Intelligent Farming Systems in Future Using Machine Learning: A Focus on India

Abstract

Abstract: The integration of machine learning (ML) into Indian agriculture holds transformative potential for addressing critical challenges such as climate variability, water scarcity, soil degradation, and market fluctuations. This paper explores the current state of ML applications in Indian agriculture, highlighting successful case studies and initiatives led by government, private sector, and academic institutions. It discusses the technological integration of ML with the Internet of Things (IoT), remote sensing, and blockchain to enhance precision farming practices. Key barriers to widespread adoption, including data quality, infrastructure, and farmer awareness, are identified, along with strategies to overcome them. Future directions emphasize the importance of robust data infrastructure, localized ML models, collaborative research, sustainable practices, and supportive policy frameworks. By leveraging ML, Indian agriculture can achieve significant improvements in productivity, sustainability, and profitability.