Recent Advances in Fish Biomass Estimation Based on Deep Learning

Abstract:

Fish biomass estimation is the core part of fine management of aquaculture, which is very important for accurate feeding, resource assessment and improvement of aquaculture benefits. The traditional manual estimation method has the inherent defects of low efficiency, contact operation and easy damage to the fish body. In large-scale scenarios such as high-density cages and recirculating aquaculture, this technical bottleneck is becoming more and more obvious. In recent years, deep learning has provided a breakthrough solution for automatic estimation of fish biomass with its powerful feature learning and complex pattern analysis capabilities. The research progress of deep learning in this field in the past five years was systematically reviewed. Focusing on the core technology of biomass estimation, the three dimensions of fish size measurement, fish counting and fish weight estimation were analyzed. Building upon this foundation, a comprehensive summary of the current challenges encountered in the practical application of deep learning techniques for fish biomass estimation was provided and perspectives on future research directions were offerred. The objective was to furnish a scientific basis for the wider adoption of deep learning methods in fish biomass estimation, thereby promoting the digitalization and intelligent advancement of modern aquaculture systems.