Artificial Intelligence and Digital Agricultural Marketing: Transforming Farmers' Market Access and Marketing Efficiency in India
Authors
Assistant Professor (Humanities), JNKVV CoA Tikamgarh M.P. (India)
Scientist, RARS Sagar M.P. (India)
Article Information
DOI: 10.47772/IJRISS.2026.100601322
Subject Category: Agriculture
Volume/Issue: 10/6 | Page No: 19398-19405
Publication Timeline
Submitted: 2026-07-01
Accepted: 2026-07-06
Published: 2026-07-18
Abstract
Agricultural marketing in India is undergoing a rapid transformation due to the integration of Artificial Intelligence (AI), digital technologies, and online marketing platforms. Traditional agricultural marketing has long been constrained by information asymmetry, inadequate market infrastructure, multiple intermediaries, price volatility, and limited access to organized markets. The emergence of AI-enabled technologies, digital marketplaces, e-commerce platforms, blockchain, big data analytics, and mobile-based advisory services has significantly improved market efficiency and transparency. Government initiatives such as the Electronic National Agriculture Market (e-NAM), AgriStack, Digital Agriculture Mission, and Farmer Producer Organizations (FPOs) have further accelerated digital transformation in agricultural marketing. AI-powered applications now assist farmers in market intelligence, price forecasting, demand prediction, quality assessment, logistics management, and direct marketing.
This paper reviews the role of Artificial Intelligence in improving agricultural marketing efficiency in India. It examines the opportunities and challenges associated with AI adoption and discusses its implications for farmers, agribusiness firms, policymakers, and consumers. The study is based on secondary data collected from research articles, government reports, and institutional publications. The findings suggest that AI has considerable potential to reduce transaction costs, improve price realization, enhance market transparency, and strengthen agricultural value chains. However, digital literacy, internet connectivity, infrastructure gaps, data privacy concerns, and high implementation costs remain major constraints. The paper concludes with policy recommendations aimed at promoting inclusive and sustainable digital agricultural marketing in India.
Keywords
Artificial Intelligence, Digital Agriculture, Agricultural Marketing, e-NAM, AgriStack, Farmer Producer Organizations, Marketing Efficiency, Digital Platforms.
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References
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