Visual analysis of fish feeding intensity for smart feeding in aquaculture using deep learning

Abstract

This paper presents a novel deep-learning approach to analyze the fish feeding intensity based on the images of fish tanks during the fish feeding process. The grade of the fish feeding intensity is an important indicator of fish appetite. On the design of a smart feeding system in aquaculture, this information is of great significance for guiding feeding and optimizing the fish production. However, conventional fish appetite assessment methods are inefficient and subjective. To solve these problems, in this study, based on a space-time two-stream 3D CNN, a deep learning approach for grading fish feeding intensity is proposed to evaluate fish appetite. The flow of the approach is implemented as follows. First, a fixed RGB camera is setup to capture the videos from the fish tanks during the feeding processes. This also constructs a dataset for training the two-stream neural network, and the fish appetite levels are graded using the trained neural network model. Finally, the performance of the method is evaluated and compared with other CNN-based deep learning approaches. The results show that the grading accuracy reached 91.18%, which outperforms the compared CNN-based approaches. Thus, the model can be used to detect and evaluate fish appetite to guide production practices.