Static laser weeding system based on improved YOLOv8 and image fusion

Laser weeding is one of the promising weed control methods for weed management in organic agriculture. However, the complex field environments lead to low weed detection accuracy, which makes it difficult to meet the requirements of high-precision laser weed control. To overcome this challenge and facilitate precise weeding by laser weeding robots in complex fields, this study suggests the use of a dual-mode image fusion algorithm of visible light and infrared light based on machine vision. This innovative technology, introducing infrared information based on visible light images, enhances weed detection accuracy and resilience to environmental factors. The introduction of the Swin-transformer module and Slim-neck module enables the creation of a brand new weed detection model allied with the YOLOv8 model, applicable for weed meristem detection. According to the experimental results, for fusion images with a resolution of 640*640, the dual-scale fusion of RGB and NIR images on the improved network has an average accuracy (mAP) of 96.0% and a detection accuracy of 94.0%, respectively. This study builds a laser weeding robot with a mobile platform, a weed recognition module and a laser polarization transmitter module. The ROS system is utilized to effectively detect weeds and determine their geometric center position after the weed detection model is successfully installed on the robot platform. The laser vibrator demonstrates accurate deflection to the weed growth position during the weed detection and laser illumination experiment. The results show that the accuracy of weed detection has reached 82.1%, and the efficiency of laser weeding has reached 72.3%. These results prove the feasibility of the laser weeding method proposed in this study. However, the fusion strategy of these two kinds of images still has great room for improvement in terms of detection accuracy and efficiency. In the future, multiple modal information can be used to improve the identification efficiency of weeds in the field.