Abstract: The present disclosure provides systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements. In particular, the present subject matter is directed to a calibration process and system that uses a calibration model to convert field measurement data expressed according to an automatic system metric into agronomy data that is expressed according to an agronomy metric.

Abstract: In one aspect, a system for monitoring sensor performance on an agricultural machine may include a controller configured to receive a plurality of images from the vision-based sensor mounted on an agricultural machine. The controller may be configured to determine an image parameter value associated with each of a plurality of pixels contained within each of the plurality of images. For each respective pixel of the plurality of pixels, the controller may be configured to determine a variance associated with the image parameter values for the respective pixel across the plurality of images. Furthermore, when the variance associated with the image parameter values for a given pixel of the plurality of pixels falls outside of a predetermined range, the controller may be configured to identify the given pixel as being at least one of obscured or inoperative.

Abstract: A system including a beamforming-sensor configured to acquire sensor-data representative of a swath in an agricultural field; and a controller configured to determine swath-property-data based on the sensor-data.

Abstract: A system having a radar-sensor configured to acquire radar-data representative of swath in an agricultural field; and a controller configured to determine swath-property-data based on the radar-data.

Abstract: A method for automatically monitoring soil surface roughness as a ground-engaging operation is being performed within a field may include receiving pre-operation surface roughness data associated with a given portion of the field and receiving post-operation surface roughness data associated with the given portion of the field. In addition, the method may include analyzing the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the ground-engaging operation and actively adjusting the operation of at least one of an associated work vehicle and/or implement when the surface roughness differential differs from a target set for the surface roughness differential.

Abstract: The present invention is based, in part, on the discovery of monoclonal antibodies, and antigen-binding fragments thereof, that specifically bind to MICA/B ?3 domain, as well as immunoglobulins, polypeptides, nucleic acids thereof, and methods of using such antibodies for diagnostic, prognostic, and therapeutic purposes.

Abstract: The present disclosure provides systems and methods that measure soil roughness 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 clod detection model to determine a soil roughness 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.

Abstract: A bowling centre includes at least one bowling lane along which the bowl is rolled and having a rolling surface at one end of which are positioned the pins to be knocked down and at the opposite, longitudinal end of which is located the bowler's bay from where the bowl is thrown, the at least one lane including a side bumper running along each side of the lane and movable between a lowered position which allows the bowl to roll freely off the lane into a gutter which channels the bowl out of the lane, and a raised position for keeping the bowl on the bowling lane; an electronic control system configured to manage the at least one lane to implement a scoring program which calculates the score of the game played on the at least one lane; a lighting system includes a plurality of lighting units mounted on each side bumper, the lighting system being connected to the electronic control system which is configured to individually control each lighting unit in such a way as to light respective zones of each bumper in

Abstract: A device, in particular a keyboard, for data or command entry is disclosed. The device is suitable for entering data or commands into an electronic processing system, in particular, into an electronic system or apparatus for controlling a respective installation, such as an entertainment centre such as a bowling centre.

Abstract: An agricultural system is disclosed comprising one or more radar sensors configured to acquire radar data representative of crop rows in an agricultural field. The system also comprises a controller configured to determine crop-property-data based on the radar data. The crop property data is representative of one or more properties of crop rows that are in a field.

Abstract: In one embodiment, one or more tangible, non-transitory, machine-readable media comprising instructions configured to cause a processor to receive a signal indicative of a residue coverage on a surface of an agricultural field from a sensor, and the sensor is positioned in front of a row unit of an agricultural product application system relative to a direction of travel. Further, the instructions are configured to cause the processor to determine the residue coverage on the surface of the agricultural field based on the signal, and the residue coverage includes a percentage of the agricultural field that is covered by residue, an evenness of the residue, or a combination thereof. Moreover, the instructions are configured to cause the processor to control an agricultural product control system of the agricultural product application system based on the residue coverage.

Abstract: The present disclosure provides systems and methods that measure soil roughness 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 clod detection model to determine a soil roughness 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.

Abstract: A canopy erectable by a single user by use of a crank assembly. The canopy is constructed of an expandable frame including legs, vertical rotating members, and horizontal rotating members. Each leg has a foot on one end. One foot has a stepping portion and each of the remaining feet have a sliding portion. The vertical rotating members are each connected to one leg and are movably connected to each other vertical rotating member at a centerpoint. The horizontal rotating members are each rotatably connected between two different legs at a fixed point on each leg. Each leg also has a movable bracket slidably connected to it and the bracket is rotably connected to each adjacent horizontal rotating member. A crank is affixed to the leg with the first foot; wherein the crank is operably connected to control the position of the movable bracket.

Abstract: In one aspect, a system for monitoring the frame levelness of an agricultural implement include first and second sensors configured to capture data indicative of a position differential defined between a soil surface and a portion of an a first and second ground engaging tool positioned below the soil surface, respectively. The captured data may be associated at least partially with the receipt of sensor signals reflected off of the portion of the associated ground engaging tool positioned below the soil surface. The system may also include a controller configured to determine penetration depths of the first and second ground engaging tools based on the captured data received from the first and second sensors, respectively. The controller may also be configured to monitor the frame levelness based on a penetration depth differential defined between the first and second penetration depths.

Abstract: In one aspect, a system for illuminating a field of view of a vision-based sensor mounted on an agricultural machine may include an agricultural machine having a vision-based sensor. The system may also include a light source configured to emit supplemental light to illuminate at least a portion of the field of view of the vision-based sensor. Furthermore, the system may include a controller communicatively the light source. The controller may configured to control an operation of the light source based on an input indicative of ambient light present within the field of view of the vision-based sensor.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements. In particular, the present subject matter is directed to a calibration process and system that uses a calibration model to convert field measurement data expressed according to an automatic system metric into agronomy data that is expressed according to an agronomy metric.

Abstract: 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.

Abstract: In one aspect, a system for adjusting a sampling rate of a sensor mounted on an agricultural machine may include an agricultural machine and a sensor mounted on the agricultural machine, with the sensor being configured to capture data at a sampling rate. The system may also include a controller communicatively coupled to the sensor. The controller may be configured to receive an input indicative of an operational parameter of the agricultural machine and adjust the sampling rate at which the sensor captures data based on the received input.