## 4 Results
FPN and PSP-Net were almost unable to accurately segment lesions, simply because a large amount of background raises the mIoU. U-Net, U-Net++, and LINK-Net performed well in segmenting five types of lesions, but were prone to misidentification of grey spot, often misidentifying the withered areas of leaves as grey spot. PAN and DeepLabV3+ both tended to misidentify grey spot as alternaria blotch. DeepLabV3 and MA-Net had better segmentation and recognition of leaf lesions. After comparison, it can be seen that when MA-Net is the main structure, the image segmentation effect for the five types of lesions was the best. Our improved model for MA-Net performed better, as shown in Figure 6.  
Next, we used MA-Net as the main structure and different feature extraction networks as encoders for comparative experiments. Dpn98 and timm-gernet_m could not distinguish between alternaria blotch and grey spot. Se_resnet101 and timm-regnety_006 had poor segmentation of grey spot and were prone to misidentifying it as alternaria blotch. The segmentation effect of timm-regnetx_006 on mosaic disease was average. Resnext101_32x8d, timm-regnetx_006, and densenet169 showed a tendency to misidentify the withered parts of leaves as grey spot. The segmentation results of mit_b2 and mit_b3 were better, but mit_b3 sometimes misidentified brown spot as grey spot. From the resulting image, it can be seen that using mit_b2 as the encoder had the best segmentation effect on the five types of lesions, as shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attention** & **Dice Loss** & **mIoU** & **Accuracy** & **Precision** & **Recall** & **Fscore** \\ \hline PAB & 0.00969 & 0.98123 & 0.99677 & 0.99031 & 0.99031 & 0.99031 \\ SENet & 0.00964 & 0.98131 & 0.99679 & 0.99037 & 0.99037 & 0.99037 \\ CBAM & 0.00968 & 0.98124 & 0.99677 & 0.99032 & 0.99032 & 0.99032 \\ ECANet & **0.00947** & **0.98159** & **0.99684** & **0.99053** & **0.99053** & **0.99053** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experiment of different attention modules based on MA-Net and mit_b2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Encoder** & **Dice Loss** & **mIoU** & **Accuracy** & **Precision** & **Recall** & **Fscore** \\ \hline resnet101 & 0.01048 & 0.97973 & 0.99651 & 0.98952 & 0.98952 & 0.98952 \\ dpn98 & 0.01229 & 0.97614 & 0.99591 & 0.98772 & 0.98772 & 0.98772 \\ se\_resnet101 & 0.01220 & 0.97642 & 0.99594 & 0.98781 & 0.98781 & 0.98781 \\ resnet101\_32x8d & 0.01118 & 0.97836 & 0.99628 & 0.98883 & 0.98882 & 0.98883 \\ timm-regnety\_006 & 0.01078 & 0.97915 & 0.99641 & 0.98923 & 0.98923 & 0.98922 \\ timm-regnety\_006 & 0.01186 & 0.97705 & 0.99605 & 0.98814 & 0.98814 & 0.98814 \\ densenet169 & 0.01019 & 0.98025 & 0.99661 & 0.98982 & 0.98982 & 0.98982 \\ timm-gernet\_m & 0.01251 & 0.97574 & 0.99583 & 0.98750 & 0.98750 & 0.98750 \\ mit\_b3 & 0.00982 & 0.98098 & 0.99673 & 0.99019 & 0.99018 & 0.99018 \\ mit\_b2 & **0.00969** & **0.98123** & **0.99677** & **0.99031** & **0.99031** & **0.99031** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Experiments on Different Encoders Based on MA-Net. Using MA-Net as the main structure and mit_b2 as the encoder, experiments were conducted using different attention modules. SENet, CBAM, and ECA showed good segmentation and recognition effects on the five types of lesions. However, SENet misidentified alternaria blotch and brown spot, while CBAM misidentified brown spot and grey spot. From the resulting images, it can be seen that using ECA as the attention module had the best segmentation effect on the five types of lesions, as shown in Figure 8.  
In deep learning, performance evaluation of models is crucial. However, the traditional single model evaluation method is often affected by the data set partition, and cannot give an accurate performance evaluation. To address this issue, cross-validation has been widely adopted as a more accurate and reliable evaluation method.