## 4 Results  
The feature extraction network model selected for the experiment is the resnet101 encoder, with different networks selected for the main structure and 100 training iterations. U-Net, a popular model in medical image segmentation, had an mIoU of 0.97960 and a Dice Loss of 0.01055. The mIoU of U-Net++ was 0.97875 and the Dice Loss was 0.01103, indicating that the redesigned skip connections and multi-depth U-Net collaborative learning do not have significant advantages in leaf lesion segmentation and recognition. The results of Link-Net and DeepLabV3 were similar, with mIoU values of 0.97841 and 0.97839, respectively. The results of PAN and DeepLabV3+ were worse, with mIoU values of 0.97514 and 0.97549, respectively. The results of FPN and PSP-Net were the worst, with mIoU values of 0.94500 and 0.94467, respectively. After comparison, it can be concluded that the result with MA-Net as the main structure was the best, with an mIoU of 0.97973 and a Dice Loss of 0.01048. This could be because MA-Net introduced PAB and MFAB. Our module has further improved MA-Net by using MiT as the encoder and replacing PAB with ECA, resulting in better results, with an mIoU of 0.97973 and a Dice Loss of 0.00947, as shown in Table 2.  
We continued to use MA-Net as the structure, selecting different feature extraction networks as encoders. The experimental results of dpn98, se_resnet101, and timm-gernet_m were not good, with mIoU values of 0.97614, 0.97642, and 0.97574, respectively. The experimental results of resnext101_32x8d, timm-regnetx_006, and timm-regnety_006 were better, but did not exceed resnet101, with mIoU values of 0.97836, 0.97915, and 0.97705, respectively. The experimental results of densenet169, mit_b3, and mit_b2 all exceeded resnet101, with mIoU values of 0.98025, 0.98098, and 0.98123, respectively. After comparison, it can be concluded that using mit_b2 as the encoder produced the highest mIoU, as shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Dice Loss** & **mIoU** & **Accuracy** & **Precision** & **Recall** & **Fscore** \\ \hline U-Net & 0.01055 & 0.97960 & 0.99648 & 0.98945 & 0.98945 & 0.98945 \\ U-Net++ & 0.01103 & 0.97875 & 0.99633 & 0.98898 & 0.98898 & 0.98898 \\ Link-Net & 0.01121 & 0.97841 & 0.99626 & 0.98880 & 0.98879 & 0.98879 \\ FPN & 0.02922 & 0.94500 & 0.99027 & 0.97080 & 0.97080 & 0.97080 \\ PSP-Net & 0.02943 & 0.94467 & 0.99021 & 0.97064 & 0.97064 & 0.97064 \\ PAN & 0.01297 & 0.97514 & 0.99571 & 0.98713 & 0.98713 & 0.98713 \\ DeepLabV3 & 0.01122 & 0.97839 & 0.99627 & 0.98881 & 0.98881 & 0.98881 \\ DeepLabV3+ & 0.01274 & 0.97549 & 0.99576 & 0.98728 & 0.98727 & 0.98728 \\ MA-Net & 0.01048 & 0.97973 & 0.99651 & 0.98952 & 0.98952 & 0.98952 \\ Ours & **0.00947** & **0.98159** & **0.99684** & **0.99053** & **0.99053** & **0.99053** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Different Network Experiments Based on Resnet101 Encoder. Next, we used MA-Net as the structure and mit_b2 as the encoder, and replaced the PAB module of MA-Net. The structure of SENet is composed of SEblock blocks, and the core idea is to adaptively adjust the weights of each channel by learning the relationships between channels, thereby improving the network's expressive power. The CBAM module has two sub-modules: the Channel Attention Module and the Spatial Attention Module, which can perform attention operations in both spatial and channel dimensions to improve the model's attention to important features. The ECA module extracts important information from the feature map by weighting the channel dimensions of the input feature map with attention. Compared to the original version, the experimental results of all three attention modules improved to a certain extent. After comparison, it can be concluded that the mIoU generated while using ECANet as the attention mechanism was the highest, as shown in Table 4.  
Next, we used different network structures as the main structure and resnet101 as the encoder.