智能化农业装备学报（中英文）

鱼类行为学研究进展及其在智能养殖中的应用

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靳娇娇 ¹ , 陈南雄 ¹ , 鞠琦 ² , 吴子杰 ¹ , 刘永奎 ¹ , 李色东 ¹^,^*^,^*

智能化农业装备学报（中英文） | 2025,6(4): 73-83

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智能化农业装备学报（中英文） | 2025 , 6 (4) : 73 -83

鱼类行为学研究进展及其在智能养殖中的应用

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靳娇娇¹, 陈南雄¹, 鞠琦², 吴子杰¹, 刘永奎¹, 李色东¹^,^*^,^*

作者信息

^1.南方海洋科学与工程广东省实验室（湛江），广东湛江，524025

^2.广东海洋大学水产学院，广东湛江，524088

通讯作者:

^* 李色东，男，1963年生，广东湛江人，硕士，教授级高工；研究方向为水产育种、深远海养殖技术与标准化。E-mail：lisedong@163.com

作者简介:

靳娇娇，女，1998年生，河南新乡人，硕士；研究方向为鱼类行为学、深远海养殖技术与标准化。E-mail：1950018491@qq.com

Research progress of fish behavior and its application in intelligent aquaculture

Jiaojiao JIN¹, Nanxiong CHEN¹, Qi JU², Zijie WU¹, Yongkui LIU¹, Sedong LI¹

Affiliations

^1.Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524025, China

^2.Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China

Corresponding Author:

^* LI Sedong, E-mail: lisedong@163.com

JIN Jiaojiao, E-mail: 1950018491@qq.com

出版时间: 2025-11-15 doi: 10.12398/j.issn.2096-7217.2025.04.007

文章导航

摘要

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鱼类行为学作为动物行为学的重要分支，致力于揭示鱼类在自然及人工环境下的活动模式与行为机制。随着水产养殖业向集约化、智能化发展，深入研究鱼类行为特征及其变化规律，对于提升养殖效率、优化管理策略和保障鱼类福利具有重要意义。系统综述了鱼类摄食行为、游泳行为、应激行为和社会行为的行为特征、影响因素与当前研究进展，重点介绍了视频监控、图像识别、行为量化指标构建及深度学习等技术在鱼类行为智能识别与数据感知中的应用。研究表明，不同行为类型与鱼类健康、生长、环境适应能力密切相关，其动态变化可为精准投喂、环境调控、疾病预警等养殖管理提供决策依据。鱼类行为学研究正从传统描述性观察迈向智能化、数据化方向，未来将在深远海养殖、生态化养殖与养殖装备智能升级中发挥关键作用。通过构建行为驱动的智能管理系统，有望实现更高效、更可持续的水产养殖模式。

关键词

鱼类行为学 / 摄食行为 / 应激行为 / 社会行为 / 智能养殖

Abstract

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Fish behavior, as a vital sub-discipline of animal behavior, aims to uncover the activity patterns and behavioral mechanisms of fish under both natural and artificial conditions. With the intensification and intelligent transformation of aquaculture, in-depth research on fish behavioral characteristics and their dynamic changes is of great significance for improving farming efficiency, optimizing management strategies, and ensuring fish welfare. This paper systematically reviews the characteristics, influencing factors, and recent research progress of feeding, swimming, stress, and social behaviors in fish. It highlights the application and practice of technologies such as video monitoring, image recognition, behavioral quantification, and deep learning in intelligent behavior recognition and data perception. Current studies have shown that various behavior types are closely related to fish health, growth, and environmental adaptability. Their dynamic changes can serve as critical references for precise feeding, environmental regulation, and disease early warning in aquaculture management. Fish behavior research is evolving from traditional descriptive observation toward intelligent and data-driven approaches. In the future, it is expected to play a key role in deep-sea aquaculture, ecological farming, and the intelligent upgrading of aquaculture equipment. Building behavior-driven intelligent management systems will contribute to achieving more efficient and sustainable aquaculture models.

Key words

fish behavior / feeding behavior / stress behavior / social behavior / intelligent aquaculture

引用本文

靳娇娇, 陈南雄, 鞠琦, 吴子杰, 刘永奎, 李色东. 鱼类行为学研究进展及其在智能养殖中的应用. 智能化农业装备学报（中英文）, 2025 , 6 (4) : 73 -83 . DOI: 10.12398/j.issn.2096-7217.2025.04.007

Jiaojiao JIN, Nanxiong CHEN, Qi JU, Zijie WU, Yongkui LIU, Sedong LI. Research progress of fish behavior and its application in intelligent aquaculture[J]. Journal of Intelligent Agricultural Mechanization, 2025 , 6 (4) : 73 -83 . DOI: 10.12398/j.issn.2096-7217.2025.04.007

基金

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国家重点研发计划海洋农业与淡水渔业科技创新专项(2022YFD2400204)
湛江市“现代化海洋牧场产业人才振兴计划”专项资金(013Z24006)
南方海洋科学与工程广东省实验室（湛江）项目(011Z23002)

参考文献

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2025年第6卷第4期

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doi: 10.12398/j.issn.2096-7217.2025.04.007

接收时间：2025-06-26
首发时间：2026-11-05
出版时间：2025-11-15

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收稿日期：2025-06-26
修回日期：2025-09-20

基金

National Key R &D Program of Marine Agriculture and Freshwater Fishery Science and Technology Innovation Project(2022YFD2400204)

国家重点研发计划海洋农业与淡水渔业科技创新专项(2022YFD2400204)

Zhanjiang's "Modern Marine Ranching Industry Talent Revitalization Plan" Special Fund(013Z24006)

湛江市“现代化海洋牧场产业人才振兴计划”专项资金(013Z24006)

Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang)(011Z23002)

南方海洋科学与工程广东省实验室（湛江）项目(011Z23002)

作者信息

^1.南方海洋科学与工程广东省实验室（湛江），广东湛江，524025

^2.广东海洋大学水产学院，广东湛江，524088

通讯作者:

^* 李色东，男，1963年生，广东湛江人，硕士，教授级高工；研究方向为水产育种、深远海养殖技术与标准化。E-mail：lisedong@163.com

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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