A pond filter unit and a method for operating a pond filter unit is provided. A control unit controls the operation of the pond filter unit, including the cleaning cycle of the filter. The pond filter unit further includes one or more plugs for connecting one or more auxiliary devices, such as a separate pump, one or more additional pond filter units, illumination means/lamps, air pumps, and/or feed automats. The control unit of the pond filter further controls the one or more auxiliary devices connected to the plugs, such as pumps, air pumps, automatic feeding devices based on input settings for each of the one or more auxiliary devices. The control unit further controls the cleaning cycle for cleaning of the filter in the vessel.

A self-harvesting sustaining feed system includes a tank for housing fish, a frame located above the fish tank, one or more plant floats which float atop water within shelves held by the frame, and an insect tray for housing larvae. Temporary temperature changes in the insect tray cause the larvae within the insect tray to migrate out and fall into the fish tank below.

A self-harvesting sustaining feed system includes a tank for housing fish, a frame located above the fish tank, one or more plant floats which float atop water within shelves held by the frame, and an insect tray for housing larvae. Temporary temperature changes in the insect tray cause the larvae within the insect tray to migrate out and fall into the fish tank below.

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 floating pond fertilizer device is provided. The device includes a tray for holding fertilizer at the surface of a body of water to be fertilized. The tray has small holes in the bottom of the tray through which fertilizer is released and is secured to a float to support the tray on the surface of the water. The device has opposing mixing arms positioned inside the tray for mixing the fertilizer. The mixing arms are attached to a support arm that can freely rotate within an opening in the center of the bottom of the tray. Rotation of the support arm is facilitated by natural wind and wave action, and thus the device does not require the use of motorized or automated components for mixing or dispensing the fertilizer.

The present invention relates to a machine learning based software system that helps aquaculture facility operators optimize the productivity of their aquatic farming operations. In particular, the present invention provides information useable by people and by computer-controlled machines about how to adjust feed recipes, feeding rates, controllable operational conditions such as air and water conditions, and other controllable environmental conditions so that the growth rate and size of farmed aquatic animals and plants can be maximized while the cost and ecological impact of the aquatic animals and plants being farmed are minimized

The present invention relates to a machine learning based software system that helps aquaculture facility operators optimize the productivity of their aquatic farming operations. In particular, the present invention provides information useable by people and by computer-controlled machines about how to adjust feed recipes, feeding rates, controllable operational conditions such as air and water conditions, and other controllable environmental conditions so that the growth rate and size of farmed aquatic animals and plants can be maximized while the cost and ecological impact of the aquatic animals and plants being farmed are minimized

Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system

Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system

Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system.