# Data Augmentation Method for Plant Leaf Disease Recognition
## 4 Results and Discussion
### Results  
In the result of the apple leaf in Figure 7, the black rot disease image generated through CycleGAN does not preserve the shape of the original leaf image well by performing the translation on some background areas. On the other hand, the results generated through the proposed method reflect more distinct disease characteristics for both Rust and Black rot classes. In addition, the generated results well preserve the shape of the original real leaf image, so the validity of the proposed method can be confirmed. Even in the grape leaf results in Figure 7, the diseased leaf images generated through CycleGAN do not preserve the shape of the input image in common and reflect the poor texture. The cause of these problems is that the generator performs training to deceive the discriminator by evenly and indiscriminately trying to transform the entire training image area. In contrast, the translation results of the proposed method show that explicitly regulating transformation only in the leaf region requiring transformation can create visually valid plant leaf data. The result of potato leaf transformation in Figure 7 also shows that the transformation through the proposed method produces much more stable and realistic results. The result of potato leaf transformation in Figure 7 also shows that the transformation through the proposed method produces much more stable and realistic results. In addition, the generator can select the features necessary for conversion and unnecessary features well by partially performing conversion only on the necessary areas in the original image and preserving the color of the original image in some areas. Quantitative evaluation of images generated by GANs is a very difficult problem. Initially, to evaluate the quality of the generated image, it was verified whether it follows the distribution of the training image well through Average Log-likelihood, but this is an indicator that is far from visual validity. Moreover, the generated images are high-dimensional data, making direct comparison difficult. To solve this problem, the proposed Frechet Inception Distance (FID) score calculates the Wasserstein-2 distance between two distributions through the average vector and covariance matrix of the feature vectors of the real image and the generated image. Therefore, the lower the better, and 0 corresponds to the highest score. Tables 10-12 are the results of evaluating the samples generated through these FID scores, and the samples generated through the proposed method as a whole are low. In  
Figure 7: Comparison of disease-infected leaf image results generated by the proposed method and CycleGAN. addition, the Black rot class in grape leaves showed the largest difference at 13.53 compared to the previous results. Table 13 shows the performance of the classification models according to each augmented technique for the apple leaf data. Overall, models trained with augmented data through the proposed method and CycleGAN show better results than models trained with imbalanced data. This indicates that augmented disease images can improve actual generalization performance. In the overall experiment, each model generally showed excellent F1 scores when trained over augmented datasets via the proposed method, and the best results using EfficientNet and our method achieved 0.9995 F1 scores. Tables 14 and 15 show the performance of the classification models according to each augmentation technique for the grape and potato leaf data. Unlike the experiments in Table 13, the two datasets used for the experiments in Tables 14 and 15 lack samples from health classes. Although the results of learning augmented datasets via CycleGAN in some \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **CycleGAN** & **Our** & **Diff** \\ \hline Black rot & 48.78 & 43.63 & 5.15 \\ Scab & 61.54 & 58.39 & 3.15 \\ Rust & 68.53 & 62.18 & 6.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of generated Apple leaf image FID scores.