AI DRIVEN CROP QUALITY ENHANCEMENT AND WEED DETECTION USING MACHINE DEEP LEARNING TECHNIQUES

Abstract

Modern agriculture is under significant pressure due to changing climatic conditions, rapid population growth, and the increasing need for sustainable farming practices. Traditional methods of crop monitoring and weed control often rely on manual inspection and excessive chemical usage, which can be inefficient and harmful to the environment. To address these challenges, this research explores the application of machine learning and deep learning techniques for intelligent crop quality assessment and automated weed management. By utilizing computer vision technologies, real-time sensor data, and predictive analytics, the proposed system enables early detection of weeds, accurate classification of crop health, and data-driven decision-making for precision farming. The experimental evaluation of the proposed approach demonstrates substantial improvements over conventional agricultural practices. The AI-based model achieves higher accuracy in identifying weed species and assessing plant conditions, leading to optimized use of fertilizers and pesticides. As a result, chemical wastage is minimized, operational costs are reduced, and overall crop yield is enhanced. These findings highlight the transformative potential of integrating artificial intelligence into agriculture, offering farmers an effective, eco-friendly, and economically viable solution for meeting future food production demands while preserving environmental sustainability