Digital aquaculture leverages advanced technologies and data-driven methods, providing substantial benefits over traditional aquaculture practices. Fish tracking, counting, and behaviour analysis are crucial components of digital aquaculture, which are essential for optimizing production efficiency, enhancing fish welfare, and improving resource management. Previous reviews have focused on single modalities, limiting their ability to address the diverse challenges encountered in these tasks comprehensively. This review provides a comprehensive analysis of the current state of aquaculture digital technologies, including vision-based, acoustic-based, and biosensor-based methods. We examine the advantages, limitations, and applications of these methods, highlighting recent advancements and identifying critical research gaps. The scarcity of comprehensive fish datasets and the lack of unified evaluation standards, which make it difficult to compare the performance of different technologies, are identified as major obstacles hindering progress in this field. To overcome current limitations and improve the accuracy, robustness, and efficiency of fish monitoring systems, we explore the potential of emerging technologies such as multimodal data fusion and deep learning. Additionally, we contribute to the field by providing a summary of existing datasets available for fish tracking, counting, and behaviour analysis. Future research directions are outlined, emphasizing the need for comprehensive datasets and evaluation standards to facilitate meaningful comparisons between technologies and promote their practical implementation in real-world aquaculture settings.

翻译：数字水产养殖利用先进技术和数据驱动方法，相比传统水产养殖实践具有显著优势。鱼类追踪、计数与行为分析是数字水产养殖的核心组成部分，对于优化生产效率、提升鱼类福利和改进资源管理至关重要。现有综述多聚焦单一技术模态，难以全面应对这些任务中遇到的多样化挑战。本综述对当前水产养殖数字技术（包括基于视觉、声学和生物传感器的方法）的发展现状进行了全面分析。我们系统考察了这些方法的优势、局限及应用场景，重点评述了最新进展并指出了关键研究缺口。研究发现，综合性鱼类数据集的稀缺与统一评估标准的缺失——这导致不同技术性能难以直接比较——是阻碍该领域发展的主要障碍。为突破当前局限并提升鱼类监测系统的准确性、鲁棒性与效率，我们探讨了多模态数据融合与深度学习等新兴技术的应用潜力。此外，我们通过系统梳理现有可用于鱼类追踪、计数与行为分析的数据集，为本领域研究提供了资源支持。最后展望了未来研究方向，强调建立综合性数据集与评估标准对于促进技术间的有效比较、推动其在真实水产养殖场景中实际应用的重要性。