Ganoderma basal stem rot (BSR) disease infection in oil palm causes significant yield loss to the industry. An efficient disease control application is practical during early and moderate infection of Ganoderma in oil palm. Early detection of disease caused by Ganoderma has been conducted in the lab and on the ground manually. This approach requires inspection and sampling of each oil palm in the field, which is laborious, time-consuming, and costly. Airborne detection using a drone provides a good alternative for fast Ganoderma BSR disease detection in oil palms. This study uses hyperspectral images obtained from a camera mounted on a drone and Object-based Image Analysis (OBIA) to detect Ganoderma infection in oil palms and classify the Ganoderma BSR Disease Severity Index (GDSI). In OBIA, segmentation parameters, such as the Edge were set to 30 while Merge was set to 70 to demarcate respective oil palm without overlaps. Two classifiers namely 1) Support Vector Machine (SVM) and 2) K-Nearest Neighbour (KNN) were used to classify the segmented samples for each GDSI. The results demonstrate that the SVM classifier provides a good classification of the raster image with an overall accuracy of 92.5%. The study shows that hyperspectral images captured by drones provide a viable technique for detecting Ganoderma infection and classifying its severity.