Middle-Level Fusion YOLO on Multispectral Image to Detect Unhealthy Oil Palm Trees

Locating the unhealthy oil palm trees is one of the essential things in precision agriculture. In several countries such as Indonesia, Malaysia, and Thailand, Ganoderma disease often attacks oil palm trees. Detection techniques using leaf sample pieces, the possibility of biological changes in the leaf, and the collection process are too tricky. Detection techniques using image samples captured by the drone can be more accessible, but they need to provide complete information related to plant vegetation. This research proposes detecting unhealthy oil palms by capturing top-view images using drone. The drone has dual cameras (RGB and OCN bands) to get more information on plant vegetation. Middle-level fusion YOLO is used to recognize the unhealthy oil palm trees. The data was collected at the Oil Palm Plantation in Bogor, which contains over 100 unhealthy objects. Using multispectral images can improve performance compared to using only RGB images. The proposed method provides better performance than using only RGB images and OCN images with mAP (mean Average Precision) is 0.919. The proposed method provides better performance in detecting unhealthy oil palm trees.