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

Note: * indicates data that will be available via Agristack. ** Indicates data that will be available through Krishi Decision Support System. *** Indicates data that could be facilitated through an agriculture data exchange such as ADeX Source: World Economic Forum. (2021). Artificial Intelligence for Agriculture Innovation. https://www3.weforum.org/docs/WEF_Artificial_Intelligence_for_Agriculture_Innovation_2021.pdf.Fifteen critical datasets for AI in agriculture (Continued) Names Dataset descriptions Weather data** Climate details including rainfall, precipitation, humidity, sunlight, temperature, wind, etc. at the district level Irrigation maps** High-resolution irrigated area mapping to identify areas under irrigation, moisture levels in topsoil, root zone, etc. Storage network details***Storage network details such as crop varieties stored, maximum capacity, average use and safety buffer Warehouse details*** Warehouse details including geolocations, facilities such as cold storage, capacity constraints, tariffs, operating and handling costs and fixed costs Commodity profile data*** Profile including standards for defects based on crop varieties and usage, shelf life, trade constraints, purchase limits and timing of production Defect and pest images*** Annotated high-resolution images of pests and diseases of different crop varieties for AI-based grading, diagnosis and defect identificationTABLE 8 Future Farming in India 51