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Mr. Sudarshan Balasaheb Babar is an Assistant Professor at Lotus Business School, Pune, India. His research interests include artificial intelligence, business analytics, sustainable agriculture, and digital transformation.
Agriculture constitutes the foundation of global food production and rural economies; yet, it confronts substantial difficulties such as climate change, resource depletion, pest outbreaks, diminishing soil fertility, and rising food demand. Machine Learning (ML) has emerged as a disruptive technology that enhances agricultural output, ensures food security, and promotes sustainable rural development through data-driven decision-making. Utilising advanced algorithms, sensor networks, remote sensing technologies, and big data analytics, machine learning facilitates precision agriculture, crop yield forecasting, disease identification, resource optimisation, supply chain management, and the improvement of rural livelihoods. This chapter examines the principles, methodology, applications, problems, and future trajectories of machine learning in agriculture, food security, and rural development, emphasising its capacity to foster resilient, efficient, and sustainable agri-food systems.