## 4 Experimental Results and Analysis
### Test 3: Diseased vs. Healthy  
This section presents the findings of the classification all unhealthy and healthy images of mango plant leaves. Table 6 shows the classification results for all the diseases obtained after feature fusion based on CCA. This test was performed on all 135 images. The accuracy of 95.5% was attained using a cubic SVM, which is the highest among all the other competing classifiers. Moreover, 0.03, 0.93, 0.99, and 4.5 were the values of the sensitivity, specificity, AUC, and FNR, respectively. The confusion matrix of this test is also given in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**FNR (\%)**} & **Accuracy** \\ \cline{2-2} \cline{5-5}  & & & & & **(\%)** \\ \hline Linear discriminant & 0.13 & 0.69 & 0.78 & 27.4 & 72.6 \\ Linear SVM & 0.03 & 0.88 & 0.98 & 6.7 & 93.3 \\ Quadratic SVM & 0.14 & 0.76 & 0.88 & 25.2 & 74.8 \\ Cubic SVM & 0.03 & 0.93 & 0.99 & 4.5 & 95.5 \\ Fine KNN & 0.10 & 0.56 & 0.73 & 33.3 & 66.7 \\ Medium KNN & 0.06 & 0.83 & 0.88 & 11.2 & 88.8 \\ Cubic KNN & 0.03 & 0.89 & 0.98 & 7.5 & 92.5 \\ Weighted KNN & 0.11 & 0.79 & 0.86 & 20 & 80 \\ Subspace discriminant & 0.13 & 0.71 & 0.87 & 28.1 & 71.9 \\ Subspace KNN & 0.08 & 0.47 & 0.77 & 35.6 & 64.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Diseased vs. healthy mango leaves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**FNR (\%)**} & **Accuracy** \\ \cline{2-2} \cline{5-5}  & & & & **(\%)** \\ \hline Linear discriminant & 0.47 & 0.74 & 0.63 & 36.7 & 63.3 \\ Linear SVM & 0.05 & 0.95 & 0.95 & 4.5 & 95.5 \\ Quadratic SVM & 0.22 & 0.91 & 0.86 & 6.7 & 93.3 \\ Cubic SVM & 0.22 & 0.91 & 0.85 & 5.6 & 94.4 \\ Fine KNN & 0.24 & 0.67 & 0.71 & 28.9 & 71.1 \\ Medium KNN & 0.2 & 0.82 & 0.86 & 12.3 & 87.7 \\ Cubic KNN & 0.23 & 0.82 & 0.86 & 11.2 & 88.8 \\ Weighted KNN & 0.20 & 0.87 & 0.93 & 12.5 & 83.3 \\ Subspace discriminant & 0.29 & 0.91 & 0.82 & 18.9 & 81.1 \\ Subspace KNN & 0.24 & 0.6 & 0.75 & 32.2 & 67.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sooty mold vs. healthy mango leaves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**FNR (\%)**} & **Accuracy** \\ \cline{2-2} \cline{5-5}  & & & & **(\%)** \\ \hline Linear discriminant & 0.13 & 0.69 & 0.78 & 27.4 & 72.6 \\ Linear SVM & 0.03 & 0.88 & 0.98 & 6.7 & 93.3 \\ Quadratic SVM & 0.14 & 0.76 & 0.88 & 25.2 & 74.8 \\ Cubic SVM & 0.03 & 0.93 & 0.99 & 4.5 & 95.5 \\ Fine KNN & 0.10 & 0.56 & 0.73 & 33.3 & 66.7 \\ Medium KNN & 0.06 & 0.83 & 0.88 & 11.2 & 88.8 \\ Cubic KNN & 0.03 & 0.89 & 0.98 & 7.5 & 92.5 \\ Weighted KNN & 0.11 & 0.79 & 0.86 & 20 & 80 \\ Subspace discriminant & 0.13 & 0.71 & 0.87 & 28.1 & 71.9 \\ Subspace KNN & 0.08 & 0.47 & 0.77 & 35.6 & 64.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix of diseased vs. healthy mango leaves.