Raven Industries, Inc.

John D Preheim

Jared Ernest Kocer

Brent A Driesen

Yuri Sneyders

Jeffrey Allen Van Roekel

Alex John Weidenbach

Nicholas O Michael

Jesse Lee Wagers

Drew John Waltner

Joel Quentin Quanbeck

Robert Leonard Nelson, Jr

Edwin Ernest Wilson

Systems and methods may be used to perform nozzle control. For example, a system includes an overall system pressure valve, configured to adjust the pressure of an agricultural product within a boom. A master node is configured to receive an overall system flow rate measurement, an overall target flow rate, and an overall system pressure measurement. The master node is configured to adjust the overall system pressure valve to control the pressure of the agricultural product. A plurality of smart nozzles are configured to dispense the agricultural product, the plurality of smart nozzles each associated with an electronic control unit (ECU) and one or more individual nozzle. The smart nozzle is configured to control a nozzle flow rate of the associated one or more individual nozzles.

Nozzle control system and method

A localized product injection system includes a composite boom tube having a carrier fluid passage within a tube body, and at least one injection product passage within the tube body isolated from the carrier fluid passage. A plurality of port stations are provided at locations along the tube body. Each of the port stations includes a carrier fluid outlet port and at least one injection product outlet port. A localized injection interface is coupled at a port station. The injection interface includes a carrier fluid input coupled with the carrier fluid outlet port, and at least one injection product input coupled with the at least one injection product outlet port. The injection interface includes at least one throttling element in communication with the at least one injection product input, a mixing chamber, and an injection port configured for localized coupling and injection to a product dispenser.

Localized product injection system and methods for same

A semi-active suspension system includes a suspension element having a damping coefficient range. The suspension element optionally includes an implement end and a chassis end. The semi-active suspension system includes a suspension control circuit in communication with the suspension element. The suspension control circuit optionally includes a kinematic assessment circuit in communication with one or more sensors. The kinematic assessment circuit is configured to measure or determine kinematic characteristics of one or more of the agricultural implement and the chassis. The suspension control circuit optionally includes a damping control circuit, and the damping control circuit generates a specified damping characteristic based on the measured or determined kinematic characteristics. The damping control circuit optionally directs the suspension element to operate within the damping coefficient range based on the specified damping characteristic.

Agricultural implement suspension system

A system is configured for nozzle control. The system includes an overall system pressure valve, configured to adjust the pressure of an agricultural product within a boom. A master node is configured to receive an overall system flow rate measurement, an overall target flow rate, and an overall system pressure measurement, the master node configured to adjust the overall system pressure valve to control the pressure of the agricultural product. A plurality of smart nozzles are configured to dispense the agricultural product, the plurality of smart nozzles each associated with an electronic control unit (ECU) and one or more individual nozzle, the smart nozzle is configured to control a nozzle flow rate of the associated one or more individual nozzles.

A device to determine a height disparity between features of an image includes a memory including instructions and processing circuitry. The processing circuitry is configured by the instructions to obtain an image including a first repetitive feature and a second repetitive feature. The processing circuitry is further configured by the instructions to determine a distribution of pixels in a first area of the image, where the first area includes an occurrence of the repetitive features, and to determine a distribution of pixels in a second area of the image, where the second area includes another occurrence of the repetitive features. The processing circuitry is further configured by the instructions to evaluate the distribution of pixels in the first area and the distribution of pixels in the second area to determine a height difference between the first repetitive feature and the second repetitive feature.

Determining image feature height disparity

System and techniques for detecting a crop related row from an image are described herein. An image that includes several rows&#x2014;where the several rows including crop rows and furrows&#x2014;can be obtained. The image can be segmented to produce a set of image segments. A filter can be shifted across respective segments of the set of image segments to get a set of positions. A line can be fit members of the set of positions, the line representing a crop row or furrow.

Detecting crop related row from image

An agricultural vehicle monitoring system includes first and second noncontact sensors configured for coupling with an agricultural vehicle, where the first and second noncontact sensors are configured to sense respective first and second environmental characteristics for determining a position of the agricultural vehicle in a field. The system further includes a comparative vehicle monitor in communication with the first and second noncontact sensors. The comparative vehicle monitor includes a filter module to filter outputs of the first and second noncontact sensors based on an indicator of a relative quality of the output of each sensor. The comparative vehicle monitor additionally includes an evaluation module to determine a vehicle position of the agricultural vehicle relative to at least one of the first and second environmental characteristics according to filtered outputs of the first and second noncontact sensors.

Comparative agricultural obstacle monitor and guidance system and method for same

System and techniques for calibrating a crop row computer vision system are described herein. An image set that includes crop rows and furrows is obtained. Models of the field are searched to find a model that best fits the field. A calibration parameter is extracted from the model and communicated to a receiver.

Calibrating crop row computer vision system

An automated implement control system includes one or more distance sensors configured for coupling with an agricultural implement. The one or more distance sensors are configured to measure a ground distance and a canopy distance from the one or more sensors to the ground and crop canopy, respectively. An implement control module is in communication with the one or more distance sensors. The implement control module controls movement of the agricultural implement. The implement control module includes a confidence module configured to determine a ground confidence value based on the measured ground distance and a canopy confidence value based on the measured canopy distance. A target selection module of the implement control module is configured to select one of the measured ground or canopy distances as a control basis for controlling movement of the agricultural implement based on the comparison of confidence values.

Implement position control system and method for same

System and techniques for adaptive color transformation to aid computer vision are described herein. Colors from an image are mapped into a multi-dimensional space to create a distribution of colors in the image. A line can be fit to the distribution. Here, the line includes an angle relative to a coordinate system of the multi-dimensional space. A transformation to colors can then be applied to the image based on the angle of the line. The transformation producing a reduced image where a color complexity of the original image is reduced.

Adaptive color transformation to aid computer vision

An agricultural vehicle monitoring system includes one or more noncontact sensors configured to sense multiple objects along a scanline. A comparative vehicle monitor is in communication with the one or more noncontact sensors. The comparative vehicle monitor is configured to provide a specified row width and to identify one or more crop rows from the scan line and determine one or more lengths of scan line segments between identified crop rows. The comparative vehicle monitor is further configured to determine a vehicle position including one or more of a vehicle angle or a vehicle location according to the specified row width and the one or more determined lengths of scan line segments between the identified crop rows.

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Beresford, SD, United States of America

John D Preheim