Post-harvest management Use case DescriptionHigh-level flowchart of AI value delivery Start-up examples AI-based quality assessmentAI systems assess the quality of harvested produce using image recognition, sorting them based on size, colour and defects, which improves market value and reduces waste. –Images of produce are captured –AI evaluates quality attributes –Produce is sorted into categories –Proper packaging based on quality grades –Higher market value and reduced wasteIntello Labs (India) AgNext (India) Storage condition monitoringAI monitors storage conditions such as temperature and humidity to prevent spoilage of stored grains and produce, extending shelf life and ensuring food safety. –Sensors monitor storage conditions –AI detects deviations from optimal conditions –Notifications sent if issues detected –Adjustments made to storage environment –Reduced spoilage and maintained qualityEcozen Solutions (India) GrainSense Demand forecasting AI predicts consumer demand for various crops, helping farmers and suppliers adjust production and inventory levels accordingly, reducing waste and optimizing profits. –Market trends and historical sales data gathered –AI forecasts future demand for crops –Farmers and suppliers adjust production plans –Supply-chain logistics are aligned with demand –Reduced overproduction and waste, with optimized profitsUdaan (India) Route optimization for transportationAI optimizes transportation routes for delivering agricultural produce, reducing fuel costs, delivery times and carbon emissions. –Data on destinations, traffic and road conditions collected –AI calculates optimal routes –Drivers follow AI-recommended routes –AI adjusts routes in real time if needed –Efficient deliveries and cost savingsLocus.sh (India) BlackBuck (India): Case study Market access and price forecasting Use case DescriptionHigh-level flowchart of AI value delivery Start-up examples Price-forecasting modelsAI analyses market trends, supply and demand and historical prices to forecast future commodity prices, helping farmers decide when to sell their produce to maximize profits. –Market data and historical prices gathered –AI predicts price movements –Farmers receive price forecasts –Farmers plan sales accordingly –Improved income through better market timingAgriBazaar (India) Commodities Control (India): Case study Digital marketplaces AI-powered platforms connect farmers directly with buyers, reducing intermediaries. The platforms match supply with demand efficiently, ensuring fair prices for farmers and fresh produce for consumers. –Farmers and buyers join the platform –Farmers list produce availability –AI matches farmers with buyers –Secure payments and logistics are arranged –Fair pricing and efficient market access achievedDeHaat (India): Case study eNAM (India): Success storiesAI use cases in agriculture (continued) TABLE 7 Future Farming in India 48

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