Future Farming in India A Playbook for Scaling Artificial Intelligence in Agriculture 2025

The context In India, crop planning is typically reactive and driven by last season’s prices at an individual farm level, often resulting in cycles of gluts and shortages. This reactive approach contributes to price volatility, which increases the risks associated with agricultural investments.In contrast, AI-enabled crop planning uses a wide range of farm and non-farm data – such as soil health, weather patterns, historical prices and food import/export trends – to recommend optimal crops for farmers in various regions. This data-driven strategy aligns production with market demand, minimizing price fluctuations and mitigating the risks of overproduction and underproduction. Before each season, policy-makers release “AI-enabled” district-specific crop recommendations via extension agents, SMS, farm apps, AI voice assistants and other channels.India’s crop planning relies on historical data and generalized guidance, with limited use of real-time, detailed insights, making it reactive to market trends rather than predictive.Current scenario Farmers often base crop choices on past prices or local advice, causing oversupply or shortages. This unpredictability leads to price volatility and risks.Financial institutions, wary of price volatility risks, are hesitant to lend, restricting access for farmers/FPOs/SMEs to credit and investment opportunities.The agricultural sector struggles with volatile prices, low farm incomes and constrained growth, limiting its potential contribution to GDP . Vision 2030 Farmers increasingly use crop plan recommendations to select optimal crops, manage inputs and meet market demand, reducing shortages and oversupply for more stable prices.Banks and financial institutions confidently lend to smallholder farmers/FPOs/SMEs, reducing the risk associated with traditional agriculture lending.Investments in agriculture rise, contributing to a significant increase in farm incomes and boosting the sector’s share of national GDP .AI-enabled macro crop planning There have been several times when the oversupply of tomatoes and onions has led to price volatility in India. For instance, tomato prices in the wholesale market surged over 300% between June and July 2023 from $0.35 per kilogram to more than $1.29 per kilogram.13 In contrast, onion prices experienced a 32% drop in December 202314 due to early harvesting and oversupply, which was further exacerbated by unseasonal rainfall. These fluctuations are driven by supply gluts during harvest periods, leading to distress selling and scarcity in lean months, which forces prices higher. Similar supply gluts can potentially be addressed through AI-enabled predictive crop planning.CASE STUDY 1 Price volatility in IndiaA shift from reactive to AI-enabled crop planning FIGURE 32.1.1 AI-enabled crop planning Future Farming in India 16