Canopy and leaf vein segmentation and topology-guided localization of stem points in tobacco plants using YOLO-ODS.

Accurate stem point localization is critical for intelligent tobacco production but remains challenging in low-altitude nadir-view UAV imagery. This is because the visible upper stem after topping is small, weakly textured, and visually similar to surrounding canopy tissues. This study formulates stem point localization as a topology-guided geometric inference problem based on canopy and leaf vein structures. A lightweight instance segmentation model, YOLO-ODS, was developed on YOLOv11n-Seg to jointly segment tobacco canopies and leaf veins from UAV imagery. The model integrates an Orientation-Aware Convolution Module (OACM), a Dual Interactive Attention Module (DIAM), and Slide Loss to enhance directional feature representation and improve discrimination of fine-grained structures. Based on segmentation results, leaf vein skeletons are extracted and local trend lines are fitted to estimate stem points via geometric convergence under canopy constraints. Experimental results showed that YOLO-ODS achieved a Recall of 86.79%, a mask mAP@0.5 of 90.19%, and a mask mAP@0.5:0.95 of 67.20%, outperforming the YOLOv11n-Seg baseline by 2.20, 1.14, and 2.10 percentage points, respectively. For the leaf vein category, the mask mAP@0.5:0.95 reached 51.49%. With 2.86 M parameters and 10.19 GFLOPs, YOLO-ODS remained lightweight for field deployment. The proposed localization method achieved an