# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 3 Results
### Experimental Results and Analysis
#### 3.4.3 Comparison of Experimental Results between Different Network Models  
To visually validate the model's accuracy in potato disease spot recognition, Table 1 compares the recognition accuracy results of four different network models (UNet, CENe, SegNet, and FCN) with the proposed network. As shown in Table 1, the improved model achieved good accuracy in identifying potato early blight, anthracose, and late blight diseases. The accuracy of the improved model on the potato dataset was higher than that of the other three networks, indicating that the improved network model had good generalization capabilities for potato disease datasets. Figure 10: Segmentation Loss of Different Network Architectures. Figure 9: Segmentation Loss of Two Datasets. In the same cycle, a comparison was conducted among the improved RS-UNet, UNet, SegNet, CENet, and FCN network models regarding potato disease spot segmentation effectiveness, as shown in Table 2. The improved RS-UNet outperformed other network models regarding the Dice coefficient, IoU, and training time. Notably, the SegNet segmentation time was twice that of the improved UNet, and the increased segmentation time did not lead to higher IoU or Dice values. The Dice value of RS-UNet (88.86%) was 11.22% higher than that of SegNet (77.64%). The time required for UNet segmentation was 0.84 h faster than the improved UNet segmentation. However, the segmentation results exhibited a lower Dice value of 6.57% and a lower IoU value of 8.96% for UNet. Comparatively, the time required for FCN segmentation was 2.19 h slower than that of the improved RS-UNet segmentation. The segmentation results of FCN showed a lower Dice value of 2.82% and a lower IoU value of 3.84%. The improved U-Net network model demonstrated significant advantages regarding the Dice coefficient, IoU, and accuracy. To visually compare the differences in segmentation results of different networks, four randomly selected images of potato leaf diseases were visualized, as shown in Figure 11, with results from various network models. In the images, black represents the background, while white represents the segmented lesions. The first column displays images of potato leaf diseases, the second column showcases manually annotated disease spot images in red, and each subsequent column illustrates the white regions, which represent the segmentation results produced by different network models. From Figure 11, it is evident that different diseases exhibited distinct colors, shapes, and noticeable differences. In the case of potato leaf disease spots, both SegNet and UNet network models could segment the approximate areas of the lesions. However, due to the small size of some potato leaf lesions, there were inaccuracies and missed segments in some areas. FCN and CENet showed significant improvements in the segmentation results, with their potato disease spot segmentation results being closer to the labels. However, CENet exhibited errors in the segmentation of edge areas and other lesion regions, while the FCN network model had less clear edges. For the improved RS-UNet network, some lesions closely matched the labeled areas, and even tiny lesions within the diseased leaves could be accurately extracted. This demonstrates that the improved RS-UNet could accurately segment disease leaf images, including lesion areas and their edges. This advantage could be attributed to the use of a deep residual network for feature extraction during re-segmentation, along with the incorporation of the SE attention mechanism on top of the ResNet, which highlights the disease spot's feature information while suppressing irrelevant features. Therefore, the improved network model achieved higher segmentation efficiency. In summary, compared with other networks, RS-UNet was more suitable as a model for potato leaf disease segmentation.