An agricultural row unit assembly employs a pair of tine assemblies and a seedbed cultivator for soil preparation to enhance opportune planting.

An agricultural row unit assembly employs a pair of tine assemblies and a seedbed cultivator for soil preparation to enhance opportune planting.

A dynamic linkage for a tool gang on a multi-row agricultural implement. The tool gang can include one or more ground-working or other agricultural tools, which are mounted on a tool gang frame. The tool gang frame is connected to a toolbar by the dynamic linkage, each includes adjusting mechanisms for limiting vertical travel and down-pressure forces applied to the tools. The dynamic linkage is configured for tripping to raise the tool gang for clearing a surface or subsurface obstacle.

An agricultural implement includes a main frame, a rotating blade assembly operatively connected to the main frame, the rotating blade assembly comprising a shaft having a plurality of blades mounted thereto, and a first grader blade and a second grader blade operatively connected to the main frame. Both the first grader blade and the second grader blade follow the rotating blade assembly such that in operation the plurality of blades positioned along the rotating blade assembly chop up a soil heave into smaller pieces of dirt and may for provide for pushing the soil towards a center. The first grader and the second grader may further push the dirt towards the center.

A tillage implement having a frame member extending in a fore-aft direction of the implement, the frame member pivotally connected in a foldable configuration, the frame member comprising a main frame section; a first wing section and a second wing section, each being pivotally connected at opposing lateral sides of the main frame section in the fore-aft direction; and a middle breaker including a first disc coupled to the main frame section, the first disc laterally offset from a centerline of the middle breaker extending in the fore-aft direction, the first disc having a concave side facing towards the centerline, and a second disc coupled to the main frame section, laterally offset from the centerline of the middle breaker, the second disc having a concave side facing towards the centerline.

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.

A rear tow hitch assembly connectable to a work tool assembly on a first agricultural implement to tow a second agricultural implement behind the first agricultural implement is provided. The first agricultural implement can be transformable between an operating position and a transport position by pivoting the work tool assembly upwards around a first axis. The rear tow hitch assembly can be shaped to clear the work tool assembly when the first agricultural implement is transformed between the operating position and the transport position while a rear tow hitch assembly remains connected to the second agricultural implement during the transformation.

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.

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 convolutional neural network to determine a level of crop residue 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.

A basket rotates about a basket axis with respect to an implement frame when moving in a direction of travel. The basket includes first and second end plates and a plurality of blades extending therebetween. The blades engage the ground surface as the basket rotates and each include a first end and a second end positioned closer to the basket than the first end. The basket defines an internal volume between the first and second end plates and the second ends of the blades. An internal scraper is positioned within the internal volume of the basket such that a portion of the internal scraper extends rearward of a vertical plane extending through the basket axis in the direction of travel and vertically away from the ground surface above a horizontal second plane extending through the basket axis. The internal scraper extends at an acute angle with respect to horizontal.