A Study of Weed Management in Agricultural Plants using IoT and Machine Learning

Abstract

Weeds are any unwanted plant growth in a cultivated land. Weeds are vital for agriculture as not all weeds as beneficial for growth of the crop. Most weeds decrease yield while increasing the production cost. This study introduces an innovative approach to weed detection and classification in agricultural environments by harnessing the synergy of Internet of Things (IoT) technology and advanced Machine Learning (ML) algorithms. Contemporary agriculture places a significant emphasis on weed management to optimize crop yields and minimize herbicide usage. Our proposed system employs a network of IoT sensors equipped with cameras and environmental sensors to maintain continuous field surveillance. By using a network of IoT smart sensors equipped with cameras and environmental sensors, our system provides real-time weed monitoring, and it's complemented by an IoT-controlled robot designed for automated weed cutting. Real-time image analysis, powered by ML algorithms, particularly the YOLOv7 model, enables the prompt recognition and categorization of weeds. By providing farmers with timely alerts through an user-friendly intuitive interface, our system facilitates tailored weed management strategies. This innovative approach combines IoT technology and advanced ML algorithms for efficient weed detection and classification in agriculture. The seamless integration of these technologies offers farmers an effective method for tailored weed management, promoting sustainable and organic farming practices with minimum herbicide use. In bridging the gap between IoT and ML technologies, our research offers a promising solution for precision agriculture and underscores the significance of sustainable and organic farming methods.