MACHINE LEARNING APPROACHES FOR WEED IDENTIFICATION IN PRECISION AGRICULTURE

Abstract

Agriculture plays a crucial role in ensuring global food security, and improving crop productivity has become an important objective in modern farming practices. One of the major challenges faced by farmers is the presence of weeds that compete with crops for nutrients, water, sunlight, and space, leading to significant reductions in crop yield and quality. Traditional weed management methods often rely on manual inspection and uniform herbicide spraying, which are labor-intensive, time-consuming, and may result in excessive chemical usage that can harm the environment. To address these challenges, this study presents a machine learning-based approach for weed identification in precision agriculture that utilizes image processing and intelligent data analysis techniques to automatically distinguish weeds from crop plants. The proposed system involves several stages including image data collection, preprocessing, feature extraction, and machine learning model training to accurately classify plant species present in agricultural fields. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Networks (CNN) are used to analyze plant characteristics such as shape, color, and texture for accurate weed detection. By learning patterns from agricultural datasets, the system can effectively identify weeds and crops with improved accuracy.