A Comprehensive Review of Fish Feeding Behavior Analysis in Aquaculture: Tasks, Techniques, and Applications
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910184020901888 |
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| author | Zhang, Shulong Li, Daoliang Zhao, Jiayin Yao, Mingyuan Chen, Yingyi Wang, Haihua |
| author_facet | Zhang, Shulong Li, Daoliang Zhao, Jiayin Yao, Mingyuan Chen, Yingyi Wang, Haihua |
| contents | Fish feeding behavior analysis is a key foundation for intelligent feeding and precision aquaculture management, and plays an important role in improving feed utilization efficiency, reducing production costs, and mitigating environmental burden. Existing reviews mainly focus on specific technical modalities or related applications in smart aquaculture, which makes it difficult to present the overall development of fish feeding behavior analysis in a comprehensive manner. To address these issues, this paper provides a thematic review of fish feeding behavior analysis in aquaculture, and systematically examines its task definition, technical support, and application status. First, from the task perspective, two core subtasks of fish feeding behavior analysis are clearly distinguished, and relevant behavioral characteristics and evaluation metrics are summarized. Second, from the technical perspective, the development trajectories of computer vision, acoustics, sensors, and multimodal fusion technologies are examined, and their advantages, limitations, and applicable scenarios are analyzed. On this basis, the application value of fish feeding behavior analysis in intelligent feeding and aquaculture management is further summarized. Finally, this paper discusses the challenges in robust perception under complex environments, generalization across fish species and farming scenarios, collaborative multimodal modeling and lightweight deployment, closed loop intelligent feeding, coordinated optimization of multiple tasks, and long-term production validation, and outlines future research directions. This review provides a reference for task standardization, technical selection, and engineering application in fish feeding behavior analysis, and offers insights into the development of smart aquaculture and sustainable aquaculture management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Review of Fish Feeding Behavior Analysis in Aquaculture: Tasks, Techniques, and Applications Zhang, Shulong Li, Daoliang Zhao, Jiayin Yao, Mingyuan Chen, Yingyi Wang, Haihua Computer Vision and Pattern Recognition Emerging Technologies I.4.9; I.2.10 Fish feeding behavior analysis is a key foundation for intelligent feeding and precision aquaculture management, and plays an important role in improving feed utilization efficiency, reducing production costs, and mitigating environmental burden. Existing reviews mainly focus on specific technical modalities or related applications in smart aquaculture, which makes it difficult to present the overall development of fish feeding behavior analysis in a comprehensive manner. To address these issues, this paper provides a thematic review of fish feeding behavior analysis in aquaculture, and systematically examines its task definition, technical support, and application status. First, from the task perspective, two core subtasks of fish feeding behavior analysis are clearly distinguished, and relevant behavioral characteristics and evaluation metrics are summarized. Second, from the technical perspective, the development trajectories of computer vision, acoustics, sensors, and multimodal fusion technologies are examined, and their advantages, limitations, and applicable scenarios are analyzed. On this basis, the application value of fish feeding behavior analysis in intelligent feeding and aquaculture management is further summarized. Finally, this paper discusses the challenges in robust perception under complex environments, generalization across fish species and farming scenarios, collaborative multimodal modeling and lightweight deployment, closed loop intelligent feeding, coordinated optimization of multiple tasks, and long-term production validation, and outlines future research directions. This review provides a reference for task standardization, technical selection, and engineering application in fish feeding behavior analysis, and offers insights into the development of smart aquaculture and sustainable aquaculture management. |
| title | A Comprehensive Review of Fish Feeding Behavior Analysis in Aquaculture: Tasks, Techniques, and Applications |
| topic | Computer Vision and Pattern Recognition Emerging Technologies I.4.9; I.2.10 |
| url | https://arxiv.org/abs/2502.15311 |