From price prediction to market intelligence: The role of artificial intelligence in reshaping agricultural marketing in India

Abstract

Agricultural marketing plays a vital role in connecting farmers with consumers, processors, wholesalers, retailers, exporters, and other stakeholders in the agricultural value chain. However, traditional agricultural marketing systems are often affected by inadequate market information, price fluctuations, information asymmetry, dependence on intermediaries, high transaction costs, post-harvest losses, inefficient logistics, and limited bargaining power of farmers. The emergence of Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, Internet of Things (IoT), remote sensing, blockchain, and digital platforms offers significant opportunities to overcome these challenges. AI can transform agricultural marketing into a more data-driven, predictive, transparent, and farmer-centric system. By analysing large datasets related to historical prices, weather, production, market arrivals, consumer demand, transportation, storage, and global commodity markets, AI can support price and demand forecasting, market intelligence, quality assessment, inventory management, supply-chain optimization, risk management, and digital commerce. In India, AI has considerable potential due to the large number of small and marginal farmers, fragmented supply chains, diverse agro-climatic conditions, and uneven market access. Digital initiatives such as the National Agriculture Market (e-NAM) provide a foundation for technology-enabled market integration and improved price discovery.