Abstract: Weed management is a critical challenge in agriculture, affecting crop yields and sustainability. Traditional methods, such as manual weeding and blanket herbicide spraying, are labor-intensive and environmentally harmful. This paper presents an AI-driven Weed Detection and Management System that utilizes deep learning models like YOLOv8 and Convolutional Neural Networks (CNNs) to accurately detect and classify weeds in real time. By integrating computer vision, precision agriculture techniques, and automated herbicide application, the system minimizes chemical usage and improves farming efficiency. Experimental results demonstrate over 92% accuracy in weed detection, making this system a scalable solution for modern agriculture.
Keywords: Weed Detection, Deep Learning, YOLO, Precision Agriculture, Machine Learning, AI in Farming
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DOI:
10.17148/IJARCCE.2025.14230
