A Field‐Adaptive Mechanical Weeding System Coupling Oscillating Pneumatic Mechanism With Deep Learning for Intra‐Row Weed Control in Lettuce

ABSTRACT Intra‐row weeding is a critical yet unresolved problem in precision horticulture, where crops and weeds exhibit tight spatial proximity and strong visual similarity under fluctuating field conditions. Addressing this challenge requires not only reliable crop‐weed discrimination but also accurate crop‐center localization tightly coupled with fast and safe actuation. This study introduces a field‐adaptive intra‐row weeding system that integrates an oscillating pneumatic mechanism with a purpose‐designed deep learning framework, LettPointNet. LettPointNet leverages multi‐scale feature fusion and a geometric center‐point constraint to enhance spatial robustness, yielding 95.1% precision, 96.8% recall, 95.9% F1‐score, 98.3% mAP50, and 90.6% mAP at a modest 7.7 GFLOPs, thereby supporting real‐time embedded operation. Relative to lightweight YOLOv11n/12n baselines, LettPointNet improves F1‐score and mAP by 3.4–3.9 and 6.5–6.6 percentage points, respectively. Conveyor‐based evaluations (0.05–0.20 m/s; three weed‐density levels) demonstrated 84.6% lettuce localization and 81.1% weeding performance, with response‐surface analysis confirming significant interactions between speed and density. Polytunnel trials further validate system robustness, achieving 82.2% and 80.9% weeding rates under favorable and low‐light conditions, respectively, with minimal crop damage (1.99% and 2.57%). Collectively, the results establish that precise perception‐actuation coupling enables reliable, real‐time intra‐row weeding and offers a viable pathway toward automated and sustainable protected‐crop management.