations and the predictive ear characteristic model. This is merely an example.

In some examples, the type of values in the functional predictive map263may be the same as the in-situ data type sensed by the in-situ sensors308. In some instances, the type of values in the functional predictive map263may have different units from the data sensed by the in-situ sensors308. In some examples, the type of values in the functional predictive map263may be different from the data type sensed by the in-situ sensors308but have a relationship to the type of data type sensed by the in-situ sensors308. For example, in some examples, the data type sensed by the in-situ sensors308may be indicative of the type of values in the functional predictive map263. In some examples, the type of data in the functional predictive map263may be different than the data type in the information maps358. In some instances, the type of data in the functional predictive map263may have different units from the data in the information maps358. In some examples, the type of data in the functional predictive map263may be different from the data type in the information map358but has a relationship to the data type in the information map358. For example, in some examples, the data type in the information maps358may be indicative of the type of data in the functional predictive map263. In some examples, the type of data in the functional predictive map263is different than one of, or both of, the in-situ data type sensed by the in-situ sensors308and the data type in the information maps358. In some examples, the type of data in the functional predictive map263is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors308and the data type in information maps358. In some examples, the type of data in the functional predictive map263is the same as one of the in-situ data type sensed by the in-situ sensors308or the data type in the information maps358, and different than the other.

As shown inFIG.4, predictive map264predicts the value of a sensed characteristic (sensed by in-situ sensors308), or a characteristic related to the sensed characteristic, at various locations across the worksite based upon one or more information values in one or more information maps358at those locations and using the predictive model311. For example, if predictive model generator310has generated a predictive model indicative of a relationship between crop state values and control input values, then, given the crop state value at different locations across the worksite, predictive map generator312generates a predictive map264that predicts control input values at different locations across the worksite. The crop state value, obtained from the crop state map, at those locations and the relationship between crop state values and the control input values, obtained from the predictive model311, are used to generate the predictive map264. This is merely one example.

Some variations in the data types that are mapped in the information maps358, the data types sensed by in-situ sensors308, and the data types predicted on the predictive map264will now be described.

In some examples, the data type in one or more information maps358is different from the data type sensed by in-situ sensors308, yet the data type in the predictive map264is the same as the data type sensed by the in-situ sensors308. For instance, the information map358may be a genotype map, and the variable sensed by the in-situ sensors308may be hair pinning. The predictive map264may then be a predictive hair pinning map that maps predictive hair pinning values to different geographic locations in the in the worksite.

Also, in some examples, the data type in the information map358is different from the data type sensed by in-situ sensors308, and the data type in the predictive map264is different from both the data type in the information map358and the data type sensed by the in-situ sensors308.

In some examples, the information map358is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors308, yet the data type in the predictive map264is the same as the data type sensed by the in-situ sensors308. For instance, the information map358may be a genotype map generated during a previous planting/seeding operation on the field, and the variable sensed by the in-situ sensors308may be an ear characteristic. The predictive map264may then be a predictive ear characteristic map that maps predictive ear characteristic values to different geographic locations in the worksite. This is merely an example.

In some examples, the information map358is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors308, and the data type in the predictive map264is also the same as the data type sensed by the in-situ sensors308. For instance, the information map358may be an ear characteristic map generated during a previous year, and the variable sensed by the in-situ sensors308may be an ear characteristic. The predictive map264may then be a predictive ear characteristic map that maps predictive values of the ear characteristic to different geographic locations in the field. In such an example, the relative ear characteristic differences in the georeferenced information map358from the prior year can be used by predictive model generator310to generate a predictive model that models a relationship between the relative ear characteristic differences on the information map358and the ear characteristic values sensed by in-situ sensors308during the current operation. The predictive model is then used by predictive map generator310to generate a predictive ear characteristic map. This is merely an example.

In another example, the information map358may be a weed map generated during a prior operation in the same year, such as a spraying operation performed by a spraying machine, and the variable sensed by the in-situ sensors308during the current harvesting operation may be an ear characteristic. The predictive map264may then be a predictive ear characteristic map that maps predictive ear characteristic values to different geographic locations in the worksite. In such an example, a map of the weed values at time of the spraying operation is geo-referenced, recorded, and provided to agricultural harvester100as an information map358of weed values. In-situ sensors308during a current operation can detect an ear characteristic at geographic locations in the field and predictive model generator310may then build a predictive model that models a relationship between the ear characteristic at time of the current operation and weed values at the time of the spraying operation. This is because the weed values at the time of the spraying operation are likely to be the same as at the time of the current operation or may be more accurate or otherwise may be more reliable than weed values obtained in other ways. This is merely an example.

In some examples, predictive map264can be provided to the control zone generator313. Control zone generator313groups adjacent portions of an area into one or more control zones based on data values of predictive map264that are associated with those adjacent portions. A control zone may include two or more contiguous portions of a worksite, 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 to alter a setting of controllable subsystems316may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map264. In that case, control zone generator313parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems316. In another example, 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 subsystem316or for groups of controllable subsystems316. The control zones may be added to the predictive map264to obtain predictive control zone map265. Predictive control zone map265can thus be similar to predictive map264except that predictive control zone map265includes control zone information defining the control zones. Thus, a functional predictive map263, as described herein, may or may not include control zones. Both predictive map264and predictive control zone map265are functional predictive maps263. In one example, a functional predictive map263does not include control zones, such as predictive map264. In another example, a functional predictive map263does include control zones, such as predictive control zone map265.

It will also be appreciated that control zone generator313can cluster values to generate control zones and the control zones can be added to predictive control zone map265, or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibrating agricultural harvester100or both. In other examples, the control zones may be presented to the operator360and used to control or calibrate agricultural harvester100, and, in other examples, the control zones may be presented to the operator360or another user, such as a remote user366, or stored for later use.

Predictive map264or predictive control zone map265, or both, are provided to control system314, which generates control signals based upon the predictive map264or predictive control zone map265or both. In some examples, communication system controller329controls communication system306to communicate the predictive map264or predictive control zone map265or control signals based on the predictive map264or predictive control zone map265to other mobile machines that are operating at the same field or in the same operation. In some examples, communication system controller329controls the communication system306to send the predictive map264, predictive control zone map265, or both to other remote systems, such as remote computing systems368.

Control system314includes communication system controller329, interface controller330, propulsion controller331, path planning controller334, end divider controllers335, zone controller336, and control system314can include other items339. Controllable subsystems316end dividers subsystem340, propulsion subsystem350, steering subsystem352, and subsystems316can include a wide variety of other controllable subsystems356.

Interface controller330is operable to generate control signals to control interface mechanisms, such as operator interface mechanisms318or user interfaces364, or both. The interface controller330is also operable to present the predictive map264or predictive control zone map265or other information derived from or based on the predictive map264, predictive control zone map265, or both to operator360or a remote user366, or both. As an example, interface controller330generates control signals to control a display mechanism to display one or both of predictive map264and predictive control zone map265for the operator360or a remote user366, or both. Interface controller330may generate operator or user actuatable mechanisms that are displayed and can be actuated by the operator or user to interact with the displayed map. The operator or user can edit the map by, for example, correcting a value displayed on the map, based on the operator's or the user's observation.

Path planning controller334illustratively generates control signals to control steering subsystem352to steer agricultural harvester100according to a desired path or according to desired parameters, such as desired steering angles. Path planning controller334can control a path planning system to generate a route for mobile machine100and can control propulsion subsystem350and steering subsystem352to steer mobile machine100along that route. Path planning controller334can generate control signals based on one or more of predictive map264, predictive map with control zones265, information maps365, or control inputs, such as by an operator or user.

Propulsion controller331illustratively generates control signals to control propulsion subsystem350to control a speed characteristic of mobile machine100, such as one or more of travel speed, acceleration, and deceleration. Propulsion subsystem350may include various power train components of mobile machine100, such as, but not limited to, an engine or motor, and a transmission (or gear box). Propulsion controller331can generate control signals based on one or more of predictive map264, predictive map with control zones265, information maps365, or control inputs, such as by an operator or user.

End divider controllers335illustratively generate control signals to control one or more operating parameters of the end dividers (e.g.,146,150or147). For example, each end divider controller335can generate control signals to control a respective end divider, or a single end divider controller335can control multiple end dividers. End divider controllers335generate control signals to control end divider actuators342. For instance an end divider actuator342may control the movement (e.g., extension/retraction of an end divider or the speed of rotation of an end divider and/or the direction of rotation of the end divider). In some examples, each end divider has its own respective actuator(s)342. In some examples, a single actuator342may control an operational parameter of multiple end dividers. End divider controllers335can generate control signals based on one or more of predictive map264, predictive map with control zones265, information maps365, or control inputs, such as by an operator or user.

Zone controller336illustratively generates control signals to control one or more controllable subsystems316to control operation of the one or more controllable subsystems316based on the predictive control zone map265.

Other controllers339included on the mobile machine100, or at other locations in agricultural system300, can control other subsystems316based on the predictive map264or predictive control zone map265or both as well.

While the illustrated example ofFIG.4shows that various components of agricultural harvesting system architecture300are located on agricultural harvester100, it will be understood that in other examples one or more of the components illustrated on agricultural harvester100inFIG.4can be located at other locations, such as one or more remote computing systems368. For instance, one or more of data stores302, map selector309, predictive model generator310, predictive model311, predictive map generator312, functional predictive maps (e.g.,264and265), control zone generator313, and control system314can be located remotely from mobile machine100but can communicate with (or be communicated to) agricultural harvester100via communication system306and network359. Thus, the predictive models311and functional predictive maps263may be generated at remote locations away from agricultural harvester100and communicated to mobile machine100over network302, for instance, communication system306can download the predictive models311and functional predictive maps263from the remote locations and store them in data store302. In other examples, agricultural harvester100may access the predictive models311and functional predictive maps263at the remote locations without downloading the predictive models311and functional predictive maps263. The information used in the generation of the predictive models311and functional predictive maps263may be provided to the predictive model generator310and the predictive map generator312at those remote locations over network359, for example in-situ sensor data generator by in-situ sensors308can be provided over network359to the remote locations. Similarly, information maps358can be provided to the remote locations.

Similarly, where various components are located remotely from agricultural harvester100, those components can receive data from components of agricultural harvester100over network359. For example, where predictive model generator310and predictive map generator312are located remotely from agricultural harvester100, such as at remote computing systems368, data generated by in-situ sensors308and geographic position sensors304, for instance, can be communicated to the remote computing systems368over network359. Additionally, information maps358can be obtained by remote computing systems368over network359or over another network.

Similarly, the remote systems may include a respective control system or control signal generator that generates control output (e.g., control signals) that are communicated to agricultural harvester100and used by the local control system314for the control of agricultural harvester100.

