基于深度学习的鱼类生物量估算研究进展

Recent Advances in Fish Biomass Estimation Based on Deep Learning

摘要:

鱼类生物量估算是水产养殖精细化管理的核心环节，对精准投喂、资源评估及养殖效益提升至关重要。传统人工估测方法存在效率低、需接触式操作且易损伤鱼体等问题，在高密度网箱、循环水养殖等规模化场景中，这一技术瓶颈愈发明显。近年来，深度学习凭借强大的特征学习与复杂模式解析能力，为鱼类生物量自动化估算提供了突破性方案。本文系统梳理近5年深度学习在该领域研究进展，围绕生物量估算核心技术环节，从鱼体尺寸测量、鱼类计数、鱼体质量估计三大维度展开分析。在此基础上总结了当前深度学习技术在鱼类生物量估算实践中面临的问题并进行展望，旨在为深度学习在鱼类生物量估算中的推广应用提供科学参考，助力水产养殖向数字化、智能化升级。

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.