The Role of Machine Learning in Indian Agriculture: An Integrative Review

Machine learning (ML) has become a revolutionary tool in agriculture, providing sophisticated functions for crop selection, yield forecasting, disease detection, and resource optimisation. In India, marked by ecological diversity, smallholder prevalence, and infrastructure inequalities, machine learning has the potential to tackle enduring productivity and sustainability issues. This paper examines the present state of machine learning applications in Indian agriculture, identifies significant obstacles to deployment, and offers strategic recommendations for future advancement. Widely utilised machine learning methods, such as Support Vector Machines, Random Forests, and deep learning methodologies, have exhibited robust efficacy in classification, prediction, and picture diagnostics. Nevertheless, numerous obstacles impede large-scale implementation, including insufficient digital infrastructure in rural regions, the absence of standardised and high-quality datasets, limited digital literacy among agricultural practitioners, the inadequate generalisability of algorithms across agro-ecological zones, and insufficient integration with public extension services. The research underscores the necessity of creating open-access agricultural databases, promoting interdisciplinary collaboration, developing user-friendly and localised technologies, enhancing policy frameworks, and empowering farmers and extension workers. It is essential to address these interconnected challenges through a comprehensive, ecosystem-orientated approach in order to fully realise the potential of machine learning to promote an inclusive, climate-resilient, and data-driven agricultural revolution in India.