A unit (UO) for the processing ground (T), comprises at least a first group (G1, D3) for supporting rotating cutting disk means (D) adapted to realize grooves (S) in said ground (T); at least a second group (G2) for supporting plowshare means (V, E) adapted to realize, in correspondence of said grooves (S), cuts (Z) below the surface of said ground (T); and mechanical coupling means (M2) for dynamically connecting said second group (G2) to said first group (G1, D3).

A towed agricultural implement includes one or more processing units each supported on a chassis, in which each chassis supports at least one processing unit on a central axis, the chassis having a left hand side wheel assembly support and a right hand side wheel assembly support, free ends of the wheel assembly supports having wheel assemblies, each wheel assembly comprising a wheel mounted on a wheel mount, the wheel mount being rotatable about an inclined axis, and the wheel being rotatable about a horizontal axis. The wheel assembly supporting structures and the wheel mounts allow limited relative movement of each wheel mount with respect to the wheel assembly supporting structures which enables greater control when the towed agricultural implement is towed about a corner.

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.

A towed agricultural implement includes one or more processing units each supported on a chassis, in which each chassis supports at least one processing unit on a central axis, the chassis having a left hand side wheel assembly support and a right hand side wheel assembly support, free ends of the wheel assembly supports having wheel assemblies, each wheel assembly comprising a wheel mounted on a wheel mount, the wheel mount being rotatable about an inclined axis, and the wheel being rotatable about a horizontal axis. The wheel assembly supporting structures and the wheel mounts allow limited relative movement of each wheel mount with respect to the wheel assembly supporting structures which enables greater control when the towed agricultural implement is towed about a corner.

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.

An apparatus for combining planting implements enables an operator of a tractor to use both a conventional cultivating implement and a conventional broadcasting implement simultaneously. The apparatus is rigidly affixed to a conventional cultivating implement and comprises a support frame, a pair of brackets, and a connection bar. In combination, the support frame, pair of brackets, and connection bar provide a three-point connection whereby a conventional broadcasting implement may be attached thereto, thus enabling an operator of a tractor to use both a conventional cultivating implement and a conventional broadcasting implement simultaneously. In operation, at least a portion of the conventional cultivating implement is disposed behind the conventional broadcasting implement when the conventional broadcasting implement is attached to the support frame, thereby allowing simultaneous cultivation and broadcasting of constituents.

An apparatus for combining planting implements enables an operator of a tractor to use both a conventional cultivating implement and a conventional broadcasting implement simultaneously. The apparatus is rigidly affixed to a conventional cultivating implement and comprises a support frame, a pair of brackets, and a connection bar. In combination, the support frame, pair of brackets, and connection bar provide a three-point connection whereby a conventional broadcasting implement may be attached thereto, thus enabling an operator of a tractor to use both a conventional cultivating implement and a conventional broadcasting implement simultaneously. In operation, at least a portion of the conventional cultivating implement is disposed behind the conventional broadcasting implement when the conventional broadcasting implement is attached to the support frame, thereby allowing simultaneous cultivation and broadcasting of constituents.

An apparatus for combining planting implements enables an operator of a tractor to use both a conventional cultivating implement and a conventional broadcasting implement simultaneously. The apparatus is rigidly affixed to a conventional cultivating implement and comprises a support frame, a pair of brackets, and a connection bar. In combination, the support frame, pair of brackets, and connection bar provide a three-point connection whereby a conventional broadcasting implement may be attached thereto, thus enabling an operator of a tractor to use both a conventional cultivating implement and a conventional broadcasting implement simultaneously. In operation, at least a portion of the conventional cultivating implement is disposed behind the conventional broadcasting implement when the conventional broadcasting implement is attached to the support frame, thereby allowing simultaneous cultivation and broadcasting of constituents.