FIG.5is a block diagram of a portion of the agricultural system architecture300shown inFIG.4. Particularly,FIG.5shows, among other things, examples of the predictive model generator310and the predictive map generator312in more detail.FIG.5also illustrates information flow among the various components shown. The predictive model generator310receives one or more of a genotype map430, a crop state map431, a weed map432, a harvest coverage map, and another type of map433. Predictive model generator310also receives a geographic location424, or an indication of a geographic location, such as from geographic position sensors304. Geographic location424illustratively represents the geographic location of a value detected by in-situ sensors308. In some examples, the geographic position of the agricultural harvester100, as detected by geographic position sensors304, will not be the same as the geographic position on the field to which a value detected by in-situ sensors308corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor304, along with timing, machine speed and heading, machine dimensions, and sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., field of view), can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor308corresponds.

In-situ sensors308illustratively include ear characteristic sensors380, such as ear loss sensors382and ear orientation sensors384, as well as processing system338. In some examples, processing system338is separate from in-situ sensors308(such as the example shown inFIG.4). In some instances, ear sensors380may be located on-board agricultural harvester100. The processing system338processes sensor data generated from ear sensor380to generate processed sensor data440indicative of ear characteristic values, such as one or more of ear loss values and ear orientation values.

As shown inFIG.5, the example predictive model generator310includes an ear loss-to-genotype model generator441, an ear loss-to-crop state model generator442, an ear loss-to-weed model generator443, an ear loss-to-harvest coverage model generator4000, an ear loss-to-other characteristic model generator444, an ear orientation-to-genotype model generator445, an ear orientation-to-crop state model generator446, an ear orientation-to-weed model generator447, and an ear orientation-to-other characteristic model generator448. In other examples, the predictive model generator310may include additional, fewer, or different components than those shown in the example ofFIG.5. Consequently, in some examples, the predictive model generator310may include other items449as well, which may include other types of predictive model generators to generate other types of ear characteristic models.

Ear loss-to-genotype model generator441identifies a relationship between ear loss value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear loss value(s), detected in the in-situ sensor data440, correspond, and genotype values from the genotype map430corresponding to the same geographic location(s) to which the detected ear loss value(s) correspond. Based on this relationship established by ear loss-to-genotype model generator441, ear loss-to-genotype model generator441generates a predictive ear characteristic model (e.g., a predictive ear loss model). The predictive ear characteristic model is used by ear loss map generator452to predict ear loss at different locations in the field based upon the georeferenced genotype values contained in the genotype map430at the same locations in the field. Thus, for a given location in the field, an ear loss value can be predicted at the given location based on the predictive ear characteristic model and the genotype value, from the genotype map430, at that given location.

Ear loss-to-crop state model generator442identifies a relationship between ear loss value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear loss value(s), detected in the in-situ sensor data440, correspond, and crop state values from the crop state map431corresponding to the same geographic location(s) to which the detected ear loss value(s) correspond. Based on this relationship established by ear loss-to-crop state model generator442, ear loss-to-crop state model generator442generates a predictive ear characteristic model (e.g., a predictive ear loss model). The predictive ear characteristic model is used by ear loss map generator452to predict ear loss at different locations in the field based upon the georeferenced crop state values contained in the crop state map431at the same locations in the field. Thus, for a given location in the field, an ear loss value can be predicted at the given location based on the predictive ear characteristic model and the crop state value, from the crop state map431, at that given location.

Ear loss-to-weed model generator443identifies a relationship between ear loss value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear loss value(s), detected in the in-situ sensor data440, correspond, and weed values from the weed map432corresponding to the same geographic location(s) to which the detected ear loss value(s) correspond. Based on this relationship established by ear loss-to-weed model generator443, ear loss-to-weed model generator443generates a predictive ear characteristic model (e.g., a predictive ear loss model). The predictive ear characteristic model is used by ear loss map generator452to predict ear loss at different locations in the field based upon the georeferenced weed values contained in the weed map432at the same locations in the field. Thus, for a given location in the field, an ear loss value can be predicted at the given location based on the predictive ear characteristic model and the weed value, from the weed map432, at that given location.

Ear loss-to-harvest coverage model generator4000identifies a relationship between ear loss value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear loss value(s), detected in the in-situ sensor data440, correspond, and harvest coverage values from the harvest coverage map4000corresponding to the same geographic location(s) to which the detected ear loss value(s) correspond. Based on this relationship established by ear loss-to-harvest coverage model generator4000, ear loss-to-harvest coverage model generator4000generates a predictive ear characteristic model (e.g., a predictive ear loss model). The predictive ear characteristic model is used by ear loss map generator452to predict ear loss at different locations in the field based upon the georeferenced harvest coverage values contained in the harvest coverage map434at the same locations in the field. Thus, for a given location in the field, an ear loss value can be predicted at the given location based on the predictive ear characteristic model and the harvest coverage value, from the harvest coverage map434, at that given location.

Ear loss-to-other characteristic model generator444identifies a relationship between ear loss value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear loss value(s), detected in the in-situ sensor data440, correspond, and values of an other characteristic from an other map433corresponding to the same geographic location(s) to which the detected ear loss value(s) correspond. Based on this relationship established by ear loss-to-other characteristic model generator444, ear loss-to-other characteristic model generator444generates a predictive ear characteristic model (e.g., a predictive ear loss model). The predictive ear characteristic model is used by ear loss map generator452to predict ear loss at different locations in the field based upon the georeferenced values of the other characteristic contained in the other map433at the same locations in the field. Thus, for a given location in the field, an ear loss value can be predicted at the given location based on the predictive ear characteristic model and the value of the other characteristic, from the other map433, at that given location.

Ear orientation-to-genotype model generator445identifies a relationship between ear orientation value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear orientation value(s), detected in the in-situ sensor data440, correspond, and genotype values from the genotype map430corresponding to the same geographic location(s) to which the detected ear orientation value(s) correspond. Based on this relationship established by ear orientation-to-genotype model generator445, ear orientation-to-genotype model generator445generates a predictive ear characteristic model (e.g., a predictive ear orientation model). The predictive ear characteristic model is used by ear orientation map generator453to predict ear orientation at different locations in the field based upon the georeferenced genotype values contained in the genotype map430at the same locations in the field. Thus, for a given location in the field, an ear orientation value can be predicted at the given location based on the predictive ear characteristic model and the genotype value, from the genotype map430, at that given location.

Ear orientation-to-crop state model generator446identifies a relationship between ear orientation value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear orientation value(s), detected in the in-situ sensor data440, correspond, and crop state values from the crop state map431corresponding to the same geographic location(s) to which the detected ear orientation value(s) correspond. Based on this relationship established by ear orientation-to-crop state model generator446, ear orientation-to-crop state model generator446generates a predictive ear characteristic model (e.g., a predictive ear orientation model). The predictive ear characteristic model is used by ear orientation map generator453to predict ear orientation at different locations in the field based upon the georeferenced crop state values contained in the crop state map431at the same locations in the field. Thus, for a given location in the field, an ear orientation value can be predicted at the given location based on the predictive ear characteristic model and the crop state value, from the crop state map431, at that given location.

Ear orientation-to-weed model generator447identifies a relationship between ear orientation value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear orientation value(s), detected in the in-situ sensor data440, correspond, and weed values from the weed map432corresponding to the same geographic location(s) to which the detected ear orientation value(s) correspond. Based on this relationship established by ear orientation-to-weed model generator447, ear orientation-to-weed model generator447generates a predictive ear characteristic model (e.g., a predictive ear orientation model). The predictive ear characteristic model is used by ear orientation map generator453to predict ear orientation at different locations in the field based upon the georeferenced weed values contained in the weed map432at the same locations in the field. Thus, for a given location in the field, an ear orientation value can be predicted at the given location based on the predictive ear characteristic model and the weed value, from the weed map432, at that given location.

Ear orientation-to-other characteristic model generator448identifies a relationship between ear orientation value(s) detected in in-situ sensor data440, at geographic location(s) to which the ear orientation value(s), detected in the in-situ sensor data440, correspond, and values of an other characteristic from an other map433corresponding to the same geographic location(s) to which the detected ear orientation value(s) correspond. Based on this relationship established by ear orientation-to-other characteristic model generator448, ear orientation-to-other characteristic model generator448generates a predictive ear characteristic model (e.g., a predictive ear orientation model). The predictive ear characteristic model is used by ear orientation map generator453to predict ear orientation at different locations in the field based upon the georeferenced values of the other characteristic contained in the other map433at the same locations in the field. Thus, for a given location in the field, an ear orientation value can be predicted at the given location based on the predictive ear characteristic model and the value of the other characteristic, from the other map433, at that given location.

In light of the above, the predictive model generator310is operable to produce a plurality of predictive ear characteristic models, such as one or more of the predictive ear characteristic models generated by model generators441,442,443,444,4000,445,446,447,448, and449. In another example, two or more of the predictive models described above may be combined into a single predictive ear characteristic model, such as a predictive ear characteristic model that predicts one or more ear characteristics (e.g., one or more of ear loss and ear orientation) based upon two or more of the genotype values, the crop state value, the weed values, the harvest coverage values, and the other characteristic values at different locations in the field. Any of these ear characteristic models, or combinations thereof, are represented collectively by predictive ear characteristic model450inFIG.5.

The predictive ear characteristic model450is provided to predictive map generator312. In the example ofFIG.5, predictive map generator312includes ear characteristic map generator451. In other examples, predictive map generator312may include additional or different map generators. Thus, in some examples, predictive map generator312may include other items458which may include other types of map generators to generate other types of maps.

Ear characteristic map generator451receives one or more of the genotype map430, the crop state map431, the weed map432, the harvest coverage map434, and an other map433, along with the predictive ear characteristic model450which predicts one or more ear characteristics (e.g., one or more of ear loss and ear orientation) based upon one or more of genotype value, a crop state value, a weed value, a harvest coverage value, and an other characteristic value, and generates a predictive map that predicts one or more ear characteristics at different locations in the field, such as functional predictive ear characteristic map460.

Ear characteristic map generator451, itself, includes ear loss map generator452and ear orientation map generator453. In other examples, ear characteristic map generator451may include additional or different map generators. Thus, in some examples, ear characteristic map generator451may include other items456which may include other types of ear characteristic map generator to generate other types of ear characteristic maps.

Ear loss map generator452receives one or more of the genotype map430, the crop state map431, the weed map432, the harvest coverage map4000, and an other map433, along with the predictive characteristic model450which predicts ear loss based upon one or more of a genotype value, a crop state value, a weed value, a harvest coverage value, and an other characteristic value, and generates a predictive map that predicts ear loss at different locations in the field, such as functional predictive ear loss map470, as a functional predictive ear characteristic map460.

Ear orientation map generator453receives one or more of the genotype map430, the crop state map431, the weed map432, and an other map433, along with the predictive characteristic model450which predicts ear orientation based upon one or more of a genotype value, a crop state value, a weed value, and an other characteristic value, and generates a predictive map that predicts ear orientation at different locations in the field, such as functional predictive ear orientation map480, as a functional predictive ear characteristic map460.

