## 4 Results and Discussion  
### Data Augmentation Experiment  
The influence of multiple factors and comprehensive impact on the results of the data augmentation model in SFA-ViT are presented in Table 2. The original models in the experiments were not augmented with data. The models for each factor were expanded by nine times the number of images relative to the original training set using the corresponding factor augmentation method. The complete model, on the other hand, was augmented with a nine-fold data augmentation in the ratio of 1:1:1 for each factor.  
The experimental results indicate that the ViT model achieves the best performance results relative to the other base models, which may be attributed to the self-attention mechanism of ViT, inherently more adept at capturing global features, enabling it to make superior classification decisions on the original data.  
In comparison to the scenario without augmentation, the models augmented with environmental, growth, and filming factors have seen an increase in accuracy across both datasets. Taking the experimental results of the ViT model on the IP102 dataset as an example, the classification accuracy improved by 1.28% after environmental factor augmentation, by 0.94% after growth, and by 0.9% after shooting factor augmentation. This indicates that the three factors in the model have, to different degrees, introduced a wider range of conditions, which has reduced the risk of the model becoming overly specialized to particular circumstances. Within the IP102 dataset, the augmentation of environmental factors produced the most favorable outcomes, which is likely because the dataset includes a substantial number of field images where environmental factor augmentation can improve the model's judgment of background disturbances. On the other hand, in the Plant Village dataset, the augmentation of growth, rather than other factors, surpassed the others in effectiveness. This could be due to the dataset's images all being laboratory-shot with a consistent background, thus making the enhancement of the relationship between disease severity levels more effective.  
Furthermore, the most notable enhancement in model performance was achieved through the application of complete augmentation, suggesting that the combined impact of multiple factors aids the model in acquiring a wider array of feature information, thus improving its generalization and robustness. On the IP102 dataset, the classification accuracy of the VGG model improved by 1.67%, Resnet50 by 1.54%, and ViT by 1.71%. The outcomes on the Plant Village dataset also experienced improvements to varying  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model 1**} & \multicolumn{3}{c}{**IP102**} & \multicolumn{3}{c}{**Plant Village**} \\ \cline{2-7}  & **VGG** & **Resnet50** & **ViT** & **VGG** & **Resnet50** & **ViT** \\ \hline O & 63.98 & 65.75 & 67.25 & 98.99 & 99.38 & 99.63 \\ E & 65.01 & 66.87 & 68.53 & 99.25 & 99.46 & 99.68 \\ G & 64.87 & 66.57 & 68.19 & 99.42 & 99.52 & 99.76 \\ F & 64.69 & 66.62 & 68.15 & 99.26 & 99.50 & 99.70 \\ C &extents. These results indicate that the data augmentation model demonstrates favorable practical efficacy, effectively addressing the issues of data imbalance and sample scarcity in agricultural pest and disease classification and detection, thereby reducing the economic costs of research.