# A Plant Disease Classification Algorithm Based on Attention MobileNet V2
## 3 Experimental Results and Analysis  
### Feature Extraction Algorithm  
The result of leaf extraction is shown in Figure 8. To evaluate the algorithm performance, the following index [40] is introduced:  
\[\left\{ \begin{array}{l}AOM=\frac{R_{S}\cap R_{S}}{R_{S}\cup R_{S}}\\ AVM=\frac{R_{S}-R_{S}}{R_{S}}\\ AUM=\frac{R_{S}-R_{S}}{R_{S}}\\ CM=\frac{1}{3}\{AOM+(1-AVM)+(1-AUM)\}\end{array} \right. \tag{29}\]  
where the area overlap measure (_AOM_), the area over segmentation measure (_AVM_), the area under segmentation measure (_AUM_), and the combination measure (_CM_) are used to evaluate the algorithm's performance. \(R_{S}\) represents the result of manual leaf labeling and serves as the gold standard. \(R_{S}\) denotes the result of the algorithmic labeling. Values of _AOM_ and _CM_ are proportional to the segmentation results, while _AVM_ and _AUM_ values are inversely proportional to the segmentation results. Figure 8: The effect of our object extraction. Figure 7: Scale feature fusion module. Typical tomato leaf diseases, as shown in Figure 9, were selected for the study. The results of the algorithm comparison are summarized in Table 2. The algorithm with a fixed threshold requires human-computer interaction for threshold selection, which exhibits low adaptability and presents different colors in the presence of the scab, resulting in the inability to accurately segment tomato leaves with a single threshold. On the other hand, OSTU [34] achieves threshold segmentation by calculating the gap between classes and setting the threshold, which is self-adaptive. However, due to the uniqueness of the threshold setting, the segmentation performance is somewhat limited. The GSO [41] algorithm searches for local optimal clustering, which realizes target recognition even under complex backgrounds, but it has some limitations in considering inter-class differences. The proposed algorithm fuses the OSTU algorithm with the attribute-weighted Bayes algorithm. This hybrid approach considers inter-class differences and intra-class similarities at a local level and exhibits a favorable effect on shadow suppression. To show the effect of the algorithm, an analysis is conducted on the effect of Gabor filtering. As shown in Figure 10, a variety of conditions of leaves are observed: Figure 10 shows a smooth leaf surface; Figure 10 displays leaves damaged as a whole; Figure 10 illustrates leaves with a large number of spots on the surface; and Figure 10 exhibits leaves with yellowing surfaces. After Gabor filtering, Leaf 1 demonstrates rich texture features at low dimensions and angles, appearing relatively rich and smooth as dimensions and angles increase. Leaf 2 exhibits a robust response in high dimensions and angles, with high pixel values in the displayed image. Leaf 3 presents more textures after Gabor filtering. Overall, the response of Leaf 4 is not strong, but the lesion area exhibits a robust response in high dimensions and angles. Through the above analysis, the Gabor-filtered image is input into the depth learning module for disease classification. To reflect the effect of feature extraction through Gabor filtering, the study takes the original image and Gabor-filtered image as input, uses a deep network for training, and compares their convergence performance, as shown in Figure 11. The results clearly demonstrate that the features extracted by Gabor are more representative, with the fast convergence speed of the algorithm, which brings it closer to the target function. This is because Gabor extracts multi-angle features, analyzes image characteristics, focuses on targets, and achieves efficient representation. Figure 9: Typical tomato diseases.