Citation

Soh, Yi Yang and Lew, Sook Ling and Ooi, Shih Yin (2024) Oil palm diseases detection using computer vision techniques. In: 3rd International Conference on Computer, Information Technology, and Intelligent Computing (CITIC2023), 26–28 July 2023, Virtual Conference.

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Abstract

Unmanned aerial vehicles (UAVs), with sensors that can detect spectrums are useful tools for spotting diseases early in crops. This research zooms in on identifying Ganoderma disease in oil palm farms in Melaka and Sabah using UAV images. This research analyzed RGB pictures taken by the UAV mounted camera and multispectral data boosting their precision with georeferencing techniques. By employing the Normalized Green-Red Difference Index (NGRDI) and Normalized Difference Vegetation Index (NDVI) for image examination we effectively outlined the boundaries of oil palm trees. Future research should explore deep learning algorithms and further exploit NDVI to enhance disease detection. These developments are vital for improving strategies to detect and manage Ganoderma disease in oil palm plantations.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2024 06:18
Last Modified:	01 Aug 2024 06:18
URII:	http://shdl.mmu.edu.my/id/eprint/12721

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