Predictive map generator312thus outputs a functional predictive ear characteristic map460that is predictive of one or more ear characteristics (e.g., one or more of ear loss or ear orientation). Functional predictive ear characteristic map460is a predictive map264. The functional predictive ear characteristic map460, in one example, predicts one or more ear characteristics (e.g., one or more of ear loss and ear orientation) at different locations in a field. The functional predictive ear characteristic map460may be provided to control zone generator313, control system314, or both. Control zone generator313generates control zones and incorporates those control zones into the functional predictive ear characteristic map460to produce a predictive control zone map265, that is a functional predictive ear characteristic control zone map461. For example, functional predictive ear loss map470can provided to control zone generator313to produce, as functional predictive ear characteristic control zone map461, a functional predictive ear loss control zone map471, In another example, functional predictive ear orientation map480can be provided to control zone generator313to produce, as a functional predictive ear characteristic control zone map461, a functional predictive ear orientation control zone map481.

One or both of functional predictive ear characteristic map460and functional predictive ear characteristic control zone map461may be provided to control system314, which generates control signals to control one or more of the controllable subsystems316based upon the functional predictive ear characteristic map460, the functional predictive ear characteristic control zone map461, or both.

FIG.6is a block diagram of a portion of the agricultural system architecture300shown inFIG.4. Particularly,FIG.6shows, among other things, examples of the predictive model generator310and the predictive map generator312in more detail.FIG.6also illustrates information flow among the various components shown. The predictive model generator310receives one or more of a genotype map430, a crop state map431, a weed map432, a harvest coverage map, and another type of map433. Predictive model generator310also receives a geographic location1424, or an indication of a geographic location, such as from geographic position sensors304. Geographic location1424illustratively represents the geographic location of a value detected by in-situ sensors308. In some examples, the geographic position of the agricultural harvester100, as detected by geographic position sensors304, will not be the same as the geographic position on the field to which a value detected by in-situ sensors308corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor304, along with timing, machine speed and heading, machine dimensions, and sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., field of view), can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor308corresponds. For example, in the context of a control input value detected by control input sensors390, geographic location1424may indicate the geographic location of the component of the agricultural harvester, that is controlled by the detected control input.

In-situ sensors308illustratively include control input sensors390, as well as processing system338. In some examples, processing system338is separate from in-situ sensors308(such as the example shown inFIG.4). In some instances, control input sensors390may be located on-board agricultural harvester100. The processing system338processes sensor data generated from control input sensors390to generate processed sensor data1440indicative of control input values.

As shown inFIG.6, the example predictive model generator310includes a control input-to-genotype model generator1441, a control input-to-crop state model generator1442, a control input-to-weed model generator1443, a control input-to-harvest coverage model generator1445, and a control input-to-other characteristic model generator1444. In other examples, the predictive model generator310may include additional, fewer, or different components than those shown in the example ofFIG.6. Consequently, in some examples, the predictive model generator310may include other items1449as well, which may include other types of predictive model generators to generate other types of control input models.

Control input-to-genotype model generator1441identifies a relationship between control input value(s) detected in in-situ sensor data1440, at geographic location(s) to which the control input value(s), detected in the in-situ sensor data1440, correspond, and genotype values from the genotype map430corresponding to the same geographic location(s) to which the detected control input value(s) correspond. Based on this relationship established by control input-to-genotype model generator1441, control input-to-genotype model generator1441generates a predictive control input model. The predictive control input model is used by control input map generator1452to predict control input at different locations in the field based upon the georeferenced genotype values contained in the genotype map430at the same locations in the field. Thus, for a given location in the field, a control input value can be predicted at the given location based on the predictive control input model and the genotype value, from the genotype map430, at that given location.

Control input-to-crop state model generator1442identifies a relationship between control input value(s) detected in in-situ sensor data1440, at geographic location(s) to which the control input value(s), detected in the in-situ sensor data1440, correspond, and crop state values from the crop state map431corresponding to the same geographic location(s) to which the detected control input value(s) correspond. Based on this relationship established by control input-to-crop state model generator1442, control input-to-crop state model generator1442generates a predictive control input model. The predictive control input model is used by control input map generator1452to predict control input at different locations in the field based upon the georeferenced crop state values contained in the crop state map431at the same locations in the field. Thus, for a given location in the field, a control input value can be predicted at the given location based on the predictive control input model and the crop state value, from the crop state map431, at that given location.

Control input-to-weed model generator1443identifies a relationship between control input value(s) detected in in-situ sensor data1440, at geographic location(s) to which the control input value(s), detected in the in-situ sensor data1440, correspond, and weed values from the weed map432corresponding to the same geographic location(s) to which the detected control input value(s) correspond. Based on this relationship established by control input-to-weed model generator1443, control input-to-weed model generator1443generates a predictive control input model. The predictive control input model is used by control input map generator1452to predict control input at different locations in the field based upon the georeferenced weed values contained in the weed map432at the same locations in the field. Thus, for a given location in the field, a control input value can be predicted at the given location based on the predictive control input model and the weed value, from the weed map432, at that given location.

Control input-to-harvest coverage model generator1445identifies a relationship between control input value(s) detected in in-situ sensor data1440, at geographic location(s) to which the control input value(s), detected in the in-situ sensor data1440, correspond, and harvest coverage values from the harvest coverage map434corresponding to the same geographic location(s) to which the detected control input value(s) correspond. Based on this relationship established by control input-to-harvest coverage model generator1445, control input-to-harvest coverage model generator1445generates a predictive control input model. The predictive control input model is used by control input map generator1452to predict control input at different locations in the field based upon the georeferenced harvest coverage values contained in the weed map432at the same locations in the field. Thus, for a given location in the field, a control input value can be predicted at the given location based on the predictive control input model and the harvest coverage value, from the harvest coverage map434, at that given location.

Control input-to-other characteristic model generator1444identifies a relationship between control input value(s) detected in in-situ sensor data1440, at geographic location(s) to which the control input value(s), detected in the in-situ sensor data1440, correspond, and values of an other characteristic from an other map433corresponding to the same geographic location(s) to which the detected control input value(s) correspond. Based on this relationship established by control input-to-other characteristic model generator1444, control input-to-other characteristic model generator1444generates a predictive control input model. The predictive control input model is used by control input map generator1452to predict control input at different locations in the field based upon the georeferenced values of the other characteristic contained in the other map433at the same locations in the field. Thus, for a given location in the field, a control input value can be predicted at the given location based on the predictive control input model and the value of the other characteristic, from the other map433, at that given location.

In light of the above, the predictive model generator310is operable to produce a plurality of predictive control input models, such as one or more of the predictive control input models generated by model generators1441,1442,1443,1444,1445, and1449. In another example, two or more of the predictive models described above may be combined into a single predictive control input model, such as a predictive control input model that predicts control input based upon two or more of the genotype values, the crop state value, the weed values, the harvest coverage values, and the other characteristic values at different locations in the field. Any of these control input models, or combinations thereof, are represented collectively by predictive control input model1450inFIG.6.

The predictive control input model1450is provided to predictive map generator312. In the example ofFIG.6, predictive map generator312includes control input map generator1452. In other examples, predictive map generator312may include additional or different map generators. Thus, in some examples, predictive map generator312may include other items1458which may include other types of map generators to generate other types of maps.

Control input map generator1452receives one or more of the genotype map430, the crop state map431, the weed map432, the harvest coverage map434, and an other map433, along with the predictive control input model1450which predicts control input based upon one or more of genotype value, a crop state value, a weed value, and an other characteristic value, and generates a predictive map that predicts control input at different locations in the field, such as functional predictive control input map1460.

Predictive map generator312thus outputs a functional predictive control input map1460that is predictive of control input. Functional predictive control input map1460is a predictive map264. The functional predictive control input map1460, in one example, predicts control input at different locations in a field. The functional predictive control input map1460may be provided to control zone generator313, control system314, or both. Control zone generator313generates control zones and incorporates those control zones into the functional predictive control input map1460to produce a predictive control zone map265, that is a functional predictive control input control zone map1461.

One or both of functional predictive control input map1460and functional predictive control input control zone map1461may be provided to control system314, which generates control signals to control one or more of the controllable subsystems316based upon the functional predictive control input map1460, the functional predictive control input control zone map1461, or both.

FIG.7is a block diagram of a portion of the agricultural system architecture300shown inFIG.4. Particularly,FIG.7shows, among other things, examples of the predictive model generator310and the predictive map generator312in more detail.FIG.7also illustrates information flow among the various components shown. The predictive model generator310receives one or more of a genotype map430, a crop state map431, a weed map432, a harvest coverage map434, and another type of map433. Predictive model generator310also receives a geographic location2424, or an indication of a geographic location, such as from geographic position sensors304. Geographic location2424illustratively represents the geographic location of a value detected by in-situ sensors308. In some examples, the geographic position of the agricultural harvester100, as detected by geographic position sensors304, will not be the same as the geographic position on the field to which a value detected by in-situ sensors308corresponds. It will be appreciated, that the geographic position indicated by geographic position sensor304, along with timing, machine speed and heading, machine dimensions, and sensor position (e.g., relative to geographic position sensor), sensor parameters (e.g., field of view), can be used to derive a geographic location at the field to which a value a detected by an in-situ sensor308corresponds.

In-situ sensors308illustratively include wrapping sensors386and hair pinning sensors388, as well as processing system338. In some examples, processing system338is separate from in-situ sensors308(such as the example shown inFIG.4). In some instances, wrapping sensors386or hair pinning sensors388, or both, may be located on-board agricultural harvester100. The processing system338processes sensor data generated from wrapping sensors386and hair pinning sensors388to generate processed sensor data2440indicative of wrapping values and hair pinning values.

As shown inFIG.7, the example predictive model generator310includes a wrapping-to-genotype model generator2441, a wrapping-to-crop state model generator2442, a wrapping-to-weed model generator2443, a wrapping-to-harvest coverage model generator5000, a wrapping-to-other characteristic model generator2444, a hair pinning-to-genotype model generator2445, a hair pinning-to-crop state model generator2446, a hair pinning-to-weed model generator2447, a hair pinning-to-harvest coverage model generator6000, and a hair pinning-to-other characteristic model generator2448. In other examples, the predictive model generator310may include additional, fewer, or different components than those shown in the example ofFIG.7. Consequently, in some examples, the predictive model generator310may include other items as well, which may include other types of predictive model generators to generate other types of models.

Wrapping-to-genotype model generator2441identifies a relationship between wrapping value(s) detected in in-situ sensor data2440, at geographic location(s) to which the wrapping value(s), detected in the in-situ sensor data2440, correspond, and genotype values from the genotype map430corresponding to the same geographic location(s) to which the detected wrapping value(s) correspond. Based on this relationship established by wrapping-to-genotype model generator2441, wrapping-to-genotype model generator2441generates a predictive wrapping model. The predictive wrapping model is used by wrapping map generator2452to predict wrapping at different locations in the field based upon the georeferenced genotype values contained in the genotype map430at the same locations in the field. Thus, for a given location in the field, a wrapping value can be predicted at the given location based on the predictive wrapping model and the genotype value, from the genotype map430, at that given location.

