Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects both mature trees and seedlings. The current practice of detection still depends on manual inspection by a human expert every two weeks. This study aimed to detect early G. boninense infections using visible-near infrared (VIS-NIR) hyperspectral images where there are no BSR symptoms present. Twenty-eight samples of oil palm seedlings at five months old were used whereby 15 of them were inoculated with the G. boninense pathogen. Five months later, spectral reflectance oil palm leaflets taken from fronds 1 (F1) and 2 (F2) were obtained from the VIS-NIR hyperspectral images. The significant bands were identified based on the high separation between uninoculated (U) and inoculated (I) seedlings. The results indicate that the differences were evidently seen in the NIR spectrum. The bands were later used as input parameters for the development of Support Vector Machine (SVM) classification models, and these bands were optimized according to the classification accuracy achieved by the classifiers. It was observed that the U and I seedlings were excellently classified with 100% accuracy using 35 bands and 18 bands of F1. However, the combination of F1 and F2 (F12) gave better accuracy than F2 and almost similar to F1 for specific classifiers. This finding will provide an advantage when using aerial images where there is no need to separate F1 and F2 during the data pre-processing stage.

Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses on the early detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral data and a support vector machine (SVM). The study aims to use a small number of wavelengths by using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results of detection obtained from the vegetation indices developed using spectral reflectance taken from the same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two classes would be more suitable for G. boninense detection compared to the others, both for single-band reflectance and vegetation index datasets. A linear SVM which was developed using a single-band reflectance at 934 nm was identified as the best model of detection since it was not only economical, but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and area under the receiver operating characteristic curve (AUC) (0.95).

In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124 s when run on a personal computer with an Intel^® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry.

Basal stem rot (BSR) disease of oil palm (Elaeis guineensis Jacq.) spreads through the contact of the plant roots with Ganoderma boninense (G. boninense) Pat. inoculum in the soil. The soil properties can be altered by growing seedlings with or without G. boninense inoculum. In the early stage of infection, the symptoms are difficult to detect. Therefore, an understanding of the environmental soil conditions of the plant is crucial in order to indicate the presence of the fungus. This paper presents an analysis of the temporal changes of the soil properties associated with the G. boninense infection in oil palm seedlings. A total of 40 seedlings aged five months were used in the study, comprising 20 inoculated (infected seedlings: IS) and 20 control (healthy seedlings: HS) seedlings. The seedlings were grown in a greenhouse for six months (24 weeks) under a controlled environmental temperature and humidity. The data of the soil moisture content (MC in %), electrical conductivity (EC in µS/cm), and temperature (T in °C) for each seedling were collected daily using three MEC10 soil sensors every hour and then transferred to the ThingSpeak cloud using a 3G Internet connection. Based on the results, the mean MC and EC showed a decreasing trend, while the mean T showed an increasing trend in both HS and IS during the six-month monitoring period. The overall mean in both the monthly and weekly analysis of MC, EC, and T was higher in HS than IS. However, in the monthly analysis, a Student’s t-test at a 5% significance level showed that only the soil MC and EC were significantly different between HS and IS, while in the weekly analysis, HS was significantly different from IS in all parameters. This study suggests that soil MC, EC, and T can be used as indicators of the G. boninense infection, especially for the weekly data.