Intelligent Weeding Design Based on Visual Recognition and YOLOv5

The aim of this study is to design an intelligent weed control system based on edge computing and YOLOv5 to cope with the impact of weeds on crop yield in agricultural fields. This system adopts YOLOv5 deep learning algorithm for weed recognition, and real-time image processing and data analysis is achieved by edge computing platform. Firstly, the edge device collects farmland images through the camera and performs preprocessing; then, the YOLOv5 model is used for target detection to identify and locate the weeds; then, the recognition results are transmitted to the cloud server for further feature extraction and analysis. Ultimately, the generated weed location information will be fed back to the robotic arm module to implement precise weeding operations. The innovation of the research lies in the combination of edge computing and deep learning techniques, which significantly improves the accuracy of visual recognition and reduces the latency of data transmission, enhancing the overall efficiency of the system.