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...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 2nd-5th 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 re-sources 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 mAPkpt of 96.5%,an Fl score of 94%,and an FPS of 169,while reducing the model pa-rameters 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.展开更多
基金This work was supported in part by the Tibet Shigatse Science and Technology Projects(No.RKZ2024ZY-03)the Shandong Province Modern Agricultural Industry Technology System,China(No.SDAIT-18-06)+1 种基金the China Agriculture Research System of MOF and MARA(No.CARS-18-ZJ0402)the National Natural Science Foundation of China(No.32001419).
文摘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 2nd-5th 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 re-sources 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 mAPkpt of 96.5%,an Fl score of 94%,and an FPS of 169,while reducing the model pa-rameters 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.
