# Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
## 2 Proposed Methodology
### Results on Augmented Cucumber Dataset
Based on this analysis, the fine-tuned DenseNet201 is selected for the rest of the experiments. \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 & 94.36 & 94.46 & 94.6 & 5.64 & 94.41 & 964 \\ \hline Quadratic SVM & 94.26 & 94.36 & 94.4 & 5.44 & 94.61 & 387 \\ \hline MG SVM & 91.38 & 91.7 & 91.5 & 8.62 & 91.54 & 1169 \\ \hline Fine KNN & 86.6 & 86.96 & 86.6 & 13.40 & 86.78 & 681 \\ \hline Linear SVM & 91.12 & 91.48 & 91 & 8.88 & 91.30 & 657 \\ \hline ESD & 90.22 & 90.5 & 90.5 & 9.78 & 90.36 & 655 \\ \hline ES KNN & 91.86 & 91.6 & 91.7 & 8.14 & 91.73 & 600 \\ \hline WKNN & 79.66 & 81.44 & 78 & 20.34 & 80.54 & 748 \\ \hline Cosine KNN & 82.38 & 82.72 & 82.4 & 17.62 & 82.55 & 539 \\ \hline Medium KNN & 72.64 & 76.6 & 72.6 & 27.36 & 74.57 & 392 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification results of fine-tuned ResNet50 deep model after data augmentation. \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 & 97.7 & 97.76 & 97.7 & 2.30 & 97.7 & 608 \\ \hline Quadratic SVM & 97.24 & 97.32 & 97.2 & 2.76 & 97.2 & 574 \\ \hline MG SVM & 94.64 & 94.7 & 94.6 & 5.36 & 94.6 & 1086 \\ \hline Fine KNN & 94.42 & 94.44 & 94.4 & 5.58 & 94.4 & 1285 \\ \hline Linear SVM & 94.32 & 94.62 & 94.3 & 5.68 & 94.4 & 513 \\ \hline ESD & 95.82 & 96.68 & 95.8 & 4.18 & 96.2 & 2394 \\ \hline ES KNN & 96.36 & 96.26 & 96.3 & 3.64 & 96.3 & 4072 \\ \hline WKNN & 92.16 & 92.48 & 92.3 & 7.84 & 92.3 & 1501 \\ \hline Cosine KNN & 86.46 & 86.84 & 86.5 & 13.54 & 86.6 & 1404 \\ \hline Medium KNN & 83.44 & 84.44 & 83.5 & 16.56 & 83.93 & 1342 \\ \hline \end{tabular}
\end{table} 
Table 5: Classification results of fine-tuned ResNet101 deep model after data augmentation. The fine-tuned deep learning model is selected based on the better accuracy and applied proposed Entropy-ELM feature selection technique. The results are given in Table 7. This presents the best accuracy of 98% on Cubic SVM. The other computed measures are the recall rate which is 98.02, the precision rate at 97.98, and the F1-Score at 98%. The recall rate of Cubic SVM can be also verified through a confusion matrix, illustrated in Figure 5. Compared to the results given in Table 6, it is noted that the accuracy is a bit reduced but on the other side, a huge change occurred in the computation time. The time is also plotted in Figure 6 (FDenseNet201 and Dense Entropy-ELM). \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 & 98.44 & 98.5 & 98.4 & 1.56 & 98.47 & 355 \\ \hline Quadratic SVM & 97.4 & 97.46 & 97.4 & 2.60 & 97.43 & 330 \\ \hline MG SVM & 95.32 & 95.62 & 95.4 & 4.68 & 95.47 & 623 \\ \hline Fine KNN & 93.42 & 93.42 & 93.4 & 6.58 & 93.42 & 734 \\ \hline Linear SVM & 92.62 & 93.16 & 92.7 & 7.38 & 92.89 & 302 \\ \hline ESD & 96.62 & 96.6 & 96.6 & 3.38 & 96.61 & 1495 \\ \hline ES KNN & 94.1 & 94.08 & 94.1 & 5.90 & 94.09 & 1923 \\ \hline WKNN & 92.26 & 92.4 & 92.3 & 7.74 & 92.33 & 927 \\ \hline Cosine KNN & 85.44 & 85.94 & 85.3 & 14.56 & 85.69 & 807 \\ \hline Medium KNN & 85.9 & 86.9 & 85.8 & 14.10 & 86.40 & 764 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification results of fine-tuned Densenet201 deep model after data augmentation. Figure 4: Confusion matrix-based representation of Cubic SVM accuracy. Figure 5: Confusion matrix of Cubic SVM after employing feature selection technique.