Citation

Kwang, Chee Seng and Razak, Siti Fatimah Abdul and Yogarayan, Sumendra and Bin Muhammad, Abdul Mateen Montree and Ling, Choo Ai and Bakar, Shahrul Azman and Abidin, Haryati (2025) Ganoderma Detection in Oil Palm Plantations Using UAV Hyperspectral Imaging and AI. In: TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON), 27-30 October 2025, Kota Kinabalu, Malaysia.

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Abstract

The imperative for early and accurate detection of Ganoderma Boninense infections in oil palms is paramount to mitigating the devastating impact of basal stem rot. This disease poses a significant threat to palm oil production and the economic stability of affected regions. Conventional detection methods rely heavily on visual inspection or destructive laboratory analysis, which are time-consuming, labour-intensive, and cost-inefficient for large plantations. To address these limitations, this paper proposes a novel method for Ganoderma detection using frame-based hyperspectral imaging captured by an unmanned aerial vehicle (UAV). The approach incorporates feature-based band registration to correct spectral misalignments, followed by dimensionality reduction using principal component analysis (PCA). Support vector machines (SVMs) were evaluated for classification alongside a fine-tuned ResNet50 model. The results demonstrated that the TF- ResNet50 model achieved an accuracy of 84%, with a sensitivity of 74% for early infected trees and a specificity of 86% for healthy trees, underscoring the potential of UAV-based hyperspectral imaging for scalable, non-destructive disease monitoring in oil palm plantations.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Ganoderma Boninense, Basal Stem Rot, Hyperspectral
Subjects:	S Agriculture > SB Plant culture
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Apr 2026 03:23
Last Modified:	20 Apr 2026 03:23
URII:	http://shdl.mmu.edu.my/id/eprint/15767

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