# Data Augmentation Method for Plant Leaf Disease Recognition
## 4 Results and Discussion
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Augmentation** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{3}{*}{ResNet 18} & Imbalance & 0.9768 & 0.9743 & 0.9755 \\ \cline{2-5}  & CycleGAN & 0.9791 & 0.9762 & 0.9774 \\ \cline{2-5}  & Proposed & 0.9793 & 0.9787 & 0.9790 \\ \hline \multirow{3}{*}{DenseNet 161} & Imbalance & 0.9879 & 0.9859 & 0.9867 \\ \cline{2-5}  & CycleGAN & 0.9912 & 0.9899 & 0.9905 \\ \cline{2-5}  & Proposed & 0.9903 & 0.9926 & 0.9914 \\ \hline \multirow{3}{*}{MobileNet v2} & Imbalance & 0.9855 & 0.9852 & 0.9853 \\ \cline{2-5}  & CycleGAN & 0.9868 & 0.9845 & 0.9856 \\ \cline{2-5}  & Proposed & 0.9882 & 0.9888 & 0.9885 \\ \hline \multirow{3}{*}{EfficientNet b0} & Imbalance & 0.9969 & 0.9968 & 0.9968 \\ \cline{2-5}  & CycleGAN & 0.9996 & 0.9985 & 0.9991 \\ \cline{1-1} \cline{2-5}  & Proposed & 0.9998 & 0.9992 & 0.9995 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of test results of Apple leaf classification models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **CycleGAN** & **Our** & **Diff** \\ \hline Black rot & 39.36 & 25.83 & 13.53 \\ Esca & 44.59 & 38.63 & 5.960 \\ Blight & 59.29 & 52.98 & 6.310 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of generated Grape leaf image FID scores. classification models were excellent, the best were those utilizing EfficientNet and our method, which achieved F1 scores of 0.9994 and 0.999, respectively. We have experimentally confirmed that training a model on an augmented dataset usually solves the overfitting problem and improves overall performance. In addition, we confirmed that EfficientNet showed excellent performance even on imbalanced datasets and achieved the best results in all experiments when used with the proposed method in this paper. Figure 8 shows the result of the two-dimensional visualization of feature vectors extracted from EfficientNet, which learned an augmented apple leaf dataset through the proposed augmentation method, through the t-distributed static neighbor embedding (t-SNE) technique. We find that the distribution of feature vectors extracted from the augmented 425 Rust samples and the real 75 Rust samples overlap, meaning that the generated image reflects the clear disease features of the real image class. Accordingly, the learned classification model may well extract features distinguished from other classes from the generated image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Augmentation** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{ResNet 18} & Imbalance & 0.9768 & 0.9743 & 0.9755 \\ \cline{2-5}  & DreplaceGAN & 0.9791 & 0.9762 & 0.9774 \\ \cline{2-5}  & CycleGAN & 0.9791 & 0.9762 & 0.9774 \\ \cline{2-5}  & Proposed & 0.9793 & 0.9787 & 0.9790 \\ \cline{2-5}  & DreplaceGAN & 0.9879 & 0.9859 & 0.9867 \\ \hline \multirow{4}{*}{DenseNet 161} & Imbalance & 0.9879 & 0.9859 & 0.9867 \\ \cline{2-5}  & CycleGAN & 0.9912 & 0.9899 & 0.9905 \\ \cline{2-5}  & CycleGAN & 0.9912 & 0.9899 & 0.9914 \\ \cline{2-5}  & DreplaceGAN & 0.9855 & 0.9852 & 0.9853 \\ \hline \multirow{4}{*}{MobileNet v2} & Imbalance & 0.9855 & 0.9852 & 0.9853 \\ \cline{2-5}  & CycleGAN & 0.9868 & 0.9845 & 0.9856 \\ \cline{2-5}  & Proposed & 0.9882 & 0.9888 & 0.9885 \\ \cline{2-5}  & Proposed & 0.9882 & 0.9888 & 0.9885 \\ \cline{2-5}  & DreplaceGAN & 0.9882 & 0.9888 & 0.9885 \\ \hline \multirow{4}{*}{EfficientNet b0} & Imbalance & 0.9882 & 0.9888 & 0.9885 \\ \cline{2-5}  & Imbalance & 0.9882 & 0.9888 & 0.9885 \\ \cline{1-1} \cline{2-5}  & CycleGAN & 0.9896 & 0.9885 & 0.9891 \\ \cline{1-1} \cline{2-5}  & Proposed & 0.9998 & 0.9992 & 0.9995 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of test results of Grape leaf classification models.