Wrapping-to-crop state model generator2442identifies a relationship between wrapping value(s) detected in in-situ sensor data2440, at geographic location(s) to which the wrapping value(s), detected in the in-situ sensor data2440, correspond, and crop state values from the crop state map431corresponding to the same geographic location(s) to which the detected wrapping value(s) correspond. Based on this relationship established by wrapping-to-crop state model generator2442, wrapping-to-crop state model generator2442generates a predictive wrapping model. The predictive wrapping model is used by wrapping map generator2452to predict wrapping at different locations in the field based upon the georeferenced crop state values contained in the crop state map431at the same locations in the field. Thus, for a given location in the field, a wrapping value can be predicted at the given location based on the predictive wrapping model and the crop state value, from the crop state map431, at that given location.

Wrapping-to-weed model generator2443identifies a relationship between wrapping value(s) detected in in-situ sensor data2440, at geographic location(s) to which the wrapping value(s), detected in the in-situ sensor data2440, correspond, and weed values from the weed map432corresponding to the same geographic location(s) to which the detected wrapping value(s) correspond. Based on this relationship established by wrapping-to-weed model generator2443, wrapping-to-weed model generator2443generates a predictive wrapping model. The predictive wrapping model is used by wrapping map generator2452to predict wrapping at different locations in the field based upon the georeferenced weed values contained in the weed map432at the same locations in the field. Thus, for a given location in the field, a wrapping value can be predicted at the given location based on the predictive wrapping model and the weed value, from the weed map432, at that given location.

Wrapping-to-harvest coverage model generator5000identifies a relationship between wrapping value(s) detected in in-situ sensor data2440, at geographic location(s) to which the wrapping value(s), detected in the in-situ sensor data2440, correspond, and harvest coverage values from the harvest coverage map434corresponding to the same geographic location(s) to which the detected wrapping value(s) correspond. Based on this relationship established by wrapping-to-harvest coverage model generator5000, wrapping-to-harvest coverage model generator5000generates a predictive wrapping model. The predictive wrapping model is used by wrapping map generator2452to predict wrapping at different locations in the field based upon the georeferenced harvest coverage values contained in the harvest coverage map434at the same locations in the field. Thus, for a given location in the field, a wrapping value can be predicted at the given location based on the predictive wrapping model and the harvest coverage value, from the harvest coverage map434, at that given location.

Wrapping-to-other characteristic model generator2444identifies a relationship between wrapping value(s) detected in in-situ sensor data2440, at geographic location(s) to which the wrapping value(s), detected in the in-situ sensor data2440, correspond, and values of an other characteristic from an other map433corresponding to the same geographic location(s) to which the detected wrapping value(s) correspond. Based on this relationship established by wrapping-to-other characteristic model generator2444, wrapping-to-other characteristic model generator2444generates a predictive wrapping model. The predictive wrapping model is used by wrapping map generator2452to predict wrapping at different locations in the field based upon the georeferenced values of the other characteristic contained in the other map433at the same locations in the field. Thus, for a given location in the field, a wrapping value can be predicted at the given location based on the predictive wrapping model and the value of the other characteristic, from the other map433, at that given location.

In light of the above, the predictive model generator310is operable to produce a plurality of predictive wrapping models, such as one or more of the predictive wrapping models generated by model generators2441,2442,2443,2444,5000and2449. In another example, two or more of the predictive models described above may be combined into a single predictive wrapping model, such as a predictive wrapping model that predicts wrapping based upon two or more of the genotype values, the crop state value, the weed values, the harvest coverage values, and the other characteristic values at different locations in the field. Any of these wrapping models, or combinations thereof, are represented collectively by predictive wrapping model2450inFIG.7.

The predictive wrapping model2450is provided to predictive map generator312. In the example ofFIG.7, predictive map generator312includes wrapping map generator2452. In other examples, predictive map generator312may include additional or different map generators. Thus, in some examples, predictive map generator312may include other items2458which may include other types of map generators to generate other types of maps.

Wrapping map generator2452receives one or more of the genotype map430, the crop state map431, the weed map432, the harvest coverage map434, and an other map433, along with the predictive wrapping model2450which predicts wrapping based upon one or more of a genotype value, a crop state value, a weed value, a harvest coverage value, and an other characteristic value, and generates a predictive map that predicts wrapping at different locations in the field, such as functional predictive wrapping map2470.

Predictive map generator312thus outputs a functional predictive wrapping map2470that is predictive of wrapping. Functional predictive wrapping map2470is a predictive map264. The functional predictive wrapping map2470, in one example, predicts wrapping at different locations in a field. The functional predictive wrapping map2470may be provided to control zone generator313, control system314, or both. Control zone generator313generates control zones and incorporates those control zones into the functional predictive wrapping map2470to produce a predictive control zone map265, that is a functional predictive wrapping control zone map2471.

Hair pinning-to-genotype model generator2445identifies a relationship between hair pinning value(s) detected in in-situ sensor data2440, at geographic location(s) to which the hair pinning value(s), detected in the in-situ sensor data2440, correspond, and genotype values from the genotype map430corresponding to the same geographic location(s) to which the detected hair pinning value(s) correspond. Based on this relationship established by hair pinning-to-genotype model generator2445, hair pinning-to-genotype model generator2445generates a predictive hair pinning model. The predictive hair pinning model is used by hair pinning map generator2453to predict hair pinning at different locations in the field based upon the georeferenced genotype values contained in the genotype map430at the same locations in the field. Thus, for a given location in the field, a hair pinning value can be predicted at the given location based on the predictive hair pinning model and the genotype value, from the genotype map430, at that given location.

Hair pinning-to-crop state model generator2446identifies a relationship between hair pinning value(s) detected in in-situ sensor data2440, at geographic location(s) to which the hair pinning value(s), detected in the in-situ sensor data2440, correspond, and crop state values from the crop state map431corresponding to the same geographic location(s) to which the detected hair pinning value(s) correspond. Based on this relationship established by hair pinning-to-crop state model generator2446, hair pinning-to-crop state model generator2446generates a predictive hair pinning model. The predictive hair pinning model is used by hair pinning map generator2453to predict hair pinning at different locations in the field based upon the georeferenced crop state values contained in the crop state map431at the same locations in the field. Thus, for a given location in the field, a hair pinning value can be predicted at the given location based on the predictive hair pinning model and the crop state value, from the crop state map431, at that given location.

Hair pinning-to-weed model generator2447identifies a relationship between hair pinning value(s) detected in in-situ sensor data2440, at geographic location(s) to which the hair pinning value(s), detected in the in-situ sensor data2440, correspond, and weed values from the weed map432corresponding to the same geographic location(s) to which the detected hair pinning value(s) correspond. Based on this relationship established by hair pinning-to-weed model generator2447, hair pinning-to-weed model generator2447generates a predictive hair pinning model. The predictive hair pinning model is used by hair pinning map generator2453to predict hair pinning at different locations in the field based upon the georeferenced weed values contained in the weed map432at the same locations in the field. Thus, for a given location in the field, a hair pinning value can be predicted at the given location based on the predictive hair pinning model and the weed value, from the weed map432, at that given location.

Hair pinning-to-harvest coverage model generator6000identifies a relationship between hair pinning value(s) detected in in-situ sensor data2440, at geographic location(s) to which the hair pinning value(s), detected in the in-situ sensor data2440, correspond, and harvest coverage values from the harvest coverage map434corresponding to the same geographic location(s) to which the detected hair pinning value(s) correspond. Based on this relationship established by hair pinning-to-harvest coverage model generator6000, hair pinning-to-harvest coverage model generator6000generates a predictive hair pinning model. The predictive hair pinning model is used by hair pinning map generator2453to predict hair pinning at different locations in the field based upon the georeferenced harvest coverage values contained in the harvest coverage map434at the same locations in the field. Thus, for a given location in the field, a hair pinning value can be predicted at the given location based on the predictive hair pinning model and the harvest coverage value, from the harvest coverage map434, at that given location.

Hair pinning-to-other characteristic model generator2448identifies a relationship between hair pinning value(s) detected in in-situ sensor data2440, at geographic location(s) to which the hair pinning value(s), detected in the in-situ sensor data2440, correspond, and values of an other characteristic from an other map433corresponding to the same geographic location(s) to which the detected hair pinning value(s) correspond. Based on this relationship established by hair pinning-to-other characteristic model generator2448, hair pinning-to-other characteristic model generator2448generates a predictive hair pinning model. The predictive hair pinning model is used by hair pinning map generator2453to predict hair pinning at different locations in the field based upon the georeferenced values of the other characteristic contained in the other map433at the same locations in the field. Thus, for a given location in the field, a hair pinning value can be predicted at the given location based on the predictive hair pinning model and the value of the other characteristic, from the other map433, at that given location.

In light of the above, the predictive model generator310is operable to produce a plurality of predictive hair pinning models, such as one or more of the predictive hair pinning models generated by model generators2445,2446,2447,2448,6000and2449. In another example, two or more of the predictive models described above may be combined into a single predictive hair pinning model, such as a predictive hair pinning model that predicts hair pinning based upon two or more of the genotype values, the crop state value, the weed values, the harvest coverage values, and the other characteristic values at different locations in the field. Any of these hair pinning models, or combinations thereof, are represented collectively by predictive hair pinning model2451inFIG.7.

The predictive hair pinning model2451is provided to predictive map generator In the example ofFIG.7, predictive map generator312includes hair pinning map generator2453. In other examples, predictive map generator312may include additional or different map generators. Thus, in some examples, predictive map generator312may include other items2458which may include other types of map generators to generate other types of maps.

Hair pinning map generator2453receives one or more of the genotype map430, the crop state map431, the weed map432, the harvest coverage map434, and an other map433, along with the predictive hair pinning model2451which predicts hair pinning based upon one or more of a genotype value, a crop state value, a weed value, a harvest coverage value, and an other characteristic value, and generates a predictive map that predicts hair pinning at different locations in the field, such as functional predictive hair pinning map2480.

Predictive map generator312thus outputs a functional predictive hair pinning map that is predictive of hair pinning. Functional predictive hair pinning map2480is a predictive map264. The functional predictive hair pinning map2480, in one example, predicts hair pinning at different locations in a field. The functional predictive hair pinning map2480may be provided to control zone generator313, control system314, or both. Control zone generator313generates control zones and incorporates those control zones into the functional predictive hair pinning map to produce a predictive control zone map265, that is a functional predictive hair pinning control zone map2481.

One or both of functional predictive hair pinning map2480and functional predictive hair pinning control zone map2481may be provided to control system314, which generates control signals to control one or more of the controllable subsystems316based upon the functional predictive hair pinning map2480, the functional predictive hair pinning control zone map2481, or both.

