## 3 Results
### Classification Model
#### 3.1.1 Dataset 1: Single-Based NIR Reflectance
In terms of consistency of the model performance, according to [55], the CV was considered low if it was less than 10%, indicating that the value was consistent. The performance is considered medium when it ranges between 10% and 20%, indicating reasonable consistency. Meanwhile, a CV with a percentage of 20% to 30% is considered high, implying a lack of consistency. Finally, if the percentage of the CV is greater than 30%, it is considered extremely high, indicating a great lack of consistency. As tabulated in Table 7, it is shown that, in terms of accuracy, linear SVM had the lowest CV score with 0.17%, followed by fine Gaussian SVM (0.24%), medium Gaussian SVM (0.35%), coarse Gaussian SVM (0.37%), quadratic SVM (6.36%), and the highest CV was cubic SVM (26.34%). In respect of sensitivity, fine Gaussian SVM had the lowest CV score which was 2.40%, followed by linear SVM (2.72%), medium and coarse Gaussian SVM (3.49%), quadratic SVM (7.29%), and cubic SVM (41.97%). Regarding the CV of specificity, the lowest percentage of CV was achieved by linear SVM (2.09%), followed by fine Gaussian SVM (2.28), medium Gaussian SVM (2.70%), coarse Gaussian SVM (2.98%), quadratic SVM (13.94%), and cubic SVM (20.91%). Since linear, fine Gaussian, medium Gaussian, and coarse Gaussian have a low percentages of CV, these SVM models show very consistent results in detecting _G. bonitense_ infection in oil palm seedlings. For quadratic SVM, the CV score for accuracy and sensitivity was below 10%. In terms of specificity, the CV score between 10% and 20% was considered as reasonable consistency. Unfortunately, cubic SVM had a CV score above 25% which was considered extremely high, indicating a lack of consistency. Overall, the linear SVM model has the lowest score of CV for accuracy and specificity, and second lowest score of CV for sensitivity. The linear SVM also obtained a high score for accuracy, sensitivity, and specificity with scores above 90%. Since the linear SVM had the lowest CV score, it shows that the reduced number of wavelengths for the linear SVM did not affect the consistency of the model to detect _G. bonitense_ infection. Even when the number of wavelengths was reduced to 1, the linear SVM with an optimal kernel size of 0.0174 still had an excellent accuracy (94.80%), sensitivity (97.60%), specificity (92.50%), and AUC (0.95). Therefore, the linear SVM with a dataset taken from 934 nm was identified as the best model for detection. It can accurately identify both infected and healthy seedlings. The model also improved the classification accuracy obtained by [26] that used nine wavelengths for detection by 1.80%. While the linear SVM demonstrated the leading execution capability, the cubic SVM demonstrated in the other direction. Scatterplots of the datasets before and after the classification process are shown in Figure 3. Figure 3b shows the Linear SVM managed \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Specificity**} \\ \cline{2-10}
**Scheme 94** & **Mean, x** & **Standard** & **CV** & **Mean, x** & **Standard** & **CV** & **Mean, x** & **Standard** & **CV** \\  & **(\%)** & **Deviation, s** & **(\%)** & **(\%)** & **Deviation, s** & **(\%)** & **(\%)** & **Deviation, s** & **(\%)** \\ \hline Linear & 94.62 & 0.16 & 0.17 & 93.10 & 2.53 & 2.72 & 96.10 & 2.01 & 2.09 \\ Quadratic & 85.62 & 5.44 & 6.36 & 88.92 & 6.48 & 7.29 & 82.36 & 11.48 & 13.94 \\ Cubic & 48.74 & 12.83 & 26.34 & 45.26 & 18.99 & 41.97 & 52.22 & 10.92 & 20.91 \\ Fine & 94.60 & 0.22 & 0.24 & 92.80 & 2.23 & 2.40 & 96.02 & 2.19 & 2.28 \\ Gaussian & 94.56 & 0.33 & 0.35 & 92.08 & 3.22 & 3.49 & 96.36 & 2.60 & 2.70 \\ Coarse Gaussian & 94.64 & 0.35 & 0.37 & 92.10 & 3.21 & 3.49 & 96.48 & 2.87 & 2.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean (x) and standard deviation (s) and coefficients of variations (CV) for accuracy, sensitivity, and specificity of each SVM model.