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Research progress of fish behavior and its application in intelligent aquaculture
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Jiaojiao JIN1, Nanxiong CHEN1, Qi JU2, Zijie WU1, Yongkui LIU1, Sedong LI1
Journal of Intelligent Agricultural Mechanization | 2025, 6(4) : 73 - 83
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Journal of Intelligent Agricultural Mechanization | 2025, 6(4): 73-83
Research progress of fish behavior and its application in intelligent aquaculture
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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

    • JIN Jiaojiao, E-mail:

About Author:

JIN Jiaojiao, E-mail:

Published: 2025-11-15 doi: 10.12398/j.issn.2096-7217.2025.04.007
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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.

fish behavior  /  feeding behavior  /  stress behavior  /  social behavior  /  intelligent aquaculture
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
Funding
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  • National Key R &D Program of Marine Agriculture and Freshwater Fishery Science and Technology Innovation Project(2022YFD2400204)
  • Zhanjiang's "Modern Marine Ranching Industry Talent Revitalization Plan" Special Fund(013Z24006)
  • Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang)(011Z23002)
Appendix
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Year 2025 volume 6 Issue 4
31
9
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Article Info
doi: 10.12398/j.issn.2096-7217.2025.04.007
  • Receive Date：2025-06-26
  • Online Date：2026-11-05
  • Published：2025-11-15
Article Data
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History
  • Received：2025-06-26
  • Revised：2025-09-20
Funding
National Key R &D Program of Marine Agriculture and Freshwater Fishery Science and Technology Innovation Project(2022YFD2400204)
Zhanjiang's "Modern Marine Ranching Industry Talent Revitalization Plan" Special Fund(013Z24006)
Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang)(011Z23002)
Affiliations
    1.Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524025, China
    2.Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China

Corresponding:

* LI Sedong, E-mail:
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表12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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