Dynamic feeding method for aquaculture fish using multi-task neural network

In recirculating aquaculture system (RAS), fish feeding is the most important part in production management, which is not only related to economic benefits, but also the key to ensure fish welfare and increase production. At present, in RAS, fish are basically fed either artificially or automatically (quantitatively supply feed at definite time), which can easily result in under-feeding or over-feeding of fish. Therefore, there is an urgent to develop an intelligent method that realizes appropriate feeding according to the actual demands of fish. This research attempts to explore a fish dynamic feeding method based on the multi-task network to meet the automatic adjustment of both the feeding intervals (the time intervals between feeding points in repeated feeding in a single-round) and feeding rates. The specific objectives of this study include two parts: 1) to construct a multi-task network to analyze the feeding activity of cultured fish and monitor the amount of uneaten feed pellets; 2) to design a feeding strategy based on information obtained from the multi-task network that realizes the dynamic adjustment of feeding intervals and the decision of feeding endpoint. The waste of feed pellets can be reduced by dynamically adjusting the feeding intervals, and the under-feeding and over-feeding of fish can be prevented by determining feeding endpoint. The results indicated that the accuracy of feeding activity classification by multi-task network reached 95.44%, and the mean absolute error (MAE) and mean square error (MSE) in uneaten feed pellet counting were 4.80 and 6.75, which indicate that the multi-task network can accurately monitor the fish feeding activity and the amount of uneaten feed pellets. Based on the two monitored information, combined with the feeding strategy, we dynamically adjusted the feeding intervals and determined the feeding endpoint, and then compared the feeding endpoints with manual judgment to verify the feasibility and accuracy of the dynamic feeding method based on the multi-task network. In summary, this research provides a more accurate and efficient solution for the intelligent and precise feeding of cultured fish, and provides the theoretical foundation for the development of intelligent feeding devices.