Denoising of Hyperspectral Signal from Drone for Ganoderma Disease Detection in Oil Palm

• Published in: International Journal of Geoinformatics
• Main Author: Izzuddin M.A.; Hamzah A.; Nisfariza M.N.; Idris A.S.; Nor Aizam A.; Sharul Aikal B.M.; Nordiana A.A.; Mohd Najib A.; Mohd Shukri I.
• Format: Article
• Language: English
• Published: Association for Geoinformation Technology 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163569422&doi=10.52939%2fijg.v19i5.2659&partnerID=40&md5=ebf980421f9635501b7358e9c2a1c861

Oil palm is an important crop that generates high income to Malaysia. However, the oil palm is susceptible to Ganoderma infection that reduces the productivity of the oil palm. Conventional ground-based disease detection is laborious and costly. Therefore, airborne remote sensing technology coupled with ground detection provides a more effective control of the disease. Airborne hyperspectral remote sensing utilizes narrow and contiguous bands to assist in detection of diseases in crops. Spectral responses recorded by the camera tend to suffer from interference and these noises could reduce the quality of the data. Therefore, this study presents the application of Savitzky-Golay and wavelet spectral denoising technique to improve the hyperspectral signatures for Ganoderma disease detection in oil palm. © 2023, Association for Geoinformation Technology. All rights reserved.