Abstract: An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.

Abstract: In accordance with an example embodiment, a method for directing a work machine to one or more worksites from a selection of candidate worksites is disclosed. The method includes receiving obscurant data related to a forecast availability of obscurants at one or more worksites; receiving environmental data related to the suppression, creation, transportation, or direction of obscurants; and receiving operational data related to machine components and the ability of the machine components to generate obscurants or have performance degraded by obscurants at the one or more worksites. Determining an obscurant metric for each of the one or more worksites based on the obscurant data, the environmental data, and the operational data; and directing the work machine to the one or more worksites based on the obscurant metric.

Abstract: Control zones are dynamically identified on a thematic map and work machine actuator settings are dynamically identified for each control zone. A position of the work machine is sensed and actuators on the work machine are controlled based on the control zones that the work machine is in, and based upon the settings corresponding to the control zone. When modification criteria are met, an actuator setting in a control zone can be modified, during operation. The control zone is then divided on a near real-time display into a harvested portion of the control zone, and a new control zone that has yet to be harvested, and that has the modified actuator setting value. A record is then generated and stored, for the harvested control zone, and for the new control zone.

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

Abstract: A work machine receives a thematic map that maps values of a variable to different geographic locations at a worksite. Control zones are dynamically identified on the thematic map and actuator settings are dynamically identified for each control zone. A position of the work machine is sensed, and actuators on the work machine are controlled based upon the control zones that the work machine is in, or is entering, and based upon the settings corresponding to the control zone. These control zones and settings are dynamically adjusted based on in situ (field) data collected by sensors on the work machine.

Abstract: A map obtainable by an agricultural harvester contains regime zones with settings resolvers. A plurality of different target actuator settings corresponding to the geographic location of the agricultural work machine are identified. A regime zone is identified based on the geographic location of the agricultural harvester. One of the plurality of different target actuator settings is selected as a resolved target actuator setting based on the settings resolver corresponding to the identified regime zone and is used to control the agricultural harvester.

Abstract: A predictive map is obtained by an agricultural material application system. The predictive map maps predictive weed values at different geographic locations in a field. A geographic position sensor detects a geographic locations of an agricultural material application machine at the field. A control system generates a control signal to control the agricultural material application machine based on the geographic locations of the agricultural material application machine and the predictive map.

Abstract: Systems and methods for controlling agricultural headers based on crop movement relative to a portion of the agricultural header are disclosed. The presence or movement of crop material, such as a crop material representing grain (e.g., ears, heads, or pods of crops (“EHP”)), relative to the harvester header or a portion of the harvester header may be detected, such as by analyzing image data representing one or more images collected over time. Based on a position or movement or both of the crop material relative to the header, one or more parameters of the harvester header may be adjusted, for example, to reduce an amount of grain loss.

Abstract: A method for remediating developmentally delayed plants within a field of crops using at least one work vehicle during a field operation. The method comprises identifying delayed plants within the field with a sensor on the work vehicle and generating, with a processor, location data associated with the location of the delayed plant with the field. Upon arriving at the location of the delayed plant with the work vehicle, the delayed plant is remediated.

Abstract: An information map is obtained by an agricultural system. The information map maps values of a topographic characteristic to different geographic locations in a field. An in-situ sensor detects values of an environmental characteristic as an agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts the environmental characteristic at different locations in the field based on a relationship between the values of the topographic characteristic and the values of the environmental characteristic detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

Abstract: A field planting system for planting seeds of at least two plant genotypes in a field includes a multi-variety planter operable to plant the seeds of the at least two plant genotypes in the field. The field planting system also includes a controller in operable communication with the multi-variety planter. The controller is configured to receive georeferenced field information for the field and plant information for the at least two plant genotypes, and generate a planting map for the field based on the georeferenced field information and the plant information, the planting map including a planting pattern for interplanting the seeds of the at least two plant genotypes. The controller is further configured to monitor a location of the multi-variety planter in the field, and control the multi-variety planter to plant the seeds of the at least two plant genotypes in the field according to the planting map.

Abstract: Systems, methods, and devices for receiving soil parameter data of a worksite and identifying one or more locations of the worksite for treatment based on the received soil parameter data are disclosed. In some implementations, the soil parameter data include thermal latency data. Soil compaction data of the worksite may be derived from the thermal latency data. A soil parameter map of the worksite may be generated based on the received soil parameter data, and a plan of action, such as identifying one or more locations to perform a soil treatment operation, may be determined based on the soil parameter map.

Abstract: A mobile agricultural machine obtains an agricultural characteristic map indicative of agricultural characteristics of a field, wherein the agricultural characteristic map is based on data collected at or prior to a first time. The mobile agricultural machine obtains supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time. An agricultural characteristic confidence output, indicative of a confidence level in the agricultural characteristics indicated by the agricultural characteristic map, is generated based on the agricultural characteristic map and the supplemental data. In some examples, an action signal is generated to control an action of the mobile agricultural machine based on the agricultural characteristic confidence output.

Abstract: An electronic data processor is configured to estimate a spatial region of interest of plant pixels of one or more target plants in the obtained image data for a harvestable plant component and its associated harvestable plant component pixels of the harvestable plant component. The electronic data processor is configured to identify the component pixels of a harvestable plant component within the obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels. A user interface is configured to provide the detected size of the harvestable plant component for the one or more target plants as an indicator of yield of the one or more plants or standing crop in the field.

Abstract: An data processor is configured to identify the component pixels of a harvestable plant component within an obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels, along with an adjustment based on analysis of any exposed portions of the harvestable plant component. A user interface is configured to provide a yield indicator based on the detected size of the harvestable plant component.

Abstract: A mobile agricultural machine receives a topographic map indicative of topographic characteristics of a worksite, wherein the topographic characteristics are based on data collected at or prior to a first time and receiving supplemental data indicative of characteristics relative to the worksite, the supplemental data collected after the first time. A topographic confidence output is generated which is indicative of a confidence level in the topographic characteristics of the worksite as indicated by the topographic map, based on the topographic map and the supplemental data. In some examples, an action signal is generated to control an action based on the topographic confidence output.

Abstract: An electronic data processor is configured to identify the component pixels of a harvestable plant component within the obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels. A user interface is configured to provide an aggregate, sectional yield, or per row yield based on a detected size of the harvestable plant component for the one or more target plants as an indicator of yield of the one or more plants or standing crop in the field.

Abstract: Worksite data from an unmanned aerial vehicle (UAV) is received and an indication of the worksite data is generated. A quality of the received worksite data is calculated based on quality data within the received worksite data. Control signals are generated and provided to the UAV based on the calculated quality of the received worksite data. Additional worksite data corresponding to a modified surface of a worksite area is received from a plurality of mobile machines at the worksite area. A worksite error is calculated for the modified surface of the worksite area. Control signals are generated and provided to a UAV based on the calculated worksite error for the modified surface of the worksite area.

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

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