基于深度学习的鱼类养殖监测研究进展  

Research progress on fish farming monitoring based on deep learning technology

作　　者：张胜茂 李佳康[1,2] 唐峰华 吴祖立[2] 戴阳[2] 樊伟[2] ZHANG Shengmao;LI Jiakang;TANG Fenghua;WU Zuli;DAI Yang;FAN Wei(College of Information,Shanghai Ocean University,Shanghai 201306,China;Key Laboratory of Fisheries Remote Sensing,Ministry of Agriculture and Rural Affairs,East China Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences,Shanghai 200090,China)

机构地区：[1]上海海洋大学信息学院,上海201306 [2]中国水产科学研究院东海水产研究所,农业农村部渔业遥感重点试验室,上海200090

出　　处：《农业工程学报》2024年第5期1-13,共13页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61936014);崂山实验室专项经费(LSKJ202201804);中国水产科学研究院基本科研业务费(2020TD82)。

摘　　要：鱼类养殖是通过人工方式在水中养殖各种鱼类的经济活动。鱼类养殖可以在淡水、海水或者盐碱水环境中进行,通过各种监测技术和设备来培育和管理鱼的生长和繁殖。传统的鱼类养殖监测方法存在效率低和准确性差等问题。近年来,基于深度学习的视觉技术的发展为鱼类养殖监测提供了新的解决方案。该文阐述了基于深度学习的视觉技术在鱼类养殖监测中的应用,并从鱼体测量、鱼类计数、鱼类摄食、鱼类游泳行为和鱼病诊断5个方面分别对研究进展进行梳理。在此基础上总结了鱼类养殖监测在数据采集与传输、建立鱼类养殖监测数据集、超规模参数模型、终端监测设备边缘计算、数字孪生、智能监测业务化应用不足等问题和展望,旨在为深度学习在鱼类养殖监测中的推广应用提供科学参考。In recent years,with the rapid development and expansion of the global aquaculture industry,and the continuous enlargement of aquaculture farms,the industrialization,intelligence,and informatization of aquaculture have become a trend in the industry.China has become the largest producer of fisheries and aquaculture.Fish farming is an important component of aquaculture,and fish farming monitoring has become an important technology to enhance the efficiency,production,and management of fish farming.Fish farming monitoring can provide real-time and accurate data for farms,assisting farm managers in making decisions to improve efficiency and production.With the emergence of artificial intelligence technology in recent years,deep learning has rapidly developed and been widely applied in various fields such as image and audio recognition,natural language processing,robotics,bioinformatics,chemistry,and finance.The monitoring of fish farming focuses on the quantity,growth,behavior,and health status of fish.Using deep learning technology,we can quickly and accurately obtain information related to fish farming and enhance its efficiency and management.This paper presents a deep learning-based method for fish farming monitoring and reviews the literature progress in fish length measurement,fish counting,fish feeding,fish swimming behavior,and fish disease diagnosis.Although deep learning-based fish length measurement has achieved high accuracy in underwater environments,some errors still exist.The counting methods based on deep learning can be categorized into segmentation counting,detection counting,tracking counting,and density regression counting.Deep learning models based on video data have higher accuracy in recognizing fish feeding behavior than imagebased models.There have been many studies on fish tracking,but practical applications still face challenges such as fish feature extraction,the influence of fish size and obstructions,and occlusion issues.In fish disease diagnosis,it is necessary to establish standardize

关 键 词：深度学习 鱼类养殖 鱼体测量 鱼类计数 鱼类游泳行为 鱼类摄食 鱼病诊断 

分 类 号：S965[农业科学—水产养殖]