Hyperspectral Imagery for Mapping Disease Infection in Oil Palm Plantation Using Vegetation Indices and Red Edge Techniques

Abstract

Problem statement: Large scale plantation of oil palm trees requires on-time detection of diseases as the ganoderma basal stem rot disease was present in more than 50% of the oil palm plantations in Peninsular Malaysia. Approach: To deal with this problem, airborne hyperspectral imagery offers a better solution in order to detect and map the oil palm trees that were affected by the disease on time. Airborne hyperspectral can provide data on user requirement and has the capability of acquiring data in narrow and contiguous spectral bands which makes it possible to discriminate between healthy and diseased plants better compared to multispectral imagery. By using vegetation indices and red edge techniques, the condition of oil palm trees could be determined accurately. Results: Generally, all of these techniques showed better results as they could give accuracy between 73 and 84%. The highest accuracy was achieved by using Lagrangian interpolation technique with 84% of overall accuracy. Conclusion/Recommendations: The red edge based techniques were more effective than vegetation indices in detecting Ganoderma-infected oil palm trees plantation since there were three out of four techniques that could yield high accuracy results.