Citation

Kurihara, Junichi and Koo, Voon Chet and Guey, Cheaw Wen and Lee, Yang Ping and Abidin, Haryati (2022) Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging. Remote Sensing, 14 (3). p. 799. ISSN 2072-4292

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Abstract

Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease.

Item Type:	Article
Uncontrolled Keywords:	Oil palm, plant disease, hyperspectral imaging, UAV, machine learning, sustainability
Subjects:	T Technology > TD Environmental technology. Sanitary engineering > TD194-195 Environmental effects of industries and plants
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Mar 2022 01:58
Last Modified:	03 Mar 2022 01:58
URII:	http://shdl.mmu.edu.my/id/eprint/10011

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