Computer vision based approaches for fish monitoring systems: a comprehensive study

Abstract Fish monitoring has become increasingly popular due to its growing real-world applications and recent advancements in intelligent technologies such as AI, Computer Vision, and Robotics. The primary objective of this article is to review benchmark datasets used in fish monitoring while introducing a novel framework that categorizes fish monitoring applications into four main domains: Fish Detection and Recognition (FDR), Fish Biomass Estimation (FBE), Fish Behavior Classification (FBC), and Fish Health Analysis (FHA). Additionally, this study proposes dedicated workflows for each domain, marking the first comprehensive effort to establish such a structured approach in this field. The detection and recognition of fish involve identifying fish and fish species. Estimating fish biomass focuses on counting fish and measuring their size and weight. Fish Behavior Classification tracks and analyzes movement and extracts behavioral patterns. Finally, health analysis assesses the general health of the fish. The methodologies and techniques are analyzed separately within each domain, providing a detailed examination of their specific applications and contributions to fish monitoring. These innovations enable fish species classification, fish freshness evaluation, fish counting, and body length measurement for biomass estimation. The study concludes by reviewing the development of key datasets and techniques over time, identifying existing gaps and limitations in current frameworks, and proposing future research directions in fish monitoring applications.