Advanced Deep Learning Models for Real-Time Weed Detection and Classification in Cotton Fields

Abstract

Weed management in cotton farming is crucial for maintaining crop health and yield. Traditional methods, such as hand weeding and herbicide application, are labour intensive and pose environmental risks. This study explores advanced deep-learning techniques for real-time weed detection and classification in cotton fields under variable environmental conditions. The proposed system utilizes CNNs (Convolutional Neural Networks) CNNs to provide robust weed management by adapting to different lighting, soil types, and weather conditions. The research includes the development of a CNN-based model, extensive field trials for validation, and real-time implementation on edge devices. The results showcase the effectiveness of the model effectiveness in improving weed detection accuracy and efficiency, thus offering a sustainable approach to weed management in agriculture.