Morphology-guided dual-level detection and depth localization of cotton terminal buds for precision topping
Year: 2026
Type: article
Abstract: Reliable terminal-bud localization is essential for precision cotton topping, yet the small bud core is often obscured by apical leaves and visually confounded with the surrounding canopy. We developed DGQ-YOLO, a lightweight morphology-guided detector that jointly identifies the bud core and a composite terminal-bud region encompassing the core and adjacent apical leaves. This dual-level representation preserves structural cues when the bud core is partly occluded. The model combines C2f_DWR_CA for multiscale, position-sensitive feature extraction, VoVGS-CA for lightweight feature fusion, and QCN loss for quality-aware classification and small-object regression. DGQ-YOLO was evaluated using 2,230 field images of long-staple cotton at the early bud-development stage. It achieved mAP@50 values of 93.2% and 94.1% for the bud core and composite terminal-bud region, respectively. Overall mAP@50 and mAP@50:95 were 93.6% and 81.7%, respectively. Compared with YOLOv8n, DGQ-YOLO improved overall mAP@50 by 2.2 percentage points. Its parameter count decreased from 3.01 to 2.90 million, and its computational cost fell from 8.2 to 7.8 GFLOPs. Combining the centers of the two detected regions with RGB-D depth measurements supported online terminal-bud localization in field experiments. This morphology-informed perception framework supports precision topping under the tested cultivar, developmental stage, and field conditions, although broader agronomic and actuator-level validation is required.
Source: Frontiers in Plant Science
Institutions: Shihezi University, Ministry of Agriculture and Rural Affairs
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,801
Award: G1442314162
OpenAlex ID: W7221775113