## 4 Discussion  
In this study, it should be noted that the low reflectance of the I seedlings in the NIR spectrum was typical for diseased plants that demonstrated the damaged leaf's internal structure, which consequently causes water stress in the plants. Furthermore, changes in the NIR reflectance during a stress period were more evident than changes in the visible reflectance since NIR could penetrate deeper through the internal leaf structure than visible wavelengths [29]. This finding agreed with [46], where healthy citrus and asymptomatic huanglongbing infected leaves showed significant differences in the NIR spectrum. Undoubtedly, in a nutshell, healthy leaves reflect higher NIR reflectance than infected leaves even before the development of physical symptoms. Conversely, the U seedlings provide a slightly higher reflectance in the visible spectrum than the I seedlings. This pattern was contrary to the spectral signature of healthy plants examined by other researchers who agreed that healthy plants normally have lower reflectance than a diseased plant in the visible range, especially in green (520 to 560 nm) due to the higher level of chlorophyll in the leaves. However, in this study, the pattern shown was identical to the study carried out by [34], where the healthy seedlings developed higher reflectance than infected seedlings in the green wavelengths. As each plant has a specific spectral signature [47], this pattern may be a unique spectral signature for oil palm seedlings. According to [48], a higher number of input parameters in machine learning had resulted in higher accuracy models; however, this finding contrasted with linear discriminant, quadratic discriminant, and subspace kNN models, which produced higher accuracy at a lower number of bands, e.g., 9 bands. In addition, linear SVM has maintained a nearly consistent accuracy of \(\pm\)95% at 35, 18, 14 and 11 bands despite the reduced number of bands that reflect its insensitivity towards bands optimization. The differences in classification accuracy were due to different characteristics and sensitivities of specific classifiers towards the optimization of parameters [33]. For example, bagged trees only achieved the highest classification accuracy of 94.49% when 35 bands were used, whereas quadratic SVM achieved the highest classification accuracy when 11 bands were used. Additionally, it was noted that there were minor discrepancies in the percentage classification accuracy and F-score. This is because classification accuracy is only concerned with the total number of correct predictions (\(tp\) and \(tn\)), whereas F-scores balance precision and sensitivity by assigning equal weight to each. Accuracy alone is insufficient as a performance metric for issues involving unequal classification; for example, in this case, U is 279 and I is 360. The key reason for this is that the amount of data of I overwhelmed the amount of data of U, which means that even unskilled models can achieve high classification accuracy, depending on the severity of the class imbalance. However, the results showed that most SVM-based models achieved a higher F-score than the other models. The linear kernel provided the optimal hyperplane to separate the data points of U and I, followed by the fine and medium gaussian kernels. Additionally, kNN algorithms were also determined as the best classifier based on the classification accuracy obtained, with medium, cubic, and weighted kNN models outperforming other kNN models. Thus, it is revealed that medium distinctions between classes with 10 number of neighbors are more suitable, while one neighbor is too fine, and 100 neighbors are too coarse. On the other hand, the less accuracy from discriminant analysis indicates that the U and I data does not fit with the Gaussian distributions. Furthermore, both classes also do not fit well with the sigmoid function, as indicated by logistic regression results.