Barley Head Detection Using UAV Imagery and YOLOv10

Barley head detection is a crucial task for agricultural applications such as yield estimation and crop monitoring. Unlike wheat, automated barley head detection has not been extensively studied due to challenges posed by its complex head structures and the lack of annotated datasets. In this paper, we leverage YOLOv10, a state-of-the-art object detection framework, to detect barley heads from high-resolution images captured using UAVs. Our dataset, consisting of UAV-captured images and supplemented with the Global Wheat Head Dataset, provides a robust foundation for model training. The proposed approach achieves a mean Average Precision of 0.83 at Intersection of Union 0.5, setting a new benchmark for barley head detection. This work contributes to advancing automated crop monitoring systems in precision agriculture.