# Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
## 2 Proposed Methodology
### Results on Augmented Cucumber Dataset  
Experimental results of fine-tuned VGG16 pre-trained model after augmentation are given in Table 3. The best-obtained accuracy is 93.8% on Cubic SVM, whereas the recall rate and precision rates are 93.84 and 93.92%, respectively. The second best-obtained accuracy is 93.6%, which was accomplished on Quadratic SVM, whereas the recall rate and precision rates are 93.66 and 93.72%, correspondingly. The execution time of Linear SVM is better than the rest of the classifiers. The classification accuracy of fine-tuned ResNet50 on the augmented dataset is given in Table 4. This table presents the highest obtained accuracy on Cubic SVM of 94.6%, whereas the recall and precision rates are 94.36 and 94.46%, correspondingly. The second top accuracy is 94.4% obtained on Quadratic SVM, whereas the recall and precision rates are 94.26 and 94.36%, respectively. Similar to fine-tuned VGG16, the Quadratic SVM executed fast than the rest of the classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **F-VGG16** & **F-ResNet50** & **F-ResNet101** & **F-DenseNet201** \\ \hline Cubic SVM & 55.6 & 58.7 & 54.2 & 61.8 \\ \hline Quadratic SVM & 55.1 & 54.2 & 55.1 & 61.9 \\ \hline MG SVM & 56.9 & 48 & 50.7 & 60.4 \\ \hline Fine KNN & 50.7 & 48.9 & 49.8 & 52 \\ \hline Linear SVM & 51.6 & 53.3 & 51.1 & 58.4 \\ \hline ESD & 24.9 & 47.1 & 46.7 & 56.4 \\ \hline ES KNN & 50.7 & 55.1 & 53.8 & 51 \\ \hline WKNN & 51.6 & 47.6 & 40.9 & 49.5 \\ \hline Cosine KNN & 47.6 & 50.2 & 43.6 & 49 \\ \hline Medium KNN & 45.3 & 45.3 & 36.6 & 49.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results on originally selected cucumber dataset without data augmentation step for several fine-tuned deep learning models. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Classifier** & **Recall Rate (\%)** & **Precision Rate (\%)** & **Accuracy (\%)** & **FNR (\%)** & **F1 Score (\%)** & **Time (Sec)** \\ \hline Cubic SVM & 93.84 & 93.92 & 93.8 & 6.16 & 93.88 & 300 \\ \hline Quadratic SVM & 93.66 & 93.72 & 93.6 & 6.34 & 93.69 & 242 \\ \hline MG SVM & 91.78 & 92.04 & 91.8 & 8.22 & 91.91 & 463 \\ \hline Fine KNN & 88.54 & 88.54 & 88.5 & 11.46 & 88.54 & 562 \\ \hline Linear SVM & 90.56 & 90.88 & 90.6 & 9.44 & 90.72 & 188 \\ \hline ESD & 92.98 & 93 & 93 & 7.02 & 92.99 & 1526 \\ \hline ES KNN & 88.72 & 88.74 & 88.7 & 11.28 & 88.73 & 1550 \\ \hline WKNN & 86.22 & 86.46 & 86.2 & 13.78 & 86.34 & 1038 \\ \hline Cosine KNN & 80.8 & 81.36 & 80.8 & 19.20 & 81.08 & 648 \\ \hline Medium KNN & 79.44 & 80.9 & 79.5 & 20.56 & 80.16 & 589 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification results of fine-tuned VGG16 deep model after data augmentation. Experimental results of fine-tuned ResNet101 pre-trained model are given in Table 5. The best-obtained accuracy of 97.7% was accomplished on Cubic SVM. The recall and precision rates are 97.7 and 97.7%, correspondingly. The second best-obtained accuracy is 97.2% on Quadratic SVM. The recall and precision rates are 97.24 and 97.32%, correspondingly. In this experiment, the Linear SVM was executed fast than the rest of the selected classifiers. The classification results of fine-tuned Densenet201 pre-trained model are given in Table 6. In this table, the obtained best accuracy is 98.4% on Cubic SVM. Moreover, the recall and precision rates are 98.44 and 98.5%, correspondingly. Figure 4 illustrated the confusion matrix of Cubic SVM that was utilized for the verification of recall rate. The second best-obtained accuracy is 97.4%, which was accomplished on Quadratic SVM. The computation time of each classifier is also noted and the minimum time is 302 (sec) for LSVM. At the first step comparison among without augmented and augmented datasets, it is noted that the accuracy obtained on the augmented dataset is significantly better. In the second step comparison, it is noted that the fine-tuned DenseNet201 model achieved better results than VGG16, ResNet50, and ResNet101.