A lightweight deep learning-empowered cam-push rod system for cost-effective intra-row weed management in lettuce

Intra-row weed competition severely threatens lettuce ( Lactuca sativa L.) cultivation by aggressively consuming soil moisture, nutrients, and light, thereby compromising biomass accumulation and marketable yield. While autonomous mechanical weeding offers a sustainable, chemical-free alternative, its widespread adoption is frequently constrained by prohibitive computational costs and hardware expenses associated with complex robotic systems. To bridge this gap, this study presents a cost-effective intelligent weeding method based on an algorithm-hardware co-design paradigm. On the mechanical front, a novel cam-push rod actuator is designed to replace expensive servo-driven manipulators. Based on a kinematic mathematical model, the cam profile was rigorously optimized to generate a deterministic lateral avoidance trajectory. This "physically encoded" intelligence ensures rapid, precise blade-retraction trajectory tailored to the specific rosette morphology of lettuce seedlings. Synergistically, the system integrates a bespokeightweightdetection model, You Only Look Once-GSBA-Tiny (YOLO-GSBA-Tiny). Through systematic structural pruning and the integration of attention mechanisms, the model reduces parameters and Giga Floating-Point Operations Per Second (GFLOPs) by 69.0 % and 55.6 %, respectively, compared to YOLO 8n, while maintaining a mean average precision of 97.8 %. Controlled environment trials validated the prototype's efficacy, achieving a weed removal rate of 93.86 % with a minimal crop damage rate of 2.08 %. Agronomically, this precision is critical for eliminating competition during the crop’s critical weed-free period, maximizing resource use efficiency, and safeguarding yield potential. Ultimately, this work demonstrates that empowering simple, mathematically optimized mechanical systems with efficient artificial intelligence (AI) provides an agronomically sound and economically scalable pathway for precision weed management. • Proposes a novel algorithm-hardware co-design paradigm for building cost-effective intelligent weeding systems. • A lightweight model, YOLO-GSBA-Tiny, is constructed, balancing competitive accuracy with low computational cost. • An intelligent weeding robot equipped with a low-cost, high-reliability cam-push rod mechanism is developed. • Experiments validate the system's practical value, showing a high weeding rate with minimal crop damage.