## 3 Results  
### Classification Model  
#### 3.1.1 Dataset 1: Single-Based NIR Reflectance  
As shown in Figure 2, bands at wavelength numbers 926 nm, 930 nm, 934 nm, 938 nm, and 942 nm were identified as the five significant bands due to a high separation gap between healthy and infected seedlings. Tables 3-6 tabulate the score performance for each of the SVM models developed using different combinations of identified significant single-based NIR reflectance. In terms of accuracy, as shown in Table 3, the performance of coarse Gaussian SVM had the highest average accuracy score which was 94.64%. All SVM models still gave good accuracy scores (above 80%) even when the number of wavelengths were reduced except for cubic SVM. The second highest average accuracy was obtained from linear SVM with 94.62%, this was then followed by fine  
For sensitivity, as tabulated in Table 4, the highest average score was achieved by linear SVM (93.10%), followed by fine Gaussian SVM (92.80%), coarse Gaussian SVM (92.10%), medium Gaussian SVM (92.08%), quadratic SVM (88.92%), and cubic SVM (45.26%). In general, all SVM models had excellent sensitivity scores, indicating that there were few false negative results, and thus fewer cases of disease were missed except for the cubic SVM which had the lowest sensitivity score. It failed to identify disease in the infected seedlings. As tabulated in Table 5, coarse Gaussian SVM had the highest average specificity score with 96.48%, followed by medium Gaussian SVM (96.36%), linear SVM (96.10%), fine Gaussian SVM (96.02%), quadratic SVM (82.36%), and cubic SVM (52.22%). Since Coarse Gaussian SVM had the highest average of specificity score, it was identified as the best model which could correctly identify oil palm seedlings without _G. boninense_ infection. In general, all SVM models were able to correctly identify oil palm seedlings without BSR disease, except for cubic SVM. The value of AUC indicates the performance of model separability for correctly identified infected and healthy seedlings. Based on the results tabulated in Table 6, all SVM models showed excellent performance with an average value of AUC from 0.91 to 0.96 except for cubic SVM (0.42). This meant that there was a high chance that most of the models were able to distinguish between classes which were the positive and negative classes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **5** & **4** & **3** & **2** & **1** & \\
**Wavelengths** & **(926 nm, 930 nm, & **(930 nm, 934 nm, & **(930 nm, 934 nm, & **(934 nm)** & **(934 nm)** & **Average** \\
**Kernel Type** & **934 nm, 938 nm, & **938 nm, and & **and 938 nm)** & **and 938 nm)** & **(934 nm)** & **Average** \\ \hline Linear & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 \\ Quadratic & 0.95 & 0.92 & 0.89 & 0.92 & 0.89 & 0.91 \\ Cubic & 0.75 & 0.30 & 0.33 & 0.22 & 0.52 & 0.42 \\ Fine Gaussian & 0.95 & 0.96 & 0.96 & 0.96 & 0.94 & 0.95 \\ Medium Gaussian & 0.96 & 0.95 & 0.95 & 0.96 & 0.96 & 0.96 \\ Coarse Gaussian & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of each SVM model for AUC in each reduction of the number of wavelengths. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **5** & **4** & **3** & **2** & **1** & \\
**Wavelengths** & **(926 nm, 930 nm, & **(930 nm, 934 nm, & **(930 nm, 934 nm, & **(930 nm, 934 nm, & **(934 nm)** & **(934 nm)** & **Average** \\
**Kernel Type**  
Table 7 shows the mean, standard deviation, and coefficient of variance (CV) for accuracy, sensitivity, and specificity of each SVM model calculated to identify dispersion of the data. The mean of accuracy, sensitivity, and specificity of each model were calculated by averaging these respective values from all the different datasets used. In general, all the models showed good performance with most of them achieving more than 90% mean accuracy, sensitivity, and specificity.