## 3 Results
### Machine Learning Classification  
All 35 statistically significant bands tabulated in Table 3 were used as inputs for machine learning classification. The classification output in Table 4 shows that almost all models achieved more than 90% accuracy when using 35 bands. Due to the excellent classification, the number of bands was then reduced by 50%. Next, 18 bands were used as new input for the classification and resulted in 0.88% increases in the average of all accuracy, which means machine learning classification produced higher accuracy when using 18 bands compared to 35 bands. Then, the 18 bands were reduced by 50% to 9 bands. However, the 9 bands produced lower accuracies with an average of 92.36%, indicating decreases of 1.36% compared to 18 bands. Therefore, the 9 bands were subsequently increased by 50% to 14 bands. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classification Model**} & \multicolumn{4}{c}{**Classification Accuracy (\%)**} & \multicolumn{2}{c}{**No. of Attempt with the**} \\ \cline{2-6}  & \multicolumn{4}{c}{**Number of Significant Bands**} & \multicolumn{2}{c}{**Highest Accuracy**} \\ \cline{2-6}  & **35** & **18** & **9** & **14** & **11** \\ \hline Fine tree & 92.71 & 92.87 & 92.22 & 91.94 & 92.36 & – \\ Medium tree & 92.63 & 93.52 & 93.19 & 92.59 & 93.01 & – \\ Coarse tree & 94.58 & 94.81 & 93.84 & 93.56 & 94.49 & – \\ Linear discriminant & 90.46 & 92.92 & 93.05 & 92.41 & 92.73 & – \\ Quadratic discriminant & 76.93 & 88.65 & 89.39 & 88.56 & 91.20 & – \\ Logistic regression & 90.73 & 93.52 & 91.62 & 92.77 & 92.68 & – \\ Gaussian naïve Bayes & 94.58 & 94.58 & 92.12 & 93.52 & 92.45 & – \\ Kernel naive Bayes & 94.81 & 94.81 & 92.36 & 94.49 & 93.33 & – \\ Linear SVM & 94.81 & 95.23 \({}^{*}\) & 93.33 & 94.49 & 94.81 & 1 \\ Quadratic SVM & 93.66 & 93.24 & 92.82 & 93.89 & 94.72 & – \\ Cubic SVM & 94.26 & 91.62 & 78.21 & 88.79 & 93.56 & – \\ Fine Gaussian SVM & 94.81 & 95.23 \({}^{*}\) & 94.07 & 94.81 & 94.07 & 1 \\ Medium Gaussian SVM & 94.81 & 94.81 & 93.66 & 94.81 & 94.30 & – \\ Coarse Gaussian SVM & 94.81 & 94.81 & 94.21 & 93.89 & 93.89 & – \\ Fine kNN & 91.24 & 91.80 & 92.12 & 92.68 & 92.12 & – \\ Medium kNN & 94.81 & 95.23 \({}^{*}\) & 93.66 & 94.81 & 94.39 & 1 \\ Coarse kNN & 94.81 & 94.81 & 93.98 & 94.39 & 94.30 & – \\ Cosine kNN & 92.63 & 92.96 & 92.54 & 92.96 & 93.28 & – \\ Cubic kNN & 94.81 & 95.23 \({}^{*}\) & 93.65 & 94.81 & 94.39 & 1 \\ Weighted kNN & 94.81 & 95.23 \({}^{*}\) & 94.07 & 94.49 & 94.07 & 1 \\ Bagged trees & 94.49 & 94.26 & 94.07 & 94.17 & 94.17 & – \\ Subspace discriminant & 92.36 & 92.82 & 92.96 & 93.47 & 93.14 & – \\ Subspace kNN & 90.85 & 92.77 & 92.87 & 92.68 & 92.54 & – \\ \hline Average of all accuracy (\(m\)) & 92.84 & 93.73 & 92.35 & 93.23 & 93.48 & \\ \hline Differences (\(m-(m-1)\)) & & 0.88 & \(-1.38\) & 0.88 & 0.29 & \\ \hline No. of attempt with the & & & & & \\ highest accuracy & – & 5 & – & – & – \\ \hline \hline \end{tabular} Note: (\({}^{*}\)) shows the highest accuracy.  
\end{table} 
Table 3: The significant bands of leaves reflectance dataset identified by the separation value between U and I reflectance. The 14 bands have successfully improved the accuracy obtained by 9 bands with 0.88% increases in an average of all accuracy, the same as 35 bands. Since the previous run, the 9 bands have not been able to produce a good performance in machine learning classification; therefore, instead of reducing 14 bands to 7 bands, the process continued by increasing the 7 bands by 50%, i.e., to 11 bands. The results showed that 11 bands generated better classification accuracy than 14 bands, with 0.29% increases in the average of all accuracy. However, the optimization was ended at 11 bands because further reduction will cause a further decrease in classification accuracy, as happened in 9 bands. The list of all significant bands in the optimization process was tabulated in Table 5.  
Based on Table 4, it was found that the highest classification accuracy acquired was 95.23%, and only five models earned that accuracy in 18 bands.