Raven Industries, Inc.

A sprayer nozzle assembly includes a control valve configured to control a flow rate of agricultural product according to a duty cycle having valve open and valve closed positions. The assembly includes a sprayer tip having a tip orifice for spraying the agricultural product. A throttled interface duct interconnects the control valve and the sprayer tip. The throttled interface duct includes an interface duct profile. The interface duct profile is configured to decrease a flow characteristic of the agricultural product while the control valve is in the valve closed position.

A configurable nozzle includes a nozzle body having a reception chamber configured to receive an application mixture. The nozzle body includes a nozzle orifice. At least one orifice assembly is coupled with the nozzle body, the at least one orifice assembly includes an orifice plate movably coupled with the nozzle body. The orifice plate extends along at least a portion of the nozzle orifice, and movement of the orifice plate changes one or more of the size or shape of the nozzle orifice. An orifice actuator is coupled with the orifice plate, and the orifice actuator is configured to move the orifice plate.

A sprayer control system includes a plurality of smart nozzles each having at least one control valve with a valve operator, an electronic control unit for the valve operator, and one or more spray nozzles. The at least one control valve and the ECU control a flow rate of liquid agricultural product through the nozzles. A duty cycle modulator is in communication with the ECU and generates an applied duty cycle for the at least one control valve. The duty cycle modulator includes a specified duty cycle input having a specified duty cycle and a pressure monitor associated with the at least one control valve. A pressure comparator compares the valve pressure determined with the pressure monitor with a system pressure and generates a pressure error. An applied duty cycle generator generates the applied duty cycle based on the specified duty cycle modified by the pressure error.

A product dispensing system includes at least one recirculation network configured for communication with a system pump. The at least one recirculation network includes at least one sprayer boom tube and a plurality of product dispensers distributed along the at least one sprayer boom tube. A network pump is in communication with the at least one sprayer boom tube. The at least one recirculation network includes a dispensing configuration and a recirculation configuration. In the dispensing configuration the recirculation network is configured for communication with a supply tank and an agricultural product reservoir. In the recirculation configuration the recirculation network is isolated from the supply tank and the agricultural product reservoir, and the recirculation network includes a recirculation loop continuously extending through the network pump and the at least one sprayer boom tube.

A system for applying an agricultural product includes at least one control valve. For instance, the control valve has a moveable valve operator. One or more sensors monitor one or more control valve characteristics. A valve controller is configured to determine the control valve is in one or more of a primed state or an unprimed state based on a comparison of the one or more control valve characteristics to a primed valve characteristic threshold.

A method may include receiving, using a processing unit, a request to calculate an estimated duration of an agricultural job, the request including an agricultural job identifier, issuing, using the processing unit, a data retrieval command using the agricultural job identifier, receiving field characteristics associated with a field, the field characteristics including a field shape value and an area of the field, encoding, using the processing unit, the field characteristics into a data structure configured to be input into a job duration machine learning model, executing, using the processing unit, the job duration machine learning model using the data structure, after executing the job duration machine learning model, accessing an output of the job duration machine learning model, the output associated with the estimated duration of the agricultural job, and transmitting, using the processing unit, the estimated duration and the job identifier to an agricultural job scheduling system.

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.

The present disclosure relates to a system and method 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 system and method for the detection and tracking of droplets sprayed from an agricultural spray nozzle is provided. The system includes a sensor configured to observe droplets sprayed from the nozzle, a frame extractor module, a droplet shape and size extraction module, a droplet tracking module, and a data log module. The system may provide an artificial intelligence (AI)-enabled framework capable of processing images obtained of droplets, detecting and tracking all droplets appearing across image frames, and determining the droplets' geometric and dynamic data. The system further provides for the integration of deep-learning techniques into an image processing algorithm which enables precise and reliable determination of droplet characteristics. In addition, the deep-learning framework produces consistent results under a variety of uncertain imaging conditions.

A landmarking navigation system includes one or more sensors that observe one or more features in a field or proximate to a field. One or more processors are in communication with the one or more sensors. The one or more processors landmark the one or more features. Landmarking includes identifying the one or more features as landmarks, respectively. The landmarks are catalogued as catalogued landmarks. The one or more processors landmark navigate the agricultural vehicle. Landmark navigating includes observing the one or more features in the field with the sensors and comparing the observations with the catalogued landmarks. The features are identified as the catalogued landmarks, respectively. Vehicle kinematics (one or more of position, heading, speed, pitch, yaw or roll) of the agricultural vehicle are determined relative to the identified catalogued landmarks.