系统梳理鱼饲料行为分析任务、技术与应用，助力智能养殖升级
A Comprehensive Review of Fish Feeding Behavior Analysis in Aquaculture: Tasks, Techniques, and Applications
- 区分两类核心行为分析任务，明确评价指标
- 对比视觉、声学、传感器等多模态技术优劣与适用场景
- 适合智能养殖研发与政策制定者参考
鱼饲料行为分析是智能喂养与精准水产管理的关键基础，在提升饲料利用率、降低生产成本和减轻环境负担方面具有重要作用。现有综述多聚焦特定技术或应用，难以全面呈现该领域的整体发展。本文对水产养殖中鱼饲料行为分析进行主题式综述，系统梳理其任务定义、技术支撑与应用现状。从任务角度，明确区分两类核心子任务，总结相关行为特征与评估指标；从技术角度，分析计算机视觉、声学、传感器及多模态融合技术的发展脉络，评估其优势、局限与适用场景。在此基础上，进一步总结其在智能喂养与水产管理中的应用价值。最后，探讨复杂环境下鲁棒感知、跨物种与场景泛化、多模态协同建模与轻量化部署、闭环智能喂养、多任务协同优化及长期生产验证等挑战，提出未来研究方向。本综述为任务标准化、技术选型与工程应用提供参考，推动智慧养殖与可持续水产管理发展。
原文摘要 · Abstract (English)
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.
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