FIGS.8A-8B(collectively referred to herein asFIG.8) show a flow diagram illustrating one example of the operation of agricultural harvesting system architecture300in generating a predictive model and a predictive map

At block602, agricultural system300receives one or more information maps358. Examples of information maps358or receiving information maps358are discussed with respect to blocks604,606,608, and609. As discussed above, information maps358map values of a variable, corresponding to a characteristic, to different locations in the field, as indicated at block606. As indicated at block604, receiving the information maps358may involve selecting one or more of a plurality of possible information maps358that are available. For instance, one information map358may be a genotype map, such as genotype map430. Another information map358may be a crop state map, such as crop state map431. Another information map358may be a weed map, such as weed map432. Another information map may be a harvest coverage map, such as harvest coverage map434. Information maps358may include various other types of maps that map various other characteristics, such as other maps433. The process by which one or more information maps358are selected can be manual, semi-automated, or automated. The information maps358can be based on data collected prior to a current operation. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current season, or at other times. The data may be based on data detected in ways other than using aerial images. For instance, the data may be collected during a previous operation on the worksite, such an operation during a previous year, or a previous operation earlier in the current season, or at other times. The machines performing those previous operations may be outfitted with one or more sensors that generate sensor data indicative of one or more characteristics. For example, the sensed operating parameters during a previous seeding operation or sensed characteristics during a spraying operation earlier in the year may be used as data to generate the information maps358. In other examples, and as described above, the information maps358may be predictive maps having predictive values. The predictive information map358can be generated by predictive map generator312based on a model generated by predictive model generator310. The data for the information maps358can be obtained by agricultural system300using communication system306and stored in data store302. The data for the information maps358can be obtained by agricultural system300using communication system306in other ways as well, and this is indicated by block609in the flow diagram ofFIG.8.

As agricultural harvester100is operating, in-situ sensors308generate sensor signals indicative of one or more in-situ data values indicative of a characteristic. For example, ear sensors380generate sensor data indicative of one or more in-situ data values indicative of one or more ear characteristics, as indicated by block611. For example, ear sensors380can include one or more of ear loss sensors382that sense one or more in-situ data values of ear loss as an ear characteristic and ear orientation sensors384that sense one or more in-situ data values of ear orientation as an ear characteristic. Control input sensors390generate sensor data indicative of one or more in-situ data values indicative of one or more control inputs, as indicated by block612. Wrapping sensors386generate sensor data indicative of one or more in-situ data values indicative of one or more wrapping values, as indicated by block613. Hair pinning sensors388generate sensor data indicative of one or more in-situ data values indicative of one or more hair pinning values, as indicated by block614.

In some examples, data from in-situ sensors308is georeferenced using position, heading, or speed data, as well as machine dimension information, sensor position information, etc.

In one example, at block615, predictive model generator310controls one or more of the model generators441,442,443,4000,444,445,446,447,448and449to generate a model that models the relationship between the mapped values, such as the genotype values, the crop state values, the weed values, the harvest coverage values, and the other characteristic values contained in the respective information map and the in-situ values of the one or more ear characteristics (e.g., one or more of ear loss and ear orientation) sensed by the in-situ sensors308. Predictive model generator310generates a predictive ear characteristic model450that predicts values of one or more ear characteristics based on one or more of genotype values, crop state values, weed values, harvest coverage values, and other characteristic values, as indicated by block616.

In one example, at block615, predictive model generator310controls one or more of the model generators1441,1442,1443,1444,1445, and1449to generate a model that models the relationship between the mapped values, such as the genotype values, the crop state values, the weed values, the harvest coverage values, and the other characteristic values contained in the respective information map and the in-situ control input values sensed by the in-situ sensors308. Predictive model generator310generates a predictive control input model1450that predicts control input values based on one or more of genotype values, crop state values, weed values, harvest coverage values, and other characteristic values, as indicated by block617.

In one example, at block615, predictive model generator310controls one or more of the model generators2441,2442,2443,5000,2444, and2449to generate a model that models the relationship between the mapped values, such as the genotype values, the crop state values, the weed values, the harvest coverage values, and the other characteristic values contained in the respective information map and the in-situ wrapping values sensed by the in-situ sensors308. Predictive model generator310generates a predictive wrapping model2450that predicts wrapping values based on one or more of genotype values, crop state values, weed values, harvest coverage values, and other characteristic values, as indicated by block618.

In one example, at block615, predictive model generator310controls one or more of the model generators2445,2446,2447,6000,2448, and2449to generate a model that models the relationship between the mapped values, such as the genotype values, the crop state values, the weed values, the harvest coverage values, and the other characteristic values contained in the respective information map and the in-situ hair pinning values sensed by the in-situ sensors308. Predictive model generator310generates a predictive hair pinning model2451that predicts hair pinning values based on one or more of genotype values, crop state values, weed values, harvest coverage values, and other characteristic values, as indicated by block619.

At block620, the relationship(s) or model(s) generated by predictive model generator310are provided to predictive map generator312. In one example, at block620, predictive map generator312generates a functional predictive ear characteristic map460that predicts values of one or more ear characteristics (or sensor values indicative of the one or more ear characteristics), such as one or more of ear loss and ear orientation, at different geographic locations in a field at which agricultural harvester100is operating using the predictive ear characteristic model450and one or more of the information maps358, such as genotype map430, crop state map431, weed map432, harvest coverage map434, and an other map433. Generating a predictive ear characteristic map, such as functional predictive ear characteristic map460, is indicated by block621.

It should be noted that, in some examples, the functional predictive ear characteristic map460may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive ear characteristic map460that provides two or more of a map layer that provides predictive ear loss or ear orientation, or both, based on genotype values from genotype map430, a map layer that provides predictive ear loss or ear orientation, or both, based on crop state values from crop state map431, a map layer that provides predictive ear loss or ear orientation, or both, based on weed values from weed map432, a map layer that provides predictive ear loss based on values from harvest coverage map434, and a map layer that provides predictive ear loss or ear orientation, or both, based on values from an other map433. Additionally, functional predictive ear characteristic map460can include a map layer that provides one or more of predictive ear loss and predictive ear orientation based on one or more of genotype values from genotype map430, crop state values from crop state map431, weed values from weed map432, harvest coverage values from harvest coverage map434, and values of an other characteristic from an other map433.

In one example, at block620, predictive map generator312generates a functional predictive control input map1460that predicts values of control input (or sensor values indicative of control input) at different geographic locations in a field at which agricultural harvester100is operating using the predictive control input model1450and one or more of the information maps358, such as genotype map430, crop state map431, weed map432, harvest coverage map434, and an other map433. Generating a predictive control input map, such as functional predictive control input map1460, is indicated by block622.

It should be noted that, in some examples, the functional predictive control input map1460may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive control input map1460that provides two or more of a map layer that provides predictive control input based on genotype values from genotype map430, a map layer that provides predictive control input based on crop state values from crop state map431, a map layer that provides predictive control input based on weed values from weed map432, a map layer that provides predictive control input based on harvest coverage values from harvest coverage map434, and a map layer that provides predictive control input based on values from an other map433. Additionally, functional predictive control input map1460can include a map layer that provides predictive control input based on one or more of genotype values from genotype map430, crop state values from crop state map431, weed values from weed map432, harvest coverage values from harvest coverage map434, and values of an other characteristic from an other map433.

In one example, at block620, predictive map generator312generates a functional predictive wrapping map2470that predicts wrapping values (or sensor values indicative of wrapping) at different geographic locations in a field at which agricultural harvester100is operating using the predictive wrapping model2450and one or more of the information maps358, such as genotype map430, crop state map431, weed map432, harvest coverage map434, and an other map433. Generating a predictive wrapping map, such as functional predictive wrapping map2470, is indicated by block623.

It should be noted that, in some examples, the functional predictive wrapping map2470may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive wrapping map2470that provides two or more of a map layer that provides predictive wrapping based on genotype values from genotype map430, a map layer that provides predictive wrapping based on crop state values from crop state map431, a map layer that provides predictive wrapping based on weed values from weed map432, a map layer that provides predictive wrapping based on harvest coverage values from harvest coverage map434, and a map layer that provides predictive wrapping based on values from an other map433. Additionally, functional predictive wrapping map2470can include a map layer that provides predictive wrapping based on one or more of genotype values from genotype map430, crop state values from crop state map431, weed values from weed map432, harvest coverage values from harvest coverage map434, and values of an other characteristic from an other map433.

In one example, at block620, predictive map generator312generates a functional predictive hair pinning map2480that predicts hair pinning values (or sensor values indicative of hair pinning) at different geographic locations in a field at which agricultural harvester100is operating using the predictive hair pinning model2451and one or more of the information maps358, such as genotype map430, crop state map431, weed map432, harvest coverage map434, and an other map433. Generating a predictive hair pinning map, such as functional predictive hair pinning map2480, is indicated by block624.

It should be noted that, in some examples, the functional predictive hair pinning map2480may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive hair pinning map2480that provides two or more of a map layer that provides predictive hair pinning based on genotype values from genotype map430, a map layer that provides predictive hair pinning based on crop state values from crop state map431, a map layer that provides predictive hair pinning based on weed values from weed map432, a map layer that provides predictive hair pinning based on harvest coverage values from harvest coverage map434, and a map layer that provides predictive hair pinning based on values from an other map433. Additionally, functional predictive hair pinning map2480can include a map layer that provides predictive hair pinning based on one or more of genotype values from genotype map430, crop state values from crop state map431, weed values from weed map432, harvest coverage values from harvest coverage map434, and values of an other characteristic from an other map433.

At block625, predictive map generator312configures one or more of the functional predictive ear characteristic map460, the functional predictive control input map1460, the functional predictive wrapping map2470, and the functional predictive hair pinning map2480so that the one or more maps460,1460,2470, and2480are actionable (or consumable) by control system314. Predictive map generator312can provide one or more of the functional predictive ear characteristic map460, the functional predictive control input map1460, the functional predictive wrapping map2470, and the functional predictive hair pinning map2480to the control system314or to control zone generator313, or both. Some examples of the different ways in which the one or more maps460,1460,2470, and2480can be configured or output are described with respect to blocks625,626,627, and628. For instance, predictive map generator312configures one or more of the functional predictive ear characteristic map460, the functional predictive control input map1460, the functional predictive wrapping map2470, and the functional predictive hair pinning map2480so that the one or more maps460,1460,2470, and2480include values that can be read by control system314and used as the basis for generating control signals for one or more of the different controllable subsystems316of mobile machine100, as indicated by block625.

In one example, at block626, control zone generator313can divide the functional predictive ear characteristic map460into control zones based on the values on the functional predictive ear characteristic map460to generate functional predictive ear characteristic control zone map461. In one example, at block626, control zone generator313can divide the functional predictive control input map1460into control zones based on the values on the functional predictive control input map1460to generate functional predictive control input control zone map1461. In one example, at block626, control zone generator313can divide the functional predictive wrapping map2470into control zones based on the values on the functional wrapping map2470to generate functional predictive wrapping control zone map2471. In one example, at block626, control zone generator313can divide the functional predictive hair pinning map2480into control zones based on the values on the functional predictive hair pinning map2480to generate functional predictive hair pinning control zone map2481. Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system314, the controllable subsystems316, based on wear considerations, or on other criteria.

