Classification of oil palm tree conditions from UAV imagery using the YOLO object detector

Oil palm tree monitoring is essential to track productivity and prevent diseases, but manual methods are labor-intensive, cost-prohibitive, and error-prone. While existing remote sensing data and deep learning methods offer efficient alternatives for monitoring, achieving a highly accurate tree condition classification remains a challenge. Therefore, this study classifies healthy, yellow, small, and dead oil palm trees using the YOLOv8 model. A publicly available multi-class oil palm tree dataset, after extensive correction of identified labeling errors, is used to train the model. The model’s performance is compared with state-of-the-art object detectors, and a prototype web application is developed to test the model on adverse unseen image scenes. The YOLOv8 model achieves up to 99.7% F1-score and 99.3% mAP across all classes of the corrected dataset. It outperforms other object detectors and produces similar scores as the recent YOLOv10-large model. Further validation on unseen images from the developed prototype results in 76.0% F1-score and 77.9% mAP across all classes. Finally, implications emphasize the role of YOLOv8 in handling class imbalance resulting from underrepresented classes in the dataset. The experimental findings, practical demonstration, and implications presented in this paper offer robust and reliable monitoring of oil palm trees with innovations in precision agriculture.