Tea Shoot Category Detection Based on UAV Remote Sensing

This study proposes an object detection network named HR-YOLOv10-S for UAV remote sensing-based detection of three tea shoot categories. The network aims to solve key problems in UAV images under complex tea plantation environments, including large target scale changes, excessive background information, and motion blur. Based on the YOLOv10 framework, the proposed network incorporates Shape Weights and Scale Adjustment Factors into the bounding-box regression loss to jointly account for target shape and scale variations, thereby improving the geometric consistency and localization accuracy between predicted and ground-truth bounding boxes. This mechanism improves the matching ability between predicted bounding boxes and real targets. The Rectangular Self Calibrated Module is introduced to improve the network ability to model complex spatial structure information, so it can capture target edge features accurately and improve localization. The Histogram Transformer is added to make full use of the global statistical distribution features of images and reduce interference from complex background noise. Experimental results on the test dataset showed that HR-YOLOv10-S achieved a Precision of 91.11%, a Recall of 88.91%, an mAP@0.5 of 93.33%, and an F1-score of 89.99%. Compared with the baseline YOLOv10 model, these metrics increased by 8.68, 4.03, 5.18, and 6.36 percentage points, respectively. Under the same experimental settings, HR-YOLOv10-S also achieved higher values for these four metrics than SSD, CornerNet, and RT-DETR. These findings suggest that the proposed model can improve the detection performance of three tea shoot categories under conditions involving target-scale variation, background interference, and partial occlusion. Therefore, HR-YOLOv10-S provides a potentially useful approach for UAV remote sensing-based detection of three tea shoot categories and smart tea plantation management, although further validation on independent datasets is needed to assess its broader generalizability.