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

Advisory services and knowledge dissemination Use case DescriptionHigh-level flowchart of AI value delivery Start-up examples AI chatbots for farmer adviceAI-powered chatbots provide farmers with instant answers to agricultural queries in local languages, offering advice on best practices, crop management and problem-solving. –Farmers ask questions via app or SMS –AI understands the query using natural language processing –AI accesses agricultural information database –Answers provided in simple language –Farmers receive timely adviceAwaaz De (India): case study Microsoft AI for Agriculture: Case study Decision support systemsAI systems integrate various data sources to provide farmers with personalized recommendations on crop planning, input usage and market opportunities, enhancing decision-making. –Data on weather, soil and markets gathered –AI processes data to generate insights –Personalized advice provided to farmers –Farmers make informed decisions –Improved farm productivity and profitabilityKisan Network (India): Case study Cropin (India): Case study Precision agriculture Use case DescriptionHigh-level flowchart of AI value delivery Start-up examples Variable rate application (VRA)AI systems analyse field data to determine the precise number of inputs (such as seeds, fertilizers and pesticides) needed in different parts of the field. This optimizes input usage, reduces waste and enhances crop yields. –Field data gathered via sensors and maps –AI determines input needs per area –AI generates a prescription map showing input requirements –Machinery applies inputs precisely –Improved yields and resource efficiencyJohn Deere: Technology for precision agriculture Trimble Agriculture: Solutions Precision irrigation managementAI analyses weather forecasts, soil moisture data and cropwater needs to optimize irrigation schedules. This ensures that crops receive the right amount of water at the right time, conserving water and improving plant health. –Soil-moisture sensors and weather data collected –AI calculates irrigation needs –Optimal watering times are set –Irrigation systems water crops accordingly –Continuous adjustments are made based on new dataNetafim Precision Agriculture Fasal (India): Case studies Automated farm machineryAI-powered tractors and equipment perform tasks such as planting, weeding and harvesting with high precision, reducing labour costs and increasing efficiency. –AI plans farming tasks –Autonomous machinery carries out tasks –Machines gather data as they work –AI refines operations based on data –Efficient farm operations with reduced labour needsCNH Industrial: Case studyAI use cases in agriculture (continued) TABLE 7 Future Farming in India 46