Leaves reflectance was collected using a Unispec spectroradiometer (PP Systems, Amesbury, MA, USA) that covers from 310-1130 nm. However, only reflectance spectra from 450-1000 nm were included and smoothed using the Savitzky-Golay method prior to the development of the partial least square discriminant analysis (PLS-DA) classification model. The PLS-DA yielded a classification accuracy of 94% in classifying healthy trees and trees with different levels of _G. boninense_ infection. Next, [PERSON] et al. [29] carried out early detection of _G. boninense_ in 15 years old oil palm trees. Reflectance spectra were taken from from rnd number 17 of healthy (T0), mild (T1), medium (T2), and severely (T3) infected trees using an ASD field spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO, USA). The infectionswere confirmed using a polymerase chain reaction (PCR) test. The reflectance spectra were then smoothed and transformed into the first and second derivatives spectra using the Savitzky-Golay method. It can be observed that NIR reflectance was significantly reduced as the disease severity increased. The data were reduced using principal component analysis (PCA). [PERSON] classification model predicted the diseased trees with 96% accuracy using raw reflectance spectra, while kNN yielded 97% using the first and second derivative data. Meanwhile, [PERSON] et al. [30] collected the absorbance data of T0, T1, T2 and T3 infected oil palms using a Fourier transform infrared spectroscopy (FT-IR) spectrometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). The data were obtained from from number 17 and transformed into the first and second derivatives using the Savitzky-Golay method after baseline correction and normalization. The dimensionality of the data was reduced using PCA before classification models were developed based on the best principal components to classify the different levels of _G. bontinense_ infections. The result showed that the linear discriminant analysis (LDA) model showed the highest overall classification accuracy of 92% when using the raw absorbance dataset. In addition, [PERSON] et al. [31] acquired the reflectance spectra of from number 9 and 17 of 12 years old oil palm trees using GER 1500 handheld spectrometer (Geophysical and Environmental Research Corporation, Millbrook, NY, USA). The study focused on the early detection of BSR; thus, the reflectance spectra were obtained only from T0 and T1 infected trees. The artificial neural network (ANN) was applied to discriminate between the T0 and T1 using raw, first and second derivative spectra. The raw reflectance spectra between 550 and 556 nm produced 100% classification accuracy for T1, while the first derivative reflectance has yielded 100% accuracy for T1 and 83% accuracy for T0. Furthermore, [PERSON] et al. [32] utilized dielectric spectroscopy to obtain impedance, capacitance, dielectric constant and dissipation factor to detect _G. bontinense_ in oil palm plantation. The dielectric properties of T0, T1, T2 and T3 diseased trees were reduced using PCA and classified using LDA, quadratic discriminant analysis (QDA), kNN and naive Bayes. The QDA attained the highest accuracy of 81%, and impedance was the best parameter to assess severity levels of _G. bontinense_. Next, [PERSON] et al. [33] conducted a study to discriminate the healthiness levels of mature _G. bontinense_ infected oil palms using a FARO laser scanner (Faro Technologies Inc., Lake Mary, FL, USA). Five features were extracted from the data, i.e., C200 (crown slice at 200 cm from the top), C850 (crown slice at 850 cm from the top), crown area (number of pixels inside the crown), from angle, and from number. Then, the data were reduced using PCA to increase its interpretability. Kernel naive Bayes, medium Gaussian support vector machine (SVM), and ensemble subspace discriminant achieved the highest accuracies when using PC1 and PC2, PC1 and PC3, and PC1, PC2, and PC3. The best model for the classification was kernel naive Bayes with 85% accuracy and a kappa value of 0.80.