At block627, predictive map generator312configures one or more of the functional predictive ear characteristic map460, the functional predictive control input map1460, the functional predictive wrapping map2470, and the functional predictive hair pinning map2480for presentation to an operator or other user. At block622, control zone generator313can configure one or more of the functional predictive ear characteristic control zone map461, the functional predictive control input control zone map1461, the functional predictive wrapping control zone map2471, and the functional predictive hair pinning control zone map2481for presentation to an operator or other user. When presented to an operator or other user, the presentation of the one or more maps460,1460,2470, and2480or of the one or more maps461,1461,2471, and2481, or both, may contain one or more of the predictive values on the one or more functional predictive maps460,1460,2470, and2480correlated to geographic location, the control zones of the one or more functional control zone maps461,1461,2471, and2481correlated to geographic location, and settings values or control parameters that are used based on the predicted values on the one or more functional predictive maps460,1460,2470, and2480or control zones on the one or more functional predictive control zone maps461,1461,2471, and2481. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on the one or more maps460,1460,2470, and2480or the control zones the one or more maps461,1461,2471, and2481conform to measured values that may be measured by sensors on agricultural harvester100as agricultural harvester100operates at the field. Further where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, 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 a user permission level. The user permission level may be used to determine which display elements are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of mobile machine100may be unable to see the information corresponding to the one or more functional predictive maps460,1460,2470, and2480or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the one or more functional predictive maps460,1460,2470, and2480on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on the one or more functional predictive maps460,1460,2470, and2480and also be able to change the one or more functional predictive maps460,1460,2470, and2480. In some instances, the one or more functional predictive maps460,1460,2470, and2480accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. The one or more functional predictive maps460,1460,2470, and2480or the one or more functional predictive control zone maps461,1461,2471, and2481, or both, can be configured in other ways as well, as indicated by block628.

At block629, input from geographic position sensors304and other in-situ sensors are received by the control system314. Particularly, at block630, control system314detects an input from the geographic position sensors304identifying a geographic location of agricultural harvester100. Block631represents receipt by the control system314of sensor inputs indicative of trajectory or heading of agricultural harvester100, and block632represents receipt by the control system314of a speed of agricultural harvester100. Block633represents receipt by the control system314of other information from various in-situ sensors308.

At block634, control system314generates control signals to control the controllable subsystems316based on one or more of the functional predictive maps460,1460,2470, and2480or one or more of the functional predictive control zone maps461,1461,2471, and2481, or both, and the input from the geographic position sensor304and any other in-situ sensors308. At block635, control system314applies the control signals to the controllable subsystems316. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems316that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems316that are controlled may be based on the type of the one or more functional predictive maps460,1460,2470, and2480or the one or more functional predictive control zone maps461,1461,2471, and2481, or both, that are being used. Similarly, the control signals that are generated and the controllable subsystems316that are controlled and the timing of the control signals can be based on various latencies of agricultural harvester100and the responsiveness of the controllable subsystems316.

By way of example, propulsion controller331of control system314can generate control signals to control propulsion subsystem350to control one or more propulsion parameters of agricultural harvester100, such as one or more of the speed at which the agricultural harvester100travels, the deceleration of agricultural harvester100, and the acceleration of agricultural harvester100, based on one or more of the functional predictive maps460,1460,2470, and2480or one or more of the functional predictive control zone maps461,1461,2471, and2481or both.

In another example, path planning controller334of control system314can generate control signals to control steering subsystem352to control a route parameter of agricultural harvester100, such as one or more of a commanded path at the field over which agricultural harvester100travels, and the steering of agricultural harvester100, based on one or more of the functional predictive maps460,1460,2470, and2480or one or more of the functional predictive control zone maps461,1461,2471, and2481or both,

In another example, one or more end divider controllers335of control system314can generate control signals to control end divider subsystem340to control one or more end dividers (e.g.,146and150or147), based on one or more functional predictive maps460,1460,2470, and2480or the one or more functional predictive control zone maps461,1461,2471, and or both. For example, an end divider controller335can generate control signals to control one or more end divider actuators342to control movement of one or more end dividers, for instance to extend or retract one or more of end dividers146and150, to control the speed of rotation of one or more end dividers147, or to control the direction of rotation of one or more end dividers147, based on one or more of the functional predictive maps460,1460,2470, and2480or the one or more of the functional predictive control zone maps461,1461,2471, and2481, or both.

In another example, interface controller330of control system314can generate control signals to control an interface mechanism (e.g.,218or364) to generate a display, alert, notification, or other indication based on or indicative of the one or more functional predictive maps460,1460,2470, and2480or the one or more functional predictive control zone maps461,1461,2471, and2481, or both.

In another example, communication system controller329of control system314can generate control signals to control communication system306to communicate one or more of the functional predictive maps460,1460,2470, and2480or one or more of the functional predictive control zone maps461,1461,2471, and2481, or both, to another item of agricultural system300(e.g., remote computing systems368or user interfaces364).

These are merely examples. Control system314can generate various other control signals to control various other items of mobile machine100(or agricultural system300) based on one or more of the functional predictive maps460,1460,2470, and2480or one or more of the functional predictive control zone maps461,1461,2471, and2481, or both,

At block636, a determination is made as to whether the operation has been completed. If the operation is not completed, the processing advances to block638where in-situ sensor data from geographic position sensor304and in-situ sensors308(and perhaps other sensors) continue to be read.

In some examples, at block640, agricultural system300can also detect learning trigger criteria to perform machine learning on one or more of the functional predictive maps460,1460,2470, and2480, one or more of the functional predictive control zone maps461,1461,2471, and2481, one or more of the predictive models450,1450,2450, and2451the zones generated by control zone generator313, one or more control algorithms implemented by the controllers in the control system314, and other triggered learning.

The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks642,644,646,648, and649. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors308. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors308that exceeds a threshold triggers or causes the predictive model generator310to generate a new predictive model that is used by predictive map generator312. Thus, as agricultural harvester100continues an operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors308triggers the creation of a new relationship represented by a new predictive model (e.g., one or more of new model450, new model1450, new model2450, and new model2451) generated by predictive model generator310. Further, one or more new functional predictive maps460,1460,2470, and2480or one or more new functional predictive control zone maps461,1461,2471, and2481, or both, can be generated using the respective new models450,1450,2450, and2451. Block642represents detecting a threshold amount 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 sensors308are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in the one or more information maps358) are within a selected range or is less than a defined amount, or below a threshold value, then a new predictive model is not generated by the predictive model generator310. As a result, the predictive map generator312does not generate a new functional predictive map, a new functional predictive control zone map, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator310generates a new model using all or a portion of the newly received in-situ sensor data that the predictive map generator312uses to generate a new functional predictive map which can be provided to control zone generator313for the creation of a new functional predictive control zone map. At block644, 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 of the relationship between the in-situ sensor data and the information in the one or more information maps, can be used as a trigger to cause generation of one or more of a new predictive model, a new functional predictive map, and a new functional predictive control zone map. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways.

Other learning trigger criteria can also be used. For instance, if predictive model generator310switches to a different information map (different from the originally selected information map), then switching to the different information map may trigger re-learning by predictive model generator310, predictive map generator312, control zone generator313, control system314, or other items. In another example, transitioning of agricultural harvester100to a different topography or to a different control zone may be used as learning trigger criteria as well.

In some instances, operator360or user366can also edit the one or more functional predictive maps460,1460,2470, and2480or the one or more functional predictive control zone maps461,1461,2471, and2481or both. The edits can change value(s) on the one or more functional predictive maps460,1460,2470, and2480, change a size, shape, position, or existence of control zone(s) on the one or more functional predictive control zone maps461,1461,2471, and2481, or both. Block646shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator360or user366observes that automated control of a controllable subsystem316, is not what the operator or user desires. In such instances, the operator360or user366may provide a manual adjustment to the controllable subsystem316reflecting that the operator360or user366desires the controllable subsystem316to operate in a different way than is being commanded by control system314. Thus, manual alteration of a setting by the operator360or user366can cause one or more of predictive model generator310to generate a new model, predictive map generator312to generate a new functional predictive map, control zone generator313to generate one or more new control zones on a functional predictive control zone map, and control system314to relearn a control algorithm or to perform machine learning on one or more of the controller components329through339in control system314based upon the adjustment by the operator360or user366, as shown in block648. Block649represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated by block650.

If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block650, then one or more of the predictive model generator310, predictive map generator312, control zone generator313, and control system314performs machine learning to generate one or more new predictive models, one or more new predictive maps, one or more new control zones, and one or more new control algorithms, respectively, based upon the learning trigger criteria. The new predictive model(s), the new predictive map(s), the new control zone(s), and the new control algorithm(s) are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block652.

If the operation has been completed, operation moves from block652to block654where one or more of the functional predictive map(s), functional predictive control zone map(s), the predictive model(s), the control zone(s), and the control algorithm(s), are stored. The functional predictive map(s), functional predictive control zone map(s), predictive model(s), control zone(s), and control algorithm(s), may be stored locally on data store302or sent to a remote system using communication system306for later use.

If the operation has not been completed, operation moves from block652to block such that the one or more of the new predictive model(s), the new functional predictive map(s), the new functional predictive control zone map(s), the new control zone(s), and the new control algorithm(s) can be used in the control of agricultural harvester100.

The examples herein describe the generation of a predictive model and, in some examples, the generation of a functional predictive map based on the predictive model. The examples described herein are distinguished from other approaches by the use of a model which is at least one of multi-variate or site-specific (i.e., georeferenced, such as map-based). Furthermore, the model is revised as the work machine is performing an operation and while additional in-situ sensor data is collected. The model may also be applied in the future beyond the current worksite. For example, the model may form a baseline (e.g., starting point) for a subsequent operation at a different worksite or the same worksite at a future time.

The revision of the model in response to new data may employ machine learning methods. Without limitation, machine learning methods may include memory networks, Bayes systems, decisions trees, Eigenvectors, Eigenvalues and Machine Learning, Evolutionary and Genetic Algorithms, Cluster Analysis, Expert Systems/Rules, Support Vector Machines, Engines/Symbolic Reasoning, Generative Adversarial Networks (GANs), Graph Analytics and ML, Linear Regression, Logistic Regression, LSTMs and Recurrent Neural Networks (RNNSs), Convolutional Neural Networks (CNNs), MCMC, Random Forests, Reinforcement Learning or Reward-based machine learning. Learning may be supervised or unsupervised.

Model implementations may be mathematical, making use of mathematical equations, empirical correlations, statistics, tables, matrices, and the like. Other model implementations may rely more on symbols, knowledge bases, and logic such as rule-based systems. Some implementations are hybrid, utilizing both mathematics and logic. Some models may incorporate random, non-deterministic, or unpredictable elements. Some model implementations may make uses of networks of data values such as neural networks. These are just some examples of models.

The predictive paradigm examples described herein differ from non-predictive approaches where an actuator or other machine parameter is fixed at the time the machine, system, or component is designed, set once before the machine enters the worksite, is reactively adjusted manually based on operator perception, or is reactively adjusted based on a sensor value.

