A smart agricultural automatic feeding device includes a feeding box, a weighing part is disposed in the feeding box and connected with a top of the feeding box; a separating part is disposed below the weighing part and rotatably connected to an inner wall of the feeding box, a feed end of the separating part is connected with a discharge end of weighing part; a delivery part is disposed below the separating part and includes delivery tubes arranged in parallel, the delivery tubes are fixed to the inner wall of the feeding box, feed ends of the delivery tubes are connected to the discharge end of the separating part and configured to be arranged correspondingly to a fish pond; an air supply part is disposed outside the feeding box and connected to the delivery tubes through a pressurized part, the pressurized part is in transmission connection with the separating part.

A smart agricultural automatic feeding device includes a feeding box, a weighing part is disposed in the feeding box and connected with a top of the feeding box; a separating part is disposed below the weighing part and rotatably connected to an inner wall of the feeding box, a feed end of the separating part is connected with a discharge end of weighing part; a delivery part is disposed below the separating part and includes delivery tubes arranged in parallel, the delivery tubes are fixed to the inner wall of the feeding box, feed ends of the delivery tubes are connected to the discharge end of the separating part and configured to be arranged correspondingly to a fish pond; an air supply part is disposed outside the feeding box and connected to the delivery tubes through a pressurized part, the pressurized part is in transmission connection with the separating part.

Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for automated camera positioning for feeding behavior monitoring. In some implementations, a system obtains an image of a scene, a spatial model that corresponds to a subfeeder, and calibration parameters of a camera, the system determines a size of the subfeeder in the image of the scene, the system selects an updated position of the camera relative to the subfeeder, the system provides the updated position of the camera relative to the subfeeder to a winch controller, and the system moves the camera to the updated position.

Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for automated camera positioning for feeding behavior monitoring. In some implementations, a system obtains an image of a scene, a spatial model that corresponds to a subfeeder, and calibration parameters of a camera, the system determines a size of the subfeeder in the image of the scene, the system selects an updated position of the camera relative to the subfeeder, the system provides the updated position of the camera relative to the subfeeder to a winch controller, and the system moves the camera to the updated position.

A method of surface splash scoring includes receiving, at an electronic device, a set of camera frames corresponding to images of a water surface. The electronic device processes the set of camera frames with a trained machine learning model to generate one or more quantifications associated with fish activity proximate the water surface. In some embodiments, a surface splash score is computed that represents an appetite level anticipated to be exhibited for a first time period. Subsequently, the electronic device generates an output indicative of the surface splash score.

Embodiments for an AI-based system and method for feed monitoring in a land-based fish farm are described where the system may include: a feed camera mounted to an effluent pipe for capturing a video feed having images of objects that traverse a field of view of the feed camera when each image is acquired; and a special-purpose computer that executes a pellet-tracking algorithm that employs a region of interest (ROI) proposal module, an ROI classification module, an ROI tracking module, and a trajectory classification module for at least counting uneaten feed pellets. In at least one embodiment, an object enhancer may be mounted to the effluent pipe opposite to and in the field of view of the feed camera to provide a background so that the objects in the acquired images have a definable perimeter.

Embodiments for an AI-based system and method for feed monitoring in a land-based fish farm are described where the system may include: a feed camera mounted to an effluent pipe for capturing a video feed having images of objects that traverse a field of view of the feed camera when each image is acquired; and a special-purpose computer that executes a pellet-tracking algorithm that employs a region of interest (ROI) proposal module, an ROI classification module, an ROI tracking module, and a trajectory classification module for at least counting uneaten feed pellets. In at least one embodiment, an object enhancer may be mounted to the effluent pipe opposite to and in the field of view of the feed camera to provide a background so that the objects in the acquired images have a definable perimeter.

A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.

A smart aquaculture method is provided for an aquaculture system including a breeding pool, a feeding machine and a camera disposed in the breeding pool. The method includes: taking an underwater image by the camera; calculating a feed remaining amount according to the underwater image; and controlling the feeding machine according to the feed remaining amount to dispense feed to the breeding pool.

A mobile quantitative feeding-spraying integrated system and method. The system includes at least one hull equipped with a propeller, a feeder housing fixedly arranged on the hull, a feed box arranged on the feeder housing, a flip assembly provided at an upper end of the feed box, a blanking device arranged in the feeder housing to quantitatively control the bait blanking amount, a casting device and a spraying device. An upper end of the blanking device is a feeding end, which supports and is connected to the lower end of the feed box. A lower end of the blanking device is the discharging end. An upper side of the casting device is connected with the lower end of the blanking device, and the lower side of the casting device is provided with a discharging end facing the aquaculture water body.