## 4 Results and Discussion
### Classification Experiment  
The evaluation metrics of SFA-ViT in the classification task are given in Table 3. It is worth mentioning that on the IP102 dataset, the SFA-ViT achieved a 6.52 percentage point increase in accuracy and an 8.64 percentage point improvement in the F1 score compared to the ViT model in the absence of data augmentation. The performance improvement is also significant on the Plant Village dataset. This suggests that the integration of a multi-scale parallel network architecture with knowledge distillation substantially improves the performance of the ViT model on classification tasks, which is an indication that the classification model is effective. The improvement is likely due to the enhanced ability of the model to extract more effective target feature information. On the other hand, the combined application of the classification and data augmentation models in SFA-ViT further elevates the classification outcomes, indirectly demonstrating the efficacy of the data augmentation model.  
Figure 6 illustrates the variation in classification accuracy of the model on two datasets as a function of the number of training epochs. The classification accuracy at each scale corresponds to the results obtained from the three individual scales [CLS] as described in Section 2.3. The results across the three scales reveal distinct patterns. Specifically, as the image resolution increases, so does the classification accuracy. This correlation is likely attributed to the richer feature information that high-resolution images offer, which can be more effectively captured and utilized by the model. However, the SFA-ViT achieves the highest final classification accuracy, surpassing the results of each individual scale. This achievement is credited to the technique of knowledge transfer, which encourages the model to fully learn the features from images of various resolution. It also suggests that low-resolution images still contain characteristics not present in high-resolution images, further emphasizing the importance of multi-scale synthesis. Moreover, the data depicted in the figure also substantiate the crucial role of the data augmentation sub-model within the architecture, which enhances the model's generalization capabilities, allowing it to achieve superior performance in earlier epochs.  
Table 4 presents a comparative analysis of the performance of the proposed model with other models on the IP102 and Plant Village datasets. Notably, the SFA-ViT model achieved an accuracy of 74.70% and an F1 score of 68.27% on the IP102 dataset. Similar trends were observed on the Plant Village dataset. These results further validate the superior performance of the SFA-ViT in the pest and disease image classification task, which enables a broader range of agricultural producers to conduct early detection and prevention of pests and diseases, thereby propelling the development of agricultural intelligence and precision.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model 1** & **ACC (\%)** & **F1 (\%)** & **R (\%)** & **P (\%)** & **MCC** \\ \hline \multirow{3}{*}{IP102} & ViT (O) & 67.25 & 58.87 & 58.39 & 63.05 & 0.6638 \\  & SFA-ViT (O) & 73.77 & 67.51 & 69.19 & 66.49 & 0.7303 \\  & SFA-ViT (A) & 74.70 & 68.27 & 69.86 & 67.30 & 0.7399 \\ \hline \multirow{3}{*}{Plant Village} & ViT (O) & 99.63 & 99.38 & 99.36 & 99.42 & 0.9962 \\  & SFA-ViT (O) & 99.84 & 99.70 & 99.72 & 99.68 & 0.9983 \\ \cline{1-1}  & SFA-ViT (A) & 99.87 & 99.79 & 99.78 & 99.81 & 0.9986 \\ \hline \hline \end{tabular}  
* O means model is based on original dataset, A means model is based on augmented dataset.  
\end{table}
Table 3: Effectiveness analysis of classification model and data augmentation model in SFA-ViT.