Design and experiment of an adaptive cruise weeding robot for paddy fields based on improved YOLOv5

•An adaptive cruise weeding robot for paddy fields was developed for the first time.•A real-time rice seedling recognition method based on MW-YOLOv5s was proposed.•A navigation line fitting method for paddy field adaptive operations was proposed. Weed control in paddy fields is a critical agronomic practice for enhancing rice yield, in which mechanical weed control is widely used due to its high weed control rate and convenient operation. However, traditional mechanical weed control methods require manual operation, leading to increased operational costs. Therefore, adaptive cruise weeding robots hold significant application promise and market potential. In this paper, an adaptive cruise weeding robot for paddy fields based on improved YOLOv5 is designed to adaptively perform weeding operations. Firstly, a real-time rice seedling recognition method based on MW-YOLOv5s is proposed by establishing a rice seedling dataset under different paddy field environments and growth stages. The model combines the Backbone network structure with MobileViTv3, replacing GIoU_loss with WIoU_loss, which significantly improves the precision and speed of rice seedling recognition. Next, the MW-YOLOv5s was integrated into a paddy weeding machine, and seedling navigation lines were extracted using the least square method. Finally, the control system autonomously operates the weeding machine in real time through feedback control based on the navigation path. The test results showed that the MW-YOLOv5s model exhibits strong recognition performance for rice seedlings under different paddy field backgrounds and growth stages, with a precision of 90.05 % and an mAP of 92.32 %, while the real-time performance reaches 19.51 FPS, which meet the requirements for real-time operation of the weeding machine in paddy fields. The results of the paddy field weed control experiment reveal a weed control rate of 82.4 % and a seedling injury rate of 2.8 %, which meet the agronomic requirements for mechanical weed control in paddy fields. The research findings contribute to the design of intelligent agricultural equipment and promote the practice and application of machine vision in the field of paddy field plant protection.