The functional predictive map examples described herein also differ from other map-based approaches. In some examples of these other approaches, an a priori control map is used without any modification based on in-situ sensor data or else a difference determined between data from an in-situ sensor and a predictive map are used to calibrate the in-situ sensor. In some examples of the other approaches, sensor data may be mathematically combined with a priori data to generate control signals, but in a location-agnostic way; that is, an adjustment to an a priori, georeferenced predictive setting is applied independent of the location of the work machine at the worksite. The continued use or end of use of the adjustment, in the other approaches, is not dependent on the work machine being in a particular defined location or region within the worksite.

In examples described herein, the functional predictive maps and predictive actuator control rely on obtained maps and in-situ data that are used to generate predictive models. The predictive models are then revised during the operation to generate revised functional predictive maps and revised actuator control. In some examples, the actuator control is provided based on functional predictive control zone maps which are also revised during the operation at the worksite. In some examples, the revisions (e.g., adjustments, calibrations, etc.) are tied to regions or zones of the worksite rather than to the whole worksite or some non-georeferenced condition. For example, the adjustments are applied to one or more areas of a worksite to which an adjustment is determined to be relevant (e.g., such as by satisfying one or more conditions which may result in application of an adjustment to one or more locations while not applying the adjustment to one or more other locations), as opposed to applying a change in a blanket way to every location in a non-selective way.

In some examples described herein, the models determine and apply those adjustments to selective portions or zones of the worksite based on a set of a priori data, which, in some instances, is multivariate in nature. For example, adjustments may, without limitation, be tied to defined portions of the worksite based on site-specific factors such as topography, soil type, crop genotype, soil moisture, as well as various other factors, alone or in combination. Consequently, the adjustments are applied to the portions of the field in which the site-specific factors satisfy one or more criteria and not to other portions of the field where those site-specific factors do not satisfy the one or more criteria. Thus, in some examples described herein, the model generates a revised functional predictive map for at least the current location or zone, the unworked part of the worksite, or the whole worksite.

As an example, in which the adjustment is applied only to certain areas of the field, consider the following. The system may determine that a detected in-situ characteristic value varies from a predictive value of the characteristic, such as by a threshold amount. This deviation may only be detected in areas of the field where the elevation of the worksite is above a certain level. Thus, the revision to the predictive value is only applied to other areas of the worksite having elevation above the certain level. In this simpler example, the predictive characteristic value and elevation at the point the deviation occurred and the detected characteristic value and elevation at the point the deviation cross the threshold are used to generate a linear equation. The linear equation is used to adjust the predictive characteristic value in areas of the worksite (which have not yet been operated on in the current operation, such as unharvested areas) in the functional predictive map as a function of elevation and the predicted characteristic value. This results in a revised functional predictive map in which some values are adjusted while others remain unchanged based on selected criteria, e.g., elevation as well as threshold deviation. The revised functional map is then used to generate a revised functional control zone map for controlling the machine.

As an example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of a genotype map, a crop state map, a weed map, and another type of map.

In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ values of one or more ear characteristics (e.g., one or more of ear loss and ear orientation).

A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive ear characteristic model.

A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive ear characteristic map that maps predictive values of one or more ear characteristics to one or more locations on the worksite based on a predictive ear characteristic model and the one or more obtained maps.

Control zones, which include machine settings values, can be incorporated into the functional predictive ear characteristic map to generate a functional predictive ear characteristic map with control zones.

As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of a genotype map, a crop state map, a weed map, and another type of map.

In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ control input values.

A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive control input model.

A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive control input map that maps predictive control input values to one or more locations on the worksite based on a predictive control input model and the one or more obtained maps.

Control zones, which include machine settings values, can be incorporated into the functional predictive control input map to generate a functional predictive control input map with control zones.

As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of a genotype map, a crop state map, a weed map, and another type of map.

In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ wrapping values.

A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive wrapping model.

A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive wrapping map that maps predictive wrapping values to one or more locations on the worksite based on a predictive wrapping model and the one or more obtained maps.

Control zones, which include machine settings values, can be incorporated into the functional predictive wrapping map to generate a functional predictive wrapping map with control zones.

As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of a genotype map, a crop state map, a weed map, and another type of map.

In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ hair pinning values.

A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive hair pinning model.

A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive hair pinning map that maps predictive hair pinning values to one or more locations on the worksite based on a predictive hair pinning model and the one or more obtained maps.

Control zones, which include machine settings values, can be incorporated into the functional predictive hair pinning map to generate a functional predictive hair pinning map with control zones.

As the mobile machine continues to operate at the worksite, additional in-situ sensor data is collected. A learning trigger criteria can be detected, such as threshold amount of additional in-situ sensor data being collected, a magnitude of change in a relationship (e.g., the in-situ characteristic values varies to a certain [e.g., threshold] degree from a predictive value of the characteristic), and operator or user makes edits to the predictive map(s) or to a control algorithm, or both, a certain (e.g., threshold) amount of time elapses, as well as various other learning trigger criteria. The predictive model(s) are then revised based on the additional in-situ sensor data and the values from the obtained map(s). The functional predictive map(s) or the functional predictive control zone map(s), or both, are then revised based on the revised model(s) and the values in the obtained map(s).

FIG.9is shows a flow diagram illustrating one example of the operation of controlling agricultural harvester100.

At block702, agricultural system300receives one or more information maps358. Examples of information maps358or receiving information maps358are discussed with respect to blocks704,706,708, and709. As discussed above, information maps358map values of a variable, corresponding to a characteristic, to different locations in the field, as indicated at block706. As indicated at block704, receiving the information maps358may involve selecting one or more of a plurality of possible information maps358that are available. For instance, one information map358may be a genotype map, such as genotype map430. Another information map358may be a crop state map, such as crop state map431. Another information map358may be a weed map, such as weed map432. Another information map may be a harvest coverage map, such as harvest coverage map434. Information maps358may include various other types of maps that map various other characteristics, such as other maps439. The process by which one or more information maps358are selected can be manual, semi-automated, or automated. The information maps358can be based on data collected prior to a current operation. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current season, or at other times. The data may be based on data detected in ways other than using aerial images. For instance, the data may be collected during a previous operation on the worksite, such an operation during a previous year, or a previous operation earlier in the current season, or at other times. The machines performing those previous operations may be outfitted with one or more sensors that generate sensor data indicative of one or more characteristics. For example, the sensed operating parameters during a previous seeding operation or sensed characteristics during a spraying operation earlier in the year may be used as data to generate the information maps358. In some examples, the sensed operating parameters during a previous harvesting operation in the same year may be used as data to generate the information map, for instance, sensed parameters of a harvester that partially harvested the field earlier in the year may be used as the basis for generation of a harvest coverage map. In other examples, the information maps may be generated during the current operation, for instance, obtaining sensor data, tracking the geographic location(s) and the operation(s) of one or more agricultural harvesters as the one or more agricultural harvesters move across the field can be used as the basis for the generation of the harvest coverage map, which can be continuously updated throughout the current operation. In other examples, and as described above, the information maps358may be predictive maps having predictive values. The predictive information map358can be generated by predictive map generator312based on a model generated by predictive model generator310. The data for the information maps358can be obtained by agricultural system300using communication system306and stored in data store302. The data for the information maps358can be obtained by agricultural system300using communication system306in other ways as well, and this is indicated by block709in the flow diagram ofFIG.9.

At block710, input from geographic position sensors304and other in-situ sensors308are received by control system314. Particularly, at block712, control system314detects an input from geographic position sensors304identifying a geographic location of agricultural harvester100. Block714represents receipt by the control system314of sensor inputs indicative of trajectory or heading of agricultural harvester100, and block716represents receipt by the control system314of a speed of agricultural harvester100. Block718represents receipt by the control system314of other information from various in-situ sensors308.

At block720, control system314generates control signals to control the controllable subsystems based on one or more of the information maps358, such as one or more of the genotype map430, the crop state map431, the weed map432, the harvest coverage map434, and an other map433, and the input from the geographic position sensors304, and any other input, such as, but not limited to, the speed and heading/trajectory of agricultural harvester100. At block722, control system314applies the control signals to the controllable subsystems316, such as end dividers subsystem340, as indicated by block724, or any of wide variety of other controllable subsystems316, as indicated by block726. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems316that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems316that are controlled may be based on the type of the one or more information maps358that are being used. Similarly, the control signals that are generated and the controllable subsystems316that are controlled and the timing of the control signals can be based on various latencies of agricultural harvester100and the responsiveness of the controllable subsystems316.

By way of example, end divider controllers335of control system314can generate control signals to control the operation of one or more end dividers of agricultural harvester100.

For example, the information map358may be a harvest coverage map, such as harvest coverage map434. The harvest coverage map may indicate that a row adjacent to the head of agricultural harvester100has been harvested. In such a case, an end divider controller335can generate a control signal to control an end divider actuator342to extend or raise the end divider (146or150) on the side of head144adjacent to the harvested row. In another example, an end divider controller335can generate a control signal to control an end divider actuator342to initiate or speed up the rotation of the end divider147on the side of head144adjacent to the harvested row. Where the harvest coverage map indicates that the rows adjacent to each side of head144are harvested, the end divider on each side can be controlled in similar fashion.

In another example, the harvest coverage map may indicate that a row adjacent to the head144of agricultural harvester100is unharvested. In such a case, an end divider controller335can generate a control signal to control an end divider actuator to retract or lower the end divider (146or150) on the side of head144adjacent to the unharvested row. In another example, an end divider controller335can generate a control signal to control an end divider actuator342to stop or slow down rotation of the end divider147on the side of head144adjacent to the unharvested row. Where the harvest coverage map indicates that the rows adjacent to each side of head144are unharvested, the end divider on each side can be controlling similar fashion.

Further, where the adjacent row next to one side of head144is unharvested and the adjacent row next to the opposite side of head144is harvested, the respective end divider on each side can be controlled differently and separately, as detailed above.

In another example, the information map358may be a crop state map. The crop state map may indicate that the plants (e.g., crops and/or weeds) ahead of, adjacent to, or in the area of head144are down or partially down. In such a case, end divider controller(s)335can generate control signal(s) to control end divider actuator(s)342to retract or lower end divider(s) (e.g.,146or150, or both). Where the crop state map indicates that the plants ahead of, adjacent to, or in the area of head144are down or partially down only partially across the width of head144, it may be that the end divider closer to the down or partially down plants is retracted or lowered. In another example, end divider controller(s)335can generate control signal(s) to control end divider actuator(s)342to start or speed up rotation of end divider(s)147where the crop state maps indicate that the plants ahead of, adjacent to, or in the area of head144are down or partially down. Where the crop state map indicates that the plants ahead of or in the area of head144are down or partially down only partially across the width of head144, it may be that only the end divider147closer to the down or partially down plants is controlled to start or speed up rotation.

