Real-Time Weed Segmentation in Tobacco Crops Utilizing Deep Learning on a Jetson Nano

This paper explores the deployment of a deep learning model on an edge device, such as the Jetson Nano for real-time weed detection in agricultural environments. The U-Net-MobileNetV2 architecture is utilized to achieve accurate and efficient detection of weeds while minimizing computational costs and inference time. The effectiveness of the model in accurately segmenting weed in aerial images of tobacco fields has been demonstrated through a series of simulations and real-time field validations. The methodology proposed in this study demonstrated 96% accuracy, accompanied by a mean intersection over union value of 0.851. Furthermore, the trained model is implemented on the Jetson Nano platform, and real-time validation is showcased. The successful deployment of the model on a mobile setup in the tobacco fields of Mansehra, Pakistan, underscores its practical applicability and relevance to precision agriculture practices.