Research on a precise feeding method for fry in recirculating aquaculture systems

Fry feeding in recirculating aquaculture systems (RAS) has gained prominence following China’s ban on fishing in the Yangtze River. Although previous researches have focused on dynamic adjustments to adult fish feeding status, research on fry feeding has been subsequently neglected. To fill this research gap, a precise fry feeding method was developed, comprising four main components: a fry feeding status detection module, a feeding control module, a precise feed discharging module, and a variable feed distribution module. The detection module utilizes the improved FFD-YOLO network which incorporates GhostNet, BiFPN and CA attention to detect fry feeding status, and real-time feeding decisions were made accordingly. Numerical simulations using Python were conducted to calculate the optimal feed coverage ratio, and Fuzzy-PID control was employed to rapidly adjust the rotational speed of the spreading disc. The experiments demonstrate that the FFD-YOLO algorithm achieved a precision of 91.33 %, a recall rate of 74.15 %, and a mAP_0.5 of 85.06 %, with a detection speed of 75 frames per second (FPS). Feeding distribution coverage ratios of 40 % and 80 % were recommended based on simulation results. The experimental results demonstrated that when feeding based on clear images, the errors of discharge and distribution were less than 10.2 % and 12.6 %, respectively. In contrast, when feeding based on blurred images, the errors exceeded 18.4 % and 24.1 %, respectively. Control experiments demonstrated that the proposed method can promote the growth of fry. This study provides a significant reference for future research on automatic fry feeding in industrial RAS.