Design and Experimental Evaluation of a Smart Intra-Row Weed Control System for Open-Field Cabbage

Addressing the challenges of complex structure, limited modularization capability, and insufficient responsiveness in traditional hydraulically driven inter-plant mechanical weeding equipment, this study designed and developed an electric swing-type opening and closing intra-row weeding control system. The system integrates deep learning technology for accurate identification and localization of cabbage, enabling precise control and dynamic obstacle avoidance for the weeding knives. The system’s performance was comprehensively evaluated through laboratory and field experiments. Laboratory experiments demonstrated that, under conditions of low speed and large plant spacing, the system achieved a weeding accuracy of 96.67%, with a minimum crop injury rate of 0.83%. However, as the operational speed increased, the weeding accuracy decreased while the crop injury rate increased. Two-way ANOVA results indicated that operational speed significantly affected both weeding accuracy and crop injury rate, whereas plant spacing had a significant effect on weeding accuracy but no significant effect on crop injury rate. Field experiment results further confirmed that the system maintained high weeding accuracy and crop protection under varying speed conditions. At a low speed of 0.1 m/s, the weeding accuracy was 96.00%, with a crop injury rate of 1.57%. However, as the speed increased to 0.5 m/s, the weeding accuracy dropped to 81.79%, while the crop injury rate rose to 5.49%. These experimental results verified the system’s adaptability and reliability in complex field environments, providing technical support for the adoption of intelligent mechanical weeding systems. Future research will focus on optimizing control algorithms and feedback mechanisms to enhance the system’s dynamic response capability and adaptability, thereby advancing the development of sustainable agriculture and precision field management.