CONVOLUTIONAL SVM NETWORKS FOR DETECTION OF GANODERMA BONINENSE AT EARLY STAGE IN OIL PALM USING UAV AND MULTISPECTRAL PLEIADES IMAGES
Keywords: Basal stem rot, Support Vector Machine, UAV, Pleiades
Abstract. Oil palm performs a considerable role in Malaysia’s economic system as Malaysia is the second-biggest palm oil manufacturer in the world. In oil palm plantations. Basal stem rot (BSR) is a disease caused by Ganoderma boninense that is responsible for a considerable annual losses, particularly in South East Asia. The disease remains an unresolved problem in most production areas due to lack of disease management strategy to detect the infected palms at their early stage. In recent years, advancement in remote sensing platforms and image processing methods have produced remarkable results for the detection of diseases at early stage. In this study, support vector machine (SVM) classifier was performed on UAV and Pleiades imagery to determine the ideal classification model for the early diagnosis of BSR disease in oil palms. The investigation's results showed that UAV provided the most accurate prediction, with a total accuracy of 68.28%, while 64.52% of the early Ganoderma infections could be identified with accuracy levels of 64.07% and 64.49%, respectively. The early Ganoderma infection could be recognized with an overall accuracy of 64.07% and 64.49%, respectively, while the Pleiades had an overall accuracy of 68.28% and 64.52%. Although the categorization accuracy appeared to be only modest at first glance, the quantity of detail offered by the imageries suggested that the accuracies were acceptable.