A Computer Vision-Based Intelligent Fish Feeding System Using Deep Learning Techniques for Aquaculture

Abstract

The decisions made regarding traditional fish feeding systems mainly depend on experience and simple time control. Most previous works have focused on image-based analysis of the leftover feed at the bottom of the pond to determine whether to continue or to stop feeding. However, the feasibility of such a method in an actual outdoor aquaculture pond is low. The main reason for this is that real outdoor aquaculture ponds have turbid water quality, small feed targets, interference from intense fish activity, overlapping images of fish and feed, etc. Therefore, image-based recognition is not easy to implement in actual outdoor aquaculture. To overcome this problem, this article proposes an automatic fish feeding system based on deep learning computer vision technology. In contrast to traditional computer-vision-based systems for recognizing fish feed underwater, the proposed system uses deep learning technology to recognize the size of the waves caused by fish eating feed to determine whether to continue or to stop feeding. Furthermore, several water quality sensors are adopted to assist in feeding decisions. As a result, the proposed system uses deep learning technology to recognize the size of the water waves caused by fish eating feed to determine whether to continue to cast feed or to stop feeding. Experimental results show that an accuracy of up to 93.2% can be achieved.