# Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
## 2 Proposed Methodology
### Results on Augmented Cucumber Dataset
\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 & 98.02 & 97.98 & 98.0 & 1.98 & 98.00 & 116 \\ \hline Quadratic SVM & 96.54 & 96.62 & 96.5 & 3.46 & 96.58 & 135 \\ \hline MG SVM & 84.2 & 81.4 & 84.0 & 15.80 & 82.78 & 197 \\ \hline Fine KNN & 94.82 & 94.84 & 94.8 & 5.18 & 94.83 & 217 \\ \hline Linear SVM & 82.82 & 83.14 & 82.8 & 17.18 & 82.98 & 133 \\ \hline ESD & 93.3 & 93.78 & 93.0 & 6.70 & 93.54 & 201 \\ \hline ES KNN & 93.16 & 92.2 & 93.0 & 6.84 & 92.68 & 405 \\ \hline WKNN & 94.22 & 94.28 & 94.2 & 5.78 & 94.25 & 384 \\ \hline Cosine KNN & 88.16 & 88.92 & 88.4 & 11.84 & 88.54 & 93 \\ \hline Medium KNN & 85.78 & 86.8 & 85.8 & 14.22 & 86.29 & 204 \\ \hline \end{tabular}
\end{table} 
Table 7: Classification results using proposed Entropy-ELM selection approach. After the selection of the best dense features, in the next step all fine-tuned deep model features are fused using the proposed parallel approach. The results of this experiment are given in Table 8. The best-noted accuracy in this table is 98.2% on Cubic SVM. The recall and precision rates are 97.92 and 98.12%, respectively. Figure 7 illustrated the confusion matrix that can be utilized for the verification of the recall rate. The time of each classifier is also noted and plotted in Figure 6 (Fusion Entropy-ELM). In comparison with the results of Tables 6 and 7, it is noted that the overall accuracy is improved but the time is more increased than in the Dense Entropy-ELM step. Finally, the features of Dense Entropy-ELM and Fusion Entropy-ELM are fused using the proposed parallel approach, and the results are given in Table 9. This table presents the best-obtained accuracy of 98.50% on Cubic SVM. The noted precision rate is 98.30, recall rate is 98.36 and F1-Score is 98.48%, respectively. The second best-noted accuracy is 97.5% on Quadratic SVM. The recall rate of Cubic SVM can be verified through a confusion matrix plotted in Figure 8. This figure shows the correct prediction rate of each class in the diagonal. Compared to the results of this experiment with all previous experiments, it is clearly noted that the accuracy is improved and computational time is significantly reduced. Figure 7: Confusion matrix of Cubic SVM after parallel fusion of all selected pre-trained deep features and Entropy-ELM selection. \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 & 98.36 & 98.3 & 98.5 & 1.74 & 98.48 & 111 \\ \hline Quadratic SVM & 98.1 & 97.5 & 97.5 & 1.90 & 97.80 & 117 \\ \hline MG SVM & 95.74 & 96.06 & 95.8 & 4.26 & 95.90 & 175 \\ \hline Fine KNN & 94.42 & 94.42 & 94.4 & 5.58 & 94.42 & 130 \\ \hline Linear SVM & 93.06 & 93.68 & 93.2 & 6.94 & 93.37 & 103 \\ \hline ESD & 96.36 & 96.42 & 96.4 & 3.64 & 96.39 & 196 \\ \hline ES KNN & 94.82 & 94.8 & 94.8 & 5.18 & 94.81 & 277 \\ \hline WKNN & 92.2 & 92.56 & 92.2 & 7.80 & 92.38 & 201 \\ \hline Cosine KNN & 85.48 & 85.78 & 85.4 & 14.52 & 85.63 & 142 \\ \hline Medium KNN & 85.58 & 84.36 & 85.5 & 14.42 & 84.97 & 104 \\ \hline \end{tabular}
\end{table} 
Table 9: Proposed framework classification results using augmented dataset.