Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision

Abstract In aquaculture, information on fish appetite is of great significance for guiding feeding and production practices. However, most fish appetite assessment methods are inefficient and subjective. To solve these problems, in this study, an automatic method for grading fish feeding intensity based on a convolutional neural network (CNN) and machine vision is proposed to evaluate fish appetite. The specific implementation process was as follows. First, images were collected during the feeding process, and a dataset was constructed and extended using rotation, scale, and translation (RST) augmentation techniques and noise-invariant data expansion. Then, a CNN was trained on the training dataset, and the fish appetite levels were graded using the trained CNN model. Finally, the performance of the method was evaluated and compared with other quantitative and qualitative feeding intensity assessment methods. The results show that the grading accuracy reached 90%; thus, the model can be used to detect and evaluate fish appetite to guide production practices.