(Retracted) Dynamic scene images-assisted intelligent control method for industrialized feeding through deep vision learning

Abstract

In a recirculating aquaculture system (RAS), feeding is an important factor affecting the growth of breeding objects. The traditional feeding methods relied on manual experience, which resulted in high labor costs and bait waste. To deal with these challenges, this paper proposes a dynamic scene images-assisted intelligent control method for industrial feeding through deep vision learning. First, a feeding video is processed according to the interframe difference method to obtain the image of the feeding state of the fish. Then, a modified VGG16 model is developed to determine the feeding state of the fish, transform it into a binary classification problem, and calculate the feeding frequency of the fish. After that, residual bait detection is deployed by adapting the YOLOv5 model. The results of the feeding frequency and the residual bait detection are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Experimental tests on real-world scene images showed that the accuracy of identifying the feeding state by the modified VGG16 model reaches 92.4%. Through the verification of the medium-size RAS, compared with the traditional feeding method, the intelligent feeding method significantly saves manpower and reduce 15% of bait waste.