Using Machine Learning in Detecting Ganoderma Disease in Oil Palm Plants
Keywords:
Ganoderma, UAV, reflectance, vegetation indices, Random Forest algorithm, Support Vector Machine algorithmAbstract
Ganoderma disease is asymptomatic, posing challenges in detection and identification. The utilization of Remote Sensing techniques is expected to provide rapid, accurate, and large-scale detection information. This study aims to identify and classify oil palm plant images recorded by a UAV, analyze digital vegetation indices, and apply RF and SVM algorithms. The UAV equipped with sensors capturing three bands: R, G, NIR. Data processing was conducted using software such as Mission Planner, Agisoft MetaShape, Mapir Camera Control, ArcGIS 10.5, Envi 5.3, and R Studio. The study locations were at Pabatu. Image recording took place on July 3, 2021. The observed parameters included disease incidence, reflectance values, vegetation indices (NDVI, GNDVI, SAVI, SR, CIgreen), with plant classes categorized as Healthy (H) and Infected (I). Infected plants exhibited lower reflectance compared to healthy plants. All vegetation indices, including NDVI, GNDVI, SAVi, SR, and CIgreen, were lower in infected plants compared to healthy plants. The SVM algorithm demonstrated the highest accuracy of 93.55% compared to RF only 84.42%. With Machine Learning algorithm, disease occurrences where imagery has been recorded can be predicted. This map can serve as fundamental information for control strategies, production calculations, and other cultivation activities.
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