## 5 Conclusions  
In smart fishery aquaculture, traditional practices face challenges, such as resource constraints and environmental pollution, as the demand for aquatic products increases. While smart fisheries enhance aquaculture efficiency through advanced technologies, underwater image segmentation still encounters difficulties due to variations in water quality, lighting, and data imbalance. As a result, underwater fish image segmentation has become a key technology for improving aquaculture efficiency, monitoring water quality, and increasing production. It enables accurate identification of fish locations and behaviors, aiding in underwater environment monitoring, fish health assessment, and disease warning. To address these challenges, this paper presents a new underwater instance segmentation model. AASNet introduces the Linear Correlation Attention (LCA) mechanism, which captures linear correlations between features to effectively handle color inconsistencies in underwater images, thereby improving segmentation accuracy. Additionally, the proposed Dynamic Adaptive Focus Loss (DAFL) dynamically adjusts the model's attention to difficult-to-classify samples, addressing the data imbalance issue and enhancing classification performance. Experiments on the UIIS and USIS10K datasets demonstrate that AASNet outperforms existing methods in both accuracy and inference speed. Specifically, AASNet achieves a mean Average Precision (mAP) of 31.7 on the UIIS dataset, surpassing USIS-SAM  
Figure 13: Disease segmentation results on the SDD dataset in the smart agriculture system. The descriptions of the subfigures are as follows: (**I**) Gray Mold. (**II**) Anthracnose Fruit Rot. (**III**) Leaf Spot. (**IV**) Blossom Blight. (**V**) Powdery Mildew on Fruit. (**VI**) Powdery Mildew on Leaves. by 2.3 points, and a mAP 4.6 points higher on the USIS10K dataset. Moreover, AASNet's inference time of 28.9 ms is significantly faster than the 180.5 ms of the WaterMask model. These results indicate that AASNet effectively addresses lighting changes and background interference in complex underwater environments, improving both segmentation accuracy and efficiency. Despite its success, AASNet still faces some challenges. Future research will focus on optimizing the model's real-time processing capabilities, improving segmentation performance for rare objects, enhancing its adaptability to extreme conditions, and exploring integration with water quality monitoring and automated feeding systems for more comprehensive intelligent aquaculture management [46; 47]. Conceptualization, X.J. ; methodology, J.K. and S.T. ; software, S.T. and X.J. ; validation, J.F. ; formal analysis, J.K. and S.T. ; investigation and data curation, J.F. ; writing--original draft preparation and visualization, J.K. and S.T. ; writing--review and editing, supervision, project administration, L.M. and X.J. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (Nos. 62473008, 62473009, and 62173007); Open Projects of the Institute of Systems Science, Beijing Wuzi University (No. BWUIS07); Project of ALL China Federation of Supply and Marketing Cooperatives (No. 202407); Beijing Nova Program (No. 20240484710); Beijing Scholars Program (No. 099); Beijing Municipal University Teacher Team Construction Support Plan (No. BPHR2020104); and Science, Technology, and Innovation Program of National Regional Medical Center of Taiyuan Bureau of Science and Technology (No. 202243). Not applicable. The dataset presented in this study is available at the following website link: [[https://github.com/[PERSON]/WaterMask](https://github.com/[PERSON]/WaterMask)]([https://github.com/[PERSON]/WaterMask](https://github.com/[PERSON]/WaterMask)), accessed on 1 June 2024. [[https://github.com/[PERSON]/USIS10K](https://github.com/[PERSON]/USIS10K)]([https://github.com/[PERSON]/USIS10K](https://github.com/[PERSON]/USIS10K)), accessed on 1 June 2024.