## 3 Experiments
### Performance Comparison  
#### 3.4.1 Leaf Disease Identification  
Table 1 presents the CRC results for the proposed method and conventional state-of-the-art methods. In Table 1, all networks, including VGG, ResNet, FPN, AGN, SqueezeNet, and PVT, were initialized with the pretrained parameters with an ImageNet dataset, and TL was applied to each network with a new apple leaf dataset. Except for PVT, FPN, and AGN, library functions of MATLAB (2021a) were used to train the conventional classification models: VGG, ResNet, and SqueezeNet. The optimizer used was stochastic gradient descent (SGD) [45] with momentum. The epoch number was 30, and the batch size was set to 10. The learning rate was 0.001, and the momentum term was set to 0.9. The regularization term was \(\ell_{2}\)-norm, and its weight was set to 0.0001. For more detailed parameter settings, please refer to the author's source code. For the PVT, the open-source code provided by the author in [24] was used with the default setting. AGN and FPN were implemented using MATLAB's layer functions. Therefore, the same training parameters were used for the conventional and proposed models, except for PVT. In Figure 3, if the ROI-aware FES and ROI-aware feature fusion are excluded from the LSA-Net, the proposed architecture becomes identical to the conventional VGG network. Therefore, it should be checked whether CRC can be improved with additional ROI-aware FES and ROI-aware feature fusion. In Table 1, by comparing the proposed LSA-Net and VGG16, it is known that the additional use of the ROI-aware FES and feature fusion \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **CRC (\%)** \\ \hline VGG16 [13] & 90.19 \\ \hline ResNet50 [14] & 87.87 \\ \hline Attention Gated Network (AGN) [22] & 87.69 \\ \hline Feature Pyramid Network (FPN) [23] & 88.58 \\ \hline SqueezeNet [43] & 92.24 \\ \hline Pyramid Vision Transformer (PVT) [24] & 93.70 \\ \hline
**Proposed LSA-Net** & **96.07** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance evaluation for leaf disease identification. Figure 7: Leaf images (**first row**), ground truth segmentation maps (**second row**), predicted segmentation feature maps after training entire network (**last row**). increased the CRC value by approximately 6%. This reveals that the ROI-aware FES and feature fusion can teach the SAC-SubNet regarding which areas and features should have a decisive role in classifying apple leaf diseases. The ROI-aware FES and feature fusion served as a guide for a more accurate leaf disease identification. In addition, the proposed LSA-Net demonstrated the best performance among all the methods. Although VGG and ResNet can be adopted for the identification of leaf diseases, these models have limitations in improving the discriminative power because they do not model leaf spot attention mechanisms to extract discriminative and informative features from leaf images. In contrast, the SqueezeNet and PVT include attention mechanisms such as spatial and channel attention; thus, their CRC values are higher than those of VGG and ResNet. However, their performance is not better than that of the proposed LSA-Net because the SqueezeNet and PVT are self-attention vision models, which means that these networks do not utilize side information such as leaf segmentation and gradient maps. However, the proposed LSA-Net models the leaf spot attention mechanism based on the predicted leaf segmentation to extract spot features. This result confirms that the proposed leaf spot attention model is more effective than the self-attention vision model for leaf disease identification. The AGN is also a self-attention vision model; however, its performance is not better than that of SqueezeNet or PVT. Originally, the AGN was designed for medical image analysis, and it seems that the AGN is not suitable for leaf disease identification. In Table 1, the FPN adopted ResNet50 as the backbone. The main difference between FPN and ResNet50 is the pyramidal feature hierarchy, which has semantics from low to high levels.