Benchmarking YOLO and Transformer-Based Detectors for Olive Tree Crown Identification in UAV Imagery

Olive groves are an important agricultural component in the Mediterranean region that offers various ecological benefits. The olive tree has tremendous cultural and economic value and is cultivated over a wide geographical range. It is essential to actively implement innovative agricultural practices to achieve efficient, sustainable olive cultivation. Automatic tree identification in olive groves is an essential tool for applications such as tree health monitoring and yield estimation. Deep learning-based approaches, which have recently gained prominence, hold significant potential for this purpose. However, the large amount of training data required by deep learning methods increases their time and effort costs. Data augmentation methods have been developed to solve this problem. In this study, olive tree detection and segmentation from unmanned aerial vehicle (UAV) images were performed using current You Only Look Once (YOLO) architectures (YOLOv8, YOLOv10, YOLOv11, YOLOv12) and transformer-based object detection algorithms (Real-Time DEtection TRansformer (RT-DETR) and Roboflow-DEtection Transformer (RF-DETR)). Two different datasets, one of which was a new dataset generated within the scope of this study, were used in this study. To investigate the effect of data augmentation on algorithm performance, both the original datasets and the augmented datasets were used. As a result of the study, 0.987 mAP was obtained with YOLOv11n, YOLOv11s, and YOLOv12s on the Olive Tree Detection (OTD) dataset, while 0.884 mAP was obtained with YOLOv8l and YOLOV8x on the Yalova dataset.