To systematically review the application of computer vision in the field of aquaculture,this paper provides an in- depth analysis of its current implementations and challenges across various stages of the farming process,while also offering insights into future development trends. The aim is to provide theoretical support and technical references for the intelligent transformation and upgrading of aquaculture. This study focuses on the specific application pathways and performance of visual recognition algorithms-such as convolutional neural networks and the YOLO series in aquaculture. It also elaborates on the advantages and development potential of multi-modal fusion algorithms in integrating visual images,acoustic signals,and water quality monitoring data. Existing research demonstrates that computer vision technologies can significantly enhance the precision management and production efficiency of aquaculture operations. Multi-modal fusion algorithms,in particular,have shown outstanding performance in key tasks such as fish behavior recognition and quantitative analysis of feeding intensity. However,computer vision algorithms still face challenges in practical applications,including poor image quality caused by complex underwater imaging environments and increased recognition difficulty due to diverse fish behavior patterns. Looking ahead,with the optimization of deep learning algorithms,further application of multi -modal fusion technology,and cross - disciplinary integration with technologies such as the Internet of Things and aquaculture robotics,computer vision is expected to provide critical technical support for the efficient, precise, and sustainable development of aquaculture. This will play a significant role in ensuring global aquatic product supply and food security.