Journal of Aquaculture Research & Development

Abstrait

Survey of Fish Behavior Analysis by Computer Vision

Bingshan Niu, Guangyao Li, Fang Peng, Jing Wu, Long Zhang and Zhenbo Li

Assessment of the behavior or physiology of cultured fish has always been difficult due to the sampling time, differences between experimental and aquaculture conditions, and methodological bias inherent. Recent developments in computer vision technology, however, have opened possibilities to better observe fish behavior. Such technology allows for non-destructive, rapid, economic, consistent, and objective inspection tools, while providing evaluation techniques based on image analysis and processing in a wide variety of applications. “Fish”, in this study, refers to underwater vertebrate fish belonging to the Pisces class that inhabit almost all available aquatic environments. This study aims to assess current, worldwide fish behavior study methods that use cameras which utilize computer vision. The evolution of computer vision as applied to fish behavior is explored in this paper for all stages of production, from hatcheries to harvest. Computer vision technology is regarded as existing from 1973 to 2018, specifically the Elsevier database. Fish behavior and underwater habitats are explored at large, especially in aquaculture fishing. Based on the methods observed above, relevant viewpoints on the present situation are presented as well as suggestions for future research directions.