A keypoint-based method for detecting weed growth points in corn field environments

Weed growth significantly impacts corn yield. With the continuous development of weed control technologies, achieving more effective and precise weed management has become a major challenge in corn production. To achieve precise weed suppression, this study proposes a growth point detection method based on a keypoint pose estimation model capable of effectively detecting various weeds and locating various weed growth points during the 2-5 leaf stage of corn development. To address the complex working environment of precision weeding machines in corn fields, including occlusion, dense growth, and variable lighting conditions, we design a dilation-wise residual module (DWRM) for the detector and a separation and enhancement attention module (SEAM) for pose estimation to adapt to these challenges. Furthermore, owing to the limited computational resources in field settings, we introduced the RepViT block (RVB) to achieve model lightweighting. The proposed method was evaluated on the constructed corn field dataset. The experimental results demonstrated that SRD-YOLO achieved an of 96.5%, an F1 score of 94%, and an FPS of 169, while reducing the model parameters by 8.7M. SRD-YOLO effectively meets the requirements for growth point localization under challenging conditions, providing robust technical support for real-time and precise weed control in corn fields.