DECISION MAKING SYSTEM AND METHOD OF FEEDING AQUATIC ANIMALS

    公开(公告)号:US20200170227A1

    公开(公告)日:2020-06-04

    申请号:US16627192

    申请日:2018-06-28

    摘要: The present invention relates to a method and apparatus for providing a dynamic decision-making process in relation to feeding animals in water. More particularly, the present invention relates to a method and apparatus for improving feeding and/or farming strategies used in a fish farm. According to a first aspect, there is provided a computer-implemented method for feeding one or more aquatic animals, the method comprising the steps of: receiving pre-processed sensor data in relation to the one or more aquatic animals; inputting the pre-processed sensor data into one or more learned decision-making models, wherein the one or more learned decision-making models has been trained to substantially optimise the rate and amount of food provided to the aquatic animals; determining, by the one or more learned decision-making models using the received pre-processed sensor data, feeding instructions for the one or more aquatic animals; and outputting the feeding instructions from the one or more learned decision-making models.

    Decision making system and method of feeding aquatic animals

    公开(公告)号:US11464213B2

    公开(公告)日:2022-10-11

    申请号:US16627192

    申请日:2018-06-28

    摘要: The present invention relates to a method and apparatus for providing a dynamic decision-making process in relation to feeding animals in water. More particularly, the present invention relates to a method and apparatus for improving feeding and/or farming strategies used in a fish farm. According to a first aspect, there is provided a computer-implemented method for feeding one or more aquatic animals, the method comprising the steps of: receiving pre-processed sensor data in relation to the one or more aquatic animals; inputting the pre-processed sensor data into one or more learned decision-making models, wherein the one or more learned decision-making models has been trained to substantially optimise the rate and amount of food provided to the aquatic animals; determining, by the one or more learned decision-making models using the received pre-processed sensor data, feeding instructions for the one or more aquatic animals; and outputting the feeding instructions from the one or more learned decision-making models.