Digital aquaculture leverages advanced technologies and data-driven methods, providing substantial benefits over traditional aquaculture practices. This paper presents a comprehensive review of three interconnected digital aquaculture tasks, namely, fish tracking, counting, and behaviour analysis, using a novel and unified approach. Unlike previous reviews which focused on single modalities or individual tasks, we analyse vision-based (i.e. image- and video-based), acoustic-based, and biosensor-based methods across all three tasks. We examine their advantages, limitations, and applications, highlighting recent advancements and identifying critical cross-cutting research gaps. The review also includes emerging ideas such as applying multi-task learning and large language models to address various aspects of fish monitoring, an approach not previously explored in aquaculture literature. We identify the major obstacles hindering research progress in this field, including the scarcity of comprehensive fish datasets and the lack of unified evaluation standards. To overcome the current limitations, we explore the potential of using emerging technologies such as multimodal data fusion and deep learning to improve the accuracy, robustness, and efficiency of integrated fish monitoring systems. In addition, we provide a summary of existing datasets available for fish tracking, counting, and behaviour analysis. This holistic perspective offers a roadmap for future research, emphasizing the need for comprehensive datasets and evaluation standards to facilitate meaningful comparisons between technologies and to promote their practical implementations in real-world 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。本文采用新颖统一的方法，对三个相互关联的数字水产养殖任务——鱼类追踪、计数与行为分析——进行了全面综述。与以往聚焦单一模态或独立任务的综述不同，我们分析了基于视觉（即图像与视频）、声学及生物传感器的跨任务方法。我们审视了各类方法的优势、局限与应用，重点评述了最新进展并指出了关键共性研究缺口。本综述还涵盖了多任务学习与大语言模型等新兴思想在鱼类监测多方面的应用，这一方法在水产养殖文献中尚未被探索。我们识别了阻碍该领域研究进展的主要障碍，包括综合性鱼类数据集的稀缺与统一评估标准的缺失。为突破当前局限，我们探讨了利用多模态数据融合与深度学习等新兴技术提升集成化鱼类监测系统精度、鲁棒性与效率的潜力。此外，我们汇总了现有可用于鱼类追踪、计数与行为分析的数据集。这一整体性视角为未来研究提供了路线图，强调需要建立综合性数据集与评估标准，以促进技术间的有效比较并推动其在真实场景中的实际应用。