Early Identification of Oil Palm Health Based on UAV Images Using Feature-Based Machine Learning

An efficient oil palm plantation management involves monitoring the health of the trees to ensure good yield. However, manual inspection is unable to meet the expectation of large scale plantations. The feasibility of employing unmanned aerial vehicles (UAV) and various artificial intelligence techniques have been studied with encouraging results. However, identifying unhealthy trees in UAV images is challenging. This research investigated the feasibility of using machine learning techniques in distinguishing unhealthy trees from healthy trees. Four classifiers were evaluated with two sets of data, in which one is publicly available and the other was provided by our industry collaborator. Support vector machine (SVM) with different kernels was found to perform the best in both datasets, with an accuracy of 0.96 and 0.975 respectively.