Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Review
<|reference_start|>Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Review: 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.<|reference_end|>
@article{cui2024fish,
    title={Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture:
  A Comprehensive Review},
    author={Meng Cui, Xubo Liu, Haohe Liu, Jinzheng Zhao, Daoliang Li, Wenwu Wang},
    journal={arXiv preprint arXiv:2406.17800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17800},
    primaryClass={q-bio.QM cs.SD eess.AS}
}