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

The World Economic Forum’s AI4AI Framework has identified close to 30 use cases spanning all three stages of the agriculture value chain: intelligent crop planning, smart farming and farm-to- fork solutions. These use cases highlight the diverse ways in which AI can optimize agricultural productivity, sustainability and market connectivity. The report’s authors analysed these use cases and rigorously tested their feasibility in the Indian context through expert consultations. This process shortlisted several promising AI use cases for agriculture, four of which are described in detail in this section to illustrate their process flows and key enablers. The analysis also identifies the foundational datasets required for each use case and outlines a roadmap for operationalizing these AI applications by 2030. It is important to note that the use cases presented here are indicative; any effort to prioritize use cases must be tailored to specific needs and localized context.2.1 Potential use cases Use cases identified using the World Economic Forum’s AI4AI report FIGURE 1 Farm-to-fork Market intelligence Quality and traceability Logistics Quality and traceability Supply-chain optimization Fintech, Electronic National Agriculture Market (eNWR) Warehousing supply cold chain Market linkage demand and price prediction Use cases/pilotsSmart farming Smart F-a-a-S Integrated nutrient management Crop health management Mechanization of farms Smart micro-irrigation SHC – rapid soil analysis Pest prediction Hyperlocal weather advice Smart insurance Yield prediction Crop input advisory and e-marketplace FrameworksIntelligent crop planning Macro crop-planning models Micro crop-planning models Varietal advice Extension planning Credit planning Macro irrigation planning Policy/incentive planning Inputs planning Sowing windows, sowing potential, sowing progress Four use cases detailed in the playbook FIGURE 2 Macro crop planning Solutions that allow government stakeholders and private-sector enterprises to recommend crops based on historic data, climate risks and global market trends.Rapid soil-health analysis Solutions that provide a quick assessment of soil- health parameters, enabling farmers to choose which crops to grow, and aiding nutrient processes during the production phases.AI-enabled pest prediction/control Solutions that enable farmers and other stakeholders to forecast pest attacks in advance and manage ongoing pest attacks efficiently.Smart marketplaces Solutions that connect farmers and traders through e-marketplaces while digitizing quality and quantity assaying, eliminating information asymmetry.Notes: See Artificial Intelligence for Agriculture Innovation Future Farming in India 15