boninense_ detection when the spectral reflectance was extracted from from number 1 of the seedlings. The percentage accuracy was slightly reduced to 94% when fronds number 1 and 2 were used. However, this method has the advantage of less operational time at the pre-processing stage and a reduction in the number of wavelengths to 9. A vegetation index (VI) is generated by combining reflectance from multiple spectral wavelengths into a single value using mathematical equations and transformations. Based on the work done by [39], the highest average value of accuracy for detecting _G. boninense_ infection of oil palm trees was found by using a simple ratio (SR) at 86.5%. The method could detect mild infection at 85.2% accuracy. Meanwhile, the normalized difference vegetation index (NDVI) performed very well in detecting palms with severe infection at 95.1% accuracy. For oil palm seedlings, SR developed using 610.5 nm and 738 nm was identified as the best band combination to discriminate mildly, moderately, and severely infected seedlings with a separability of 0.6441 calculated using the average silhouette width (ASW) [40]. Meanwhile, according to [41], the best index for BSR detection was developed using the band ratio of 477.5 nm and 495.5 nm. It obtained a high ASW value (0.9554) and the highest correlation with the chlorophyll in leaves (R\({}^{2}\) = 0.835). Researchers in [42] used the same bands in [40] to construct three newly formulated optimized indices for an airborne imaging spectrometer for applications (AISA) namely SR, NDVIa, and transformed vegetation index a (TVIa). The analysis showed that SR and NDVIa obtained 86% overall accuracy, and TVla got 84% overall accuracy, which was slightly higher than the overall accuracy obtained by [40]. Machine learning (ML) has been applied in various fields in agriculture including yield prediction, disease detection, weed detection, crop recognition, crop quality, water management, soil management, animal welfare, and livestock production [43]. It can be used to facilitate strategic plantation management by providing early information concerning plant health and it is proven to improve the accuracy of the classification for the severity level of disease infection from 80% [33] to 85% [34]. Various types of machine learning (ML) techniques have been used to detect _G. boninense_ infection both in the nursery and in plantation fields including support vector machine (SVM) [17; 22; 38; 44; 45; 46; 47], artificial neural network (ANN) [17; 25; 48; 49], k-nearest neighbour (kNN) [18; 29; 50], naive Bayes (NB) [18; 29; 34; 50; 51], maximum likelihood [30], random forest [49; 52], and quadratic discriminant analysis (QDA) [17]. Different types of datasets have been used--such as pixel value, odor, electrical impedance, laser scanning data, spectral reflectance, and synthetic aperture radar (SAR) data--with various levels of score found up to 100% accuracy. The difference in accuracy score was not only due to different types of datasets and situations, but also due to the different classifiers having different characteristics, where different types of classifiers are sensitive to various parameter optimization [34]. Based on the literature, it can be concluded that the oil palm seedlings infected with _G. boninense_ can be detected precisely and sensitively using the NIR spectrum of spectroscopy. With the use of the ML technique, hyperspectral data could provide 100% accuracy of detection [26]. However, this approach has limitations in that it uses many wavelengths which are not economical for hardware implementation in the future. Vegetation indices require a lesser number of wavelengths and have also shown promising results of detection. Various types of ML models have been used widely to detect _G. boninense_ infection using different types of datasets and they have obtained various levels of accuracy. Furthermore, ML has been proven to improve the percentage accuracy of detection. Therefore, this study was carried out to detect the oil palm seedlings infected by _G.