Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging

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