A method includes receiving a first signal indicative of an implement pitch angle from a sensor, determining whether the pitch angle is within a pitch angle range, generating a first control signal indicative of instructions to adjust a hitch actuator if the pitch angle is not within the pitch angle range, and communicating the first control signal to the hitch actuator.

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned crop residue classification model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. Furthermore, principal components analysis, such as projecting image patches onto Eigen-images, can be performed to reduce the dimensionality of the feature vector provided to the classification model.

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned crop residue classification model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. Furthermore, principal components analysis, such as projecting image patches onto Eigen-images, can be performed to reduce the dimensionality of the feature vector provided to the classification model.

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned semantic segmentation model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned semantic segmentation model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

Systems, methods and apparatus for shifting weight between a tractor and toolbar and between sections of the toolbar, for controlling operative height of a toolbar and sections of a toolbar and for folding a toolbar between a work position and a transport position. A ground engaging wheel and an actuator are coupled to the toolbar. In one embodiment, a fluid control system is responsive to a command signal to modify the actuator pressure such that the actuator pressure corresponds to a desired pressure.

A method for automatically leveling an agricultural implement being towed by a work vehicle includes monitoring a vehicle inclination angle via at least one vehicle-based sensor supported on the work vehicle, monitoring an implement inclination angle via at least one implement-based sensor supported on the implement, and determining that the work vehicle has begun to travel across an inclined surface based on the vehicle inclination angle. The method further includes initially adjusting a position of a hitch of the work vehicle in a first direction to maintain the implement inclination angle within a predetermined angular inclination range as the vehicle travels across the inclined surface and adjusting the position of the hitch in a second direction opposite the first direction to maintain the implement inclination angle within the predetermined angular inclination range.

A method for automatically leveling an agricultural implement being towed by a work vehicle includes monitoring a vehicle inclination angle via at least one vehicle-based sensor supported on the work vehicle, monitoring an implement inclination angle via at least one implement-based sensor supported on the implement, and determining that the work vehicle has begun to travel across an inclined surface based on the vehicle inclination angle. The method further includes initially adjusting a position of a hitch of the work vehicle in a first direction to maintain the implement inclination angle within a predetermined angular inclination range as the vehicle travels across the inclined surface and adjusting the position of the hitch in a second direction opposite the first direction to maintain the implement inclination angle within the predetermined angular inclination range.

Systems and methods for real-time, artificial intelligence control of an agricultural work vehicle and/or implement based on observed outcomes are provided. In particular, example aspects of the present subject matter are directed to systems and method that sense field conditions (also known as field finish) both before and after adjustable ground-engaging tools encounter the soil and that update a site-specific control model that provides control settings based on the observed anterior and posterior conditions. Thus, a control system can obtain sensor data descriptive of upcoming field conditions and can perform predictive adjustment and control of tools based the upcoming field conditions. The system can then use additional sensors to observe the outcome of the employed control settings. Based on a comparison of the observed outcome to a target outcome, the system can adjust for the next encounter of similar field conditions.

Systems and methods for real-time, artificial intelligence control of an agricultural work vehicle and/or implement based on observed outcomes are provided. In particular, example aspects of the present subject matter are directed to systems and method that sense field conditions (also known as field finish) both before and after adjustable ground-engaging tools encounter the soil and that update a site-specific control model that provides control settings based on the observed anterior and posterior conditions. Thus, a control system can obtain sensor data descriptive of upcoming field conditions and can perform predictive adjustment and control of tools based the upcoming field conditions. The system can then use additional sensors to observe the outcome of the employed control settings. Based on a comparison of the observed outcome to a target outcome, the system can adjust for the next encounter of similar field conditions.