## 4 Discussion  
Previous studies have indicated that some specific regions/wavelengths can provide significantly superior discrimination power than others [56; 57; 58]. Changes in reflectance in the visible (VIS) regions (400-700 nm) are mainly determined by pigment concentration and photosynthesis efficiency, while near-infrared (NIR, 700-1100 nm) regions are strongly influenced by the leaf internal structure. The VIS region provides information based on color and texture, while the NIR region principally involves N-H, C-H, and O-H bonds. These bonds contain information concerning the chemical elements, structures, and states of the molecules. For early asymptomatic disease detection, the NIR region is the primary interest as NIR can obtain information on the interior tissues, while VIS can only obtain  
Figure 4: Graph of reflectance (%) against wavelength (nm) for healthy and infected seedlings. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Type**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} & \multicolumn{2}{c}{**Specificity (\%)**} & \multicolumn{2}{c}{**Area Under Curve (AUC)**} \\ \cline{2-9}  & **SR** & **NDVI** & **SR** & **NDVI** & **SR** & **NDVI** & **SR** & **NDVI** \\ \hline Linear SVM & 57.3 & 57.3 & 88.9 & 77.2 & 16.5 & 31.5 & 0.59 & 0.58 \\ Quadratic SVM & 48.8 & 52.4 & 48.9 & 51.9 & 48.7 & 53.0 & 0.49 & 0.51 \\ Cubic SVM & 47.6 & 47.3 & 33.6 & 35.3 & 65.6 & 62.7 & 0.48 & 0.50 \\ Fine Gaussian SVM & 53.5 & 54.6 & 81.7 & 82.2 & 17.2 & 19.0 & 0.53 & 0.54 \\ Medium Gaussian SVM & 56.5 & 56.7 & 93.1 & 95.3 & 9.3 & 6.8 & 0.54 & 0.56 \\ Coarse Gaussian SVM & 57.0 & 56.5 & 97.8 & 99.4 & 4.3 & 1.1 & 0.55 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of SVM models using SR and NDVI datasets. exterior information which is invisible at the early stage of infection [59]. This finding was not only in agreement with a previous study conducted for BSR disease at mature oil palm tree [50] but also to other diseases such as in identifying cotton canopy infected with _Verticillium_ wilt [60], apple scab disease caused by _Venturia inaequalis_[61], and corn kernels infected by fungi [62]. According to [63; 64], NIR region could detect the presence of functional groups of CH3, CN, N-H, C=N, and C-O-C in the _G. bontenense_ tissue sample. Several functional groups can also be identified such as CH3, N-H, and C=H [65]. Therefore, NIR is more precise and sensitive to detect the _G. bontenense_ infection compared to VIS. Spectral data in the NIR region represents significant differences between classes of samples compared to the VIS region [26; 50]. Both vegetation indices produced a single value calculated by transforming the observations from the red and NIR spectral bands. This single value was used to enhance the presence of vegetation features and thus helped to distinguish them from the other objects present in the image. The simple ratio (SR) is the simplest VI which is a faster way to distinguish green leaves from other objects. The spectral signature of healthy leaves reflects very low reflectance in the red region but a relatively higher reflectance in the NIR region. SR is sensitive to the chlorophyll content and was developed for estimating photosynthetic pigment concentrations in leaves [66]. Meanwhile, NDVI applications discovered that if chlorophyll production was reduced by biotics (e.g., insect damage, disease), there would be increased red reflection and a reduced NDVI value [67; 68]. NDVI measurement is significantly and usefully correlated with SPAD (Soil and Plant Analysis Development) values. The SPAD values express the relative sums of chlorophyll in crop leaves and have been established in several studies [69; 70; 71]. The studies mainly focused on chlorophyll measurement, which estimated the crop relative chlorophyll content by averaging all the values of one point SPAD measured more than once. In this research, it was found that the accuracy of detection using NDVI was low.