Early Detection of Ganoderma boninense in Oil Palm Seedlings Using Support Vector Machines

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.

Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine

“…However, the procedures entail stem collection, which may result in plant damage and eventual destruction. Other proposed methods used include an electronic nose (e-nose) [15,16], electrical impedance [17,18], tomography [19,20], thermal imaging [21,22], multispectral imaging [23,24], spectroscopy or hyperspectral data [25][26][27][28][29][30], and a terrestrial laser scanner (TLS) [31][32][33][34][35]. A comprehensive review of sensors used to detect BSR by [36] has found that each technique has different scores in terms of accuracies and limitations.…”

“…The infected seedlings demonstrate lower reflectance in the NIR range (750-950 nm) compared to healthy seedlings as a result of xylem destruction, which thus causes a reduction of chlorophyll pigments and a water deficiency. Based on the research work done by [26], the use of SVM with 18 and 35 NIR-hyperspectral wavelengths could provide 100% accuracy of G. boninense detection when the spectral reflectance was extracted from frond number 1 of the seedlings. The percentage accuracy was slightly reduced to 94% when fronds number 1 and 2 were used.…”

“…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.…”

Classification of Non-Infected and Infected with Basal Stem Rot Disease Using Thermal Images and Imbalanced Data Approach

“…Non-invasive remote sensing techniques, including ground-based, airborne, and space-borne remote sensing, have also been investigated to identify and map BSR-infected trees. Recent studies have demonstrated that hyperspectral and multispectral remote sensing methods can distinguish healthy and BSR-infected trees [14][15][16][17][18][19][20]. Terrestrial laser scanning (TLS) [21][22][23], synthetic aperture radar (SAR) data [24,25], intelligent electronic nose (E-Nose) systems [26,27], tomographic sensors [28,29], and microfocus X-ray fluorescence [30] also showed positive results in detecting BSR-infected trees.…”

The Fourier transform infrared spectroscopy from Diplazium esculentum and Rivina humilis analysis to reveals the existence of necessary components in oil palm plantations of Ganoderma boninense control

“…Thus, early detection methods are urgently required. Only a few methods have been reported to detect this disease before symptoms manifest as fruiting bodies, including enzyme-linked immunosorbent assay (ELISA) (Kayalvizhi and Antony, 2011;Utomo and Niepold, 2000;Kandan et al 2009;Siddiqui et al 2021), polymerase chain reaction (PCR) (Chong et al 2011;Midot et al 2019;Bahari et al 2018;Goh et al 2016), sequencing (Hayati and Basyuni, 2019), laser machine learning (ML) through laser beam scanning (Husin et al 2020), hyperspectral imagery visible near-infrared (VIS-NIR) (Azmi et al 2020;Ahmadi et al 2017;Isha et al 2019), scanning electron microscopy (Alexander et al 2017), and network detection through Ganoderma selective media (GSM) network (Rakib et al 2014;Darus and Seman, 1992;Penido et al 2013). Furthermore, weeds as markers of the organism's presence based on dominant species have also been reported (Saragih and Purba, 2018), with the nutrients in weed leaves compared to infected and uninfected oil palm (Saragih and Purba, 2019).…”