### Underwater Fish Recognition Technology  
As the demand for underwater environment exploration continues to grow, underwater visual tasks have become an increasingly significant research direction in computer vision. Substantial progress has been made across various aspects of underwater vision. For example, [PERSON] et al. [19] developed the EUVP dataset to advance underwater image enhancement and color correction, offering both paired and unpaired image samples. [PERSON] et al. [20] focused on underwater object detection using the WishFish dataset, which includes images of diverse fish and marine life. Additionally, [PERSON] et al. [21] established IRVLab, the first underwater semantic segmentation dataset, containing 1500 images. [PERSON] et al. [22] created the DeepFish dataset for fine-grained fish detection and instance segmentation, supporting species classification. While these datasets provide valuable resources for underwater vision, methods based on them still face challenges when dealing with lighting inconsistencies and objects at varying depths. In recent studies, the WaterMask method performs well on the UIIS dataset, effectively handling complex boundaries, but still struggles in instance dense underwater scenes, where the extreme class imbalance of underwater instances reduces segmentation accuracy. Similarly, the USIS-SAM method achieves strong segmentation performance on the USIS10K dataset, but its high model complexity limits real-time applications, particularly in large-scale underwater environments. These limitations highlight the need for further optimization in real-time processing and model efficiency.  
Recent advancements in underwater agriculture, particularly aquaculture, have led to significant progress in underwater image processing and biological detection. [PERSON] et al. [23] applied the YOLOX algorithm for underwater target detection in near-shore aquaculture, improving the detection speed and accuracy of objects such as farmed nets and fish. However, challenges remain in detecting small objects in complex underwater environments. [PERSON] et al. [24] proposed an enhanced soft attention-based method for underwater fish segmentation, integrating deep learning with attention mechanisms. This method performs well in complex aquaculture settings and can effectively handle dynamic underwater changes. Yet, real-time processing capabilities still require improvement. Overall, these studies provide valuable insights and technological solutions for intelligent monitoring and management in underwater agriculture, including image enhancement, target detection, and segmentation. However, challenges related to real-time monitoring and algorithm adaptability remain significant hurdles for future research [25].  
With the growing development of underwater agriculture, numerous technologies and methods have been proposed to improve the efficiency and sustainability of aquaculture. [PERSON] et al. [26] introduced a predictive optimization-based water quality control method, which enhances fish growth efficiency and yield through predictive modeling. Despite its promise, it still requires advancements in real-time data processing and large-scale farm equipment integration. [PERSON] et al. [27] reviewed deep learning frameworks for precision fish farming, emphasizing their potential for health monitoring, behavioral analysis, and early disease detection. However, challenges related to computational power and data processing remain, especially in large-scale farm applications. These studies highlight various technological avenues for smart fish farming, but key issues such as real-time data processing, system adaptability, and model deployment require further exploration [28].