Abstract: A computer-implemented method of operating an agricultural work machine is provided. The method includes initiating row sensing guidance for the agricultural work machine to guide steering of the agricultural work machine based at least one signal from a row sensor; obtaining contextual information; determining whether a row is present based on the contextual information; and selectively ignoring the at least one signal from the row sensor based on whether a row is present. An agricultural work machine and a control system for an agricultural work machine are also provided.

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: A definition of a boundary corresponding to a geographic area in which an autonomous off-road vehicle (AOV) operates is accessed. The boundary definition includes a classification of each of a plurality of portions of the boundary as being of one or more boundary types, each boundary type associated with one or more vehicle behavior limitations. The AOV navigates to a target portion of the boundary corresponding to the geographic area. The target portion of the boundary is classified as being one or more target boundary types. In response to being within a threshold distance of the target portion of the boundary, the AOV modifies a behavior of the AOV based on the one or more vehicle behavior limitations associated with the one or more target boundary types.

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: A definition of a boundary corresponding to a geographic area in which an autonomous off-road vehicle (AOV) operates is accessed. The boundary definition includes a classification of each of a plurality of portions of the boundary as being of one or more boundary types, each boundary type associated with one or more vehicle behavior limitations. The AOV navigates to a target portion of the boundary corresponding to the geographic area. The target portion of the boundary is classified as being one or more target boundary types. In response to being within a threshold distance of the target portion of the boundary, the AOV modifies a behavior of the AOV based on the one or more vehicle behavior limitations associated with the one or more target boundary types.

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: A roll sensor is configured to detect a first roll angle at a corresponding first point of a path plan of a vehicle, or its implement, based on the estimated current position. A roll sensor is configured to detect a second roll angle at a corresponding second point of a path plan of a vehicle, or its implement, based on the estimated current position. A data processor is configured to determine a roll angle delta or difference between the first roll angle and the second roll angle. If the roll angle delta is greater than a reference roll value, the roll angle delta is evaluated over an evaluation period. For example, if the roll angle is greater than the reference roll value for equal to or greater than a target percentage or target ratio for the evaluation period, the data processor is configured to designate the roll angle sensor for possible recalibration.

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: A control system detects an agricultural machine position to determine when the agricultural machine is approaching a decision point. The decision point is a point at which the agricultural machine can move forward along one of two or more predefined possible paths. The control system detects agronomic factors and vehicle-related parameters to decide on the path to take at the decision point. The control system then generates control signals to pursue the path decided upon.

Abstract: Operations of an agricultural machine can be controlled based at least in part on a determination of whether the agricultural machine is entering/exiting a coverage area or a non-coverage area. In a non-coverage area, guidance of the agricultural machine can be based at least in part on detection by a row sensor of the agricultural machine of a presence of crops adjacent to the agricultural machine. In a coverage area, guidance of the agricultural machine can be primarily based on other received information, such as information from a global positioning system. Further, identification of an approaching coverage area and/or non-coverage area can assist in determining whether to activate or deactivate a subsystem(s) of the agricultural machine that performs an agricultural operation(s). Agricultural operations performed in non-coverage areas can be recorded on a coverage map, which may be accessed, as well as updated, by a plurality of agricultural machines.

Abstract: A computer-implemented method of controlling a mobile agricultural machine includes obtaining prior field data representing a position of plants in a field, obtaining in situ plant detection data from operation of the mobile agricultural machine in the field, determining a position error in the prior field data based on the in situ plant detection data, and generating a control signal that controls the mobile agricultural machine based on the determined position error.

Abstract: A definition of a boundary corresponding to a geographic area in which an autonomous off-road vehicle (AOV) operates is accessed. The boundary definition includes a classification of each of a plurality of portions of the boundary as being of one or more boundary types, each boundary type associated with one or more vehicle behavior limitations. The AOV navigates to a target portion of the boundary corresponding to the geographic area. The target portion of the boundary is classified as being one or more target boundary types. In response to being within a threshold distance of the target portion of the boundary, the AOV modifies a behavior of the AOV based on the one or more vehicle behavior limitations associated with the one or more target boundary types.

Abstract: A path planning module or electronic data processor is configured to establish a path plan for a planter to cover a first area of the field at least once with a sequence of path segments that are generally parallel to each other and spaced apart. A path interference estimator or the electronic data processor is configured to estimate potential interference between two or more path segments with respect to a first path segment and a second path segment. An exclusion zone module or the electronic data processor is configured to determine an exclusion zone for the planter to prohibit planting of one or more rows of seeds or seedlings within the exclusion zone based on the estimated potential interference. The electronic data processor is configured to activate or execute the path plan and determined exclusion zone in accordance with the established path plan and determined exclusion zone.

Abstract: A method and system for estimating surface roughness of a ground for an off-road vehicle to control ground speed comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A pitch sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A roll sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle, or applied to control or execute a ground speed setting of the vehicle.

Abstract: A method and system for estimating surface roughness of a ground for an off-road vehicle to control an implement comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

Abstract: A method and system for estimating surface roughness of a ground for an off-road vehicle to control steering of a vehicle, an implement, or both, comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

Abstract: A method and system for estimating surface roughness of a ground for an off-road vehicle to control an implement comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

Abstract: A method and system for estimating surface roughness of a ground for an off-road vehicle to control ground speed comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A pitch sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A roll sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle, or applied to control or execute a ground speed setting of the vehicle.

Abstract: A method and system for estimating surface roughness of a ground for an off-road vehicle to control steering of a vehicle, an implement, or both, comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

Abstract: A computer-implemented method of controlling a mobile agricultural machine includes obtaining prior field data representing a position of plants in a field, obtaining in situ plant detection data from operation of the mobile agricultural machine in the field, determining a position error in the prior field data based on the in situ plant detection data, and generating a control signal that controls the mobile agricultural machine based on the determined position error.