Fish Habitat Monitoring

Fish habitat monitoring aims to efficiently and accurately assess fish populations, behavior, and habitat conditions, primarily to support sustainable fisheries management and ecological research. Current research heavily utilizes computer vision and deep learning, employing object detection models (like YOLO and RCNN variants) and Gaussian process-based approaches for analyzing video and sensor data to track, count, and classify fish, even in challenging underwater environments. These advancements are crucial for overcoming limitations of traditional manual methods, enabling large-scale data analysis and informing conservation efforts and aquaculture optimization.