## 4 Results and Discussion
### Extended Experiments and Analysis  
In fact, the proposed AASNet model can not only play a good role in smart fishery applications but can also be transferred to other smart agriculture applications in non-underwater environments, which can well solve various tasks of image detection and segmentation. For further verification, our AASNet is again applied to the custom-built SDD dataset of intelligent agriculture, which is collected through the agricultural Internet of Things and camera equipment to monitor the growth status of strawberries and symptoms of leaf pests and diseases. The experimental results and visualizations for this additional dataset are provided below, demonstrating the migration generalization ability and application potential of the proposed AASNet across different scenarios.  
Table 6 presents the detection and segmentation performance of several segmentation models on a crop disease detection dataset. When using the GELAN+LCA backbone network, our proposed method achieves suboptimal Average Precision (APm) performance of 59.1% and 80.5% on two datasets, respectively. On the SDD dataset, it outperforms Mask R-CNN, TensorMask, CenterMask, BlendMask, MS R-CNN, Bmask R-CNN, and Cascade Mask by 7.5%, 8.4%, 5.7%, 3.8%, 6.8%, 5.7%, and 6.7%, respectively. On the PVDS dataset, it outperforms Mask R-CNN, TensorMask, CenterMask, BlendMask, MS R-CNN, Bmask R-CNN, and Cascade Mask by 10.2%, 10.7%, 6.1%, 5.7%, 9.2%, 7.4%, and 6.3%, respectively. These results demonstrate that our model can adapt to different application scenarios, achieving optimal disease segmentation results.  
Figure 13 presents the instance segmentation results of the SDD dataset across six different scenes. Regardless of whether the target contours are regular or irregular, our model consistently delivers precise segmentation and accurate class recognition, particularly in scenarios with simple backgrounds. Even in complex scenes with shadows, as shown in Figure 13,III-V, our model maintains the ability to accurately distinguish targets, demonstrating robust performance. Overall, our model exhibits exceptional performance in practical detection tasks, with more accurate segmentation results, a lower risk of missed detections, and the ability to consistently maintain high-quality segmentation in various environmental conditions. These advantages highlight the signifi  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Backbone** & **APm** & **APb** \\ \hline Mask R-CNN & Resnet50 & 51.6 & 52.8 \\ TensorMask & Resnet50 & 50.7 & 52.9 \\ CenterMask & Resnet50 & 53.4 & 56.7 \\ BlendMask & Resnet50 & 55.3 & 57.3 \\ MS R-CNN & Resnet50 & 52.3 & 52.9 \\ Bmask R-CNN & Resnet50 & 53.4 & 55.8 \\ Cascade Mask & Swint & 52.4 & 53.0 \\ AASNet (ours) & GELAN+LCA & 59.1 & 62.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Segmentation results on SDD dataset (APm: mAP of mask; APb: mAP of bbox).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Global Mean** & **mAP** & **AP50** & **AP75** \\ \hline  & & 30.9 & 48.4 & 34.3 \\ ✓ & & 31.2 & 48.7 & 34.6 \\  & ✓ & 31.4 & 49.2 & 35.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of DAFL on loss functions. The results show that the introduction of label smoothing and global mean achieves the best performance, with a mAP improvement of 0.8 points, the ✓ denotes the use of this method.  
of our model in crop disease detection within agricultural environments, validating its effectiveness in real-world scenarios.