Abstract
As one of the main aquaculture species, fish plays an important role in the food supply of aquatic products. Fish behavior can indirectly reflect their living state, such as the difference in the behavior of fish under environmental stress, hunger, fighting, reproduction, and other conditions. Currently, the behavior of fish under various situations is still in the form of artificial observation, which has the disadvantages of strong subjectivity, time-consuming and labor-intensive, and cannot be quantitatively analyzed. Additionally, fluorescent labeling or sensor labeling is a commonly used method to observe fish behavior, but the implantation of labels often causes stress reactions to fish and interferes with the experimental results. Therefore, we established multiple fish behavior datasets, and proposed approaches of fish behavior analysis based on computer vision to detect reproductive behavior, quantitative statistics of ammonia nitrogen stress behavior, and classification of feeding intensity based on multi-modal information fusion. These approaches can not only be used for qualitative analysis of fish behavior, but also for quantitative analysis, and have the advantage of non-invasive. We believe that using computer vision-based methods to research fish behavior can further improve fish welfare, farming efficiency and yield, and empower aquaculture automation and intelligence.