In another example, the information map358may be a weed map. The weed map may indicate the intensity or type of weeds ahead of, adjacent to, or in the area of head144. For example, where the weeds are more intense (relative to a threshold level), end divider controller(s)335can generate control signal(s) to retract or lower end divider(s) (e.g.,146or150, or both), such as to prevent hair pinning. In another example, where the weeds are less intense (relative to a threshold level), end divider end divider controller(s)335can generate control signal(s) to extend or raise end divider(s) (e.g.,146or150, or both) as the risk of hair pinning may be reduced. Where the weed map indicates that the weeds ahead of or in the area of head144are more intense (relative to a threshold level) only partially across the width of head144, it may be that only the end divider146or150closer to the more intense weeds is controlled to retract or lower. Similarly, where the weed map indicates that the weeds ahead of or in the area of head144are less intense (relative to a threshold level) only partially across the width of head144, it may be that only the end divider146or150closer to the less intense weeds is controlled to extend or raise.

In another example, where the weed map indicates that the weed type of the weeds ahead of, adjacent to, or in the area of head144are or include vine type weeds (or another type of weed more likely to wrap), end divider controller(s)335can generate control signal(s) to stop or slow rotation of end divider(s)147, such as to prevent wrapping. In another example, where the weeds are non-vine type (or another type of weed less likely to wrap) end divider end divider controller(s)335can generate control signal(s) to start or speed up rotation of end divider(s)147, as the risk of wrapping may be reduced. Where the weed map indicates that the type of weeds ahead of or in the area of head144are more likely to wrap but only partially across the width of head144, it may be that only the rotation of end divider147closer to the more intense weeds is stopped or slowed.

In another example, the information map358may be a genotype map. The genotype map may indicate that the crop ahead of, adjacent to, or in the area of head144is of a certain genotype. Each genotype of crop may have different characteristics such that the control of agricultural harvester for each genotype may be different. For instance, one genotype may be more likely to have non-erect or drooping ears, and thus, end dividers146or150may be retracted or lowered to prevent knocking off the ears. One genotype may be more likely to have erect ears, and thus, end dividers146or150may be extended or raised due to the reduced risk of knocking off ears. In another example, ear loss from bouncing in the head144may be less likely for a given genotype, and thus, end dividers146or150may be retracted or lowered to prevent hair pinning without risking increased ear loss or rotation of end dividers147may be stopped or slowed to prevent wrapping without risking increased ear loss. Ear loss from bouncing in the head144may be more likely for a given genotype, and thus, end dividers146or150may be extended or raised or rotation of end dividers147may be initiated or sped up. These are merely some examples.

In some examples, values from more than one information map358may be used by control system314in controlling the controllable subsystems316. For example, the harvest coverage map may indicate that the row adjacent to a side of head144is unharvested (such that the end divider on that side should be raised), however the crop state map may indicate that the plants ahead of or in the area of head144are down (such that the end divider should be retracted or lowered). Where there are conflicting controls, control system314may prioritize one over the other. The prioritization can be set by an operator or user, based on historical performance, stored in a data store, manufacturer recommended, as well as a wide variety of other sources. In another example, the harvest coverage map may indicate that the row adjacent to the side of head144is unharvested (such that the end divider on that side should be lowered) but the genotype map indicates that the crop genotype in the area adjacent to that side of head is more likely to have erect (as opposed to drooping) ears, thus control system314may only partially lower the end divider rather than fully retract the end divider. These are merely some examples.

Additionally, in some examples, control system314may perform machine learning. For instance, it may be that an operator or user provides a control input (via an interface mechanism) to control a controllable subsystem, such as end dividers subsystem340, in an area of the field with a given characteristic as indicated by an information map358. Control system314may then implement that same control in other areas of the field with that given characteristic. The operator or user may override or adjust that control, which can be used as learning trigger by the control system to adjust the control algorithm. For instance, the operator or user may provide an input to lower or retract end divider(s) in areas of the field where the crop state map indicates that the plants are down. Control system314may then automatically control the end dividers to retract or lower in other areas of the field where the crop state map indicates that the plants are down. This is merely an example.

It will be understood that the thresholds described above may be provided by an operator or user, already stored in a data store, manufacture recommended, based on historical performance, as well as from a wide variety of other sources.

At block728it is determined if the harvesting operation is complete. If the harvesting operation is not completed, the operation proceeds to block730where it is determined if new or updated information maps358have been generated. If new or updated maps have been generated, operation proceeds to block702where the new or updated maps are obtained. If no new or updated maps have been generated, operation proceeds to block710.

If, at block728, it is determined that the harvesting operation is completed then processing ends.

The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by and facilitate the functionality of the other components or items in those systems.

Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, they can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech.

A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing 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.

Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed.

It will be noted that the above discussion has described a variety of different systems, components, logic and interactions. It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components, some of which are described below, that perform the functions associated with those systems, components, or logic, or interactions. In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently 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 by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic and interactions described above. Other structures may be used as well.

FIG.10is a block diagram of agricultural harvester1000, which may be similar to agricultural harvester100shown inFIG.4. The agricultural harvester1000communicates with elements in a remote server architecture1002. In some examples, remote server architecture1002provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various examples, remote servers may deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers may deliver applications over a wide area network and may be accessible through a web browser or any other computing component. Software or components shown inFIG.4as well as data associated therewith, may be stored on servers at a remote location. The computing resources in a remote server environment may be consolidated at a remote data center location, or the computing resources may be dispersed to a plurality of remote data centers. Remote server infrastructures may deliver services through shared data centers, even though the services appear as a single point of access for the user. Thus, the components and functions described herein may be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functions may be provided from a server, or the components and functions can be installed on client devices directly, or in other ways.

In the example shown inFIG.10, some items are similar to those shown inFIG.4and those items are similarly numbered.FIG.10specifically shows that predictive model generator310or predictive map generator312, or both, may be located at a server location1004that is remote from the agricultural harvester1000. Therefore, in the example shown inFIG.10, agricultural harvester1000accesses systems through remote server location1004. In other examples, various other items may also be located at server location1004, such as data store302, map selector309, predictive model311, functional predictive maps263(including predictive maps264and predictive control zone maps265), control zone generator313, control system314, and processing system338.

FIG.10also depicts another example of a remote server architecture.FIG.10shows that some elements ofFIG.4may be disposed at a remote server location1104while others may be located elsewhere. By way of example, data store302may be disposed at a location separate from location1004and accessed via the remote server at location1004. Regardless of where the elements are located, the elements can be accessed directly by agricultural harvester1000through a network such as a wide area network or a local area network; the elements can be hosted at a remote site by a service; or the elements can be provided as a service or accessed by a connection service that resides in a remote location. Also, data may be stored in any location, and the stored data may be accessed by, or forwarded to, operators, users or systems. For instance, physical carriers may be used instead of, or in addition to, electromagnetic wave carriers. In some examples, where wireless telecommunication service coverage is poor or nonexistent, another machine, such as a fuel truck or other mobile machine or vehicle, may have an automated, semi-automated or manual information collection system. As the agricultural harvester1000comes close to the machine containing the information collection system, such as a fuel truck prior to fueling, the information collection system collects the information from the agricultural harvester1000using any type of ad-hoc wireless connection. The collected information may then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage is available. For instance, a fuel truck may enter an area having wireless communication coverage when traveling to a location to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information may be stored on the agricultural harvester1000until the agricultural harvester1000enters an area having wireless communication coverage. The agricultural harvester1000, itself, may send the information to another network.

It will also be noted that the elements ofFIG.4, or portions thereof, may be disposed on a wide variety of different devices. One or more of those devices may include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palm top computer, a cell phone, a smart phone, a multimedia player, a personal digital assistant, etc.

In some examples, remote server architecture1002may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain).

FIG.11is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user's or client's hand held device16, in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment of agricultural harvester100for use in generating, processing, or displaying the maps discussed above.FIGS.12-13are examples of handheld or mobile devices.

FIG.11provides a general block diagram of the components of a client device16that can run some components shown inFIG.4, that interacts with them, or both. In the device16, a communications link13is provided that allows the handheld device to communicate with other computing devices and under some examples provides a channel for receiving information automatically, such as by scanning. Examples of communications link13include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.

In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface15. Interface15and communication links13communicate with a processor17(which can also embody processors or servers from other FIGS.) along a bus19that is also connected to memory21and input/output (I/O) components23, as well as clock25and location system27.

I/O components23, in one example, are provided to facilitate input and output operations. I/O components23for various examples of the device16can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components23can be used as well.

Memory21stores operating system29, network settings31, applications33, application configuration settings35, data store37, communication drivers39, and communication configuration settings41. Memory21can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory21may also include computer storage media (described below). Memory21stores computer readable instructions that, when executed by processor17, cause the processor to perform computer-implemented steps or functions according to the instructions. Processor17may be activated by other components to facilitate their functionality as well.

FIG.12shows one example in which device16is a tablet computer1200. InFIG.12, computer1200is shown with user interface display screen1202. Screen1202can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. Tablet computer1200may also use an on-screen virtual keyboard. Of course, computer1200might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer1200may also illustratively receive voice inputs as well.

Note that other forms of the devices16are possible.

FIG.14is one example of a computing environment in which elements ofFIG.4can be deployed. With reference toFIG.14, an example system for implementing some embodiments includes a computing device in the form of a computer910programmed to operate as discussed above. Components of computer910may include, but are not limited to, a processing unit920(which can comprise processors or servers from previous FIGS.), a system memory930, and a system bus921that couples various system components including the system memory to the processing unit920. The system bus921may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect toFIG.4can be deployed in corresponding portions ofFIG.14.

The system memory930includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM)931and random access memory (RAM)932. A basic input/output system933(BIOS), containing the basic routines that help to transfer information between elements within computer910, such as during start-up, is typically stored in ROM931. RAM932typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit920. By way of example, and not limitation,FIG.14illustrates operating system934, application programs935, other program modules936, and program data937.

The computer910may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,FIG.14illustrates a hard disk drive941that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive955, and nonvolatile optical disk956. The hard disk drive941is typically connected to the system bus921through a non-removable memory interface such as interface940, and optical disk drive955are typically connected to the system bus921by a removable memory interface, such as interface950.

The drives and their associated computer storage media discussed above and illustrated inFIG.14, provide storage of computer readable instructions, data structures, program modules and other data for the computer910. InFIG.14, for example, hard disk drive941is illustrated as storing operating system944, application programs945, other program modules946, and program data947. Note that these components can either be the same as or different from operating system934, application programs935, other program modules936, and program data937.

A user may enter commands and information into the computer910through input devices such as a keyboard962, a microphone963, and a pointing device961, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit920through a user input interface960that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display991or other type of display device is also connected to the system bus921via an interface, such as a video interface990. In addition to the monitor, computers may also include other peripheral output devices such as speakers997and printer996, which may be connected through an output peripheral interface995.

The computer910is operated in a networked environment using logical connections (such as a controller area network—CAN, local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer980.

When used in a LAN networking environment, the computer910is connected to the LAN971through a network interface or adapter970. When used in a WAN networking environment, the computer910typically includes a modem972or other means for establishing communications over the WAN973, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.FIG.14illustrates, for example, that remote application programs985can reside on remote computer980.