Abstract
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.
Highlights
The production of oil palm in Southeast Asia has been affected by a never-ending case of basal stem rot (BSR) disease
The accuracies attained by the Support Vector Machine (SVM) models showed that F12 was able to improve the accuracies of Frond 2 (F2), which verified that both fronds could be used to detect the G. boninense infection in oil palm
G. boninense infection can be detected at an early stage even though there are no physical symptoms of the disease by using SVM classifiers with a varying number of near infrared (NIR) bands
Summary
The production of oil palm in Southeast Asia has been affected by a never-ending case of basal stem rot (BSR) disease. The mature trees, but the oil palm seedlings are susceptible to the infection whereby the symptoms appear earlier and more severe [1]. BSR affects a plantation by reducing the number of standing trees as well as the weight of fresh fruit bunches (FFB) [2]. According to Subagio and Foster [3], FFB yield decreases by an average of 0.16 tons per hectare for every dead palm or equivalent to 35% when half of the planted trees have died. Based on the BSR incident rate, the total area affected in 2020 was estimated to be 443,440 hectares, equivalent to 65.6 million oil palms, which is worrying if preventative measures are not implemented [4]. The trees with an infection of less than 20% can still be treated [6]
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