Advanced classification techniques for weed and crop species recognition using machine learning algorithms

Abstract

This study proposes an intelligent machine learning framework integrating image analysis and environmental data for precision weed management. The framework leverages efficient feature extraction techniques combined with supervised machine learning algorithms to accurately classify multiple species. Features such as color, texture, and shape characteristics are utilized for model training, enabling high-precision classification while maintaining low computational complexity. The experimental results demonstrate the robustness of the approach, achieving an average classification accuracy of 94.3% across ten weed and crop species in diverse agricultural environments. The system also achieved a 90% reduction in herbicide application compared to traditional methods, showcasing its potential for sustainable farming. Real-time testing confirmed the framework’s efficiency, processing images in under 1.5 seconds per frame, making it suitable for deployment in drones and autonomous farming equipment. These results underscore the practical and scalable nature of the proposed system in automating weed management and advancing sustainable agricultural practices.