Weed Detection

Abstract

Effective weed detection and management are crucial for ensuring optimal crop growth and yield in agricultural fields, particularly in wheat crops where weed competition can significantly impact productivity. Traditional weed management methods, such as manual scouting, mechanical weeders, and chemical herbicides, have limitations in terms of accuracy, efficiency, and environmental sustainability. To address these challenges, this study proposes a novel weed detection and management system based on deep learning and computer vision techniques. The system leverages advanced convolutional neural networks (CNNs), including YOLOv3 and YOLOv5, to detect and classify weeds in real-time from images captured in wheat fields. By analyzing a diverse range of research papers and methodologies in the field, a comprehensive literature review was conducted to identify key trends, challenges, and opportunities. Feasibility studies were conducted to assess the technical, operational, economic, legal, and functional aspects of the proposed system, demonstrating its potential for successful implementation and adoption across diverse agricultural settings. The system offers several advantages, including high accuracy weed identification, real-time monitoring, targeted herbicide application, and scalability for large-scale agricultural operations. Additionally, the system promotes environmental sustainability by minimizing herbicide usage and preserving soil health. Overall, the proposed weed detection and management system represents a significant advancement in agricultural technology, offering a sustainable, efficient, and scalable solution for addressing the challenges of weed control in wheat crops.