Why in News?
The 2nd meeting of the UN Global Initiative on AI for Agri-Food Systems (GI-AI4FS) was held at the India Mobile Congress (IMC) 2026 in New Delhi. The event was jointly hosted by the Telecommunication Engineering Centre (TEC) under the Department of Telecommunications and the Indian Council of Agricultural Research (ICAR).
The meeting emphasised the need for trustworthy, interoperable and farmer-centric AI frameworks to strengthen international cooperation on digital governance.
What is GI-AI4FS?
- About: A United Nations-backed multilateral platform aimed at harnessing Artificial Intelligence (AI) and the Internet of Things (IoT) to transform global agri-food systems.
- SDG Alignment: Directly supports SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production).
- Coordinating Agencies: Coordinated by the International Telecommunication Union (ITU) in partnership with the FAO, IFAD and the World Food Programme (WFP).
- Core Objective: Promote trustworthy, interoperable, farmer-centric AI solutions while strengthening cooperation on governance, standards and capacity building.
- India's Role: Hosting the 2nd global meeting (the first was at FAO headquarters in June 2026) reinforces India's emerging role as a convenor on AI governance and Digital Public Infrastructure (DPI) for the Global South.
How is AI Revolutionising Indian Agriculture?
1. Precision Farming
- Shifts agriculture from "input-intensive" to "intelligence-intensive" by converting data from IoT sensors, GPS and drones into localised, farm-level insights.
- Project Farm Vibes (Microsoft, Maharashtra): Boosted crop yields by 40% while cutting fertilizer use by 25%.
2. Climate-Responsive Advisory
- AI blends historical rainfall data with advanced modelling (e.g., NeuralGCM) to predict micro-level weather patterns.
- An AI-based pilot for local monsoon onset forecasting for Kharif 2025 reached 3.88 crore farmers across 13 states; 31–52% of surveyed farmers adjusted land preparation and sowing decisions.
3. Resource Optimisation
- Farm Again (Tamil Nadu): Indigenous solar-powered sensors costing just Rs 2.5 lakh deployed across 4,000 acres — doubled coconut yields and saved 4,00,000 cubic metres of water and 20,000 tonnes of CO2-equivalent emissions annually.
4. Early Pest and Disease Detection
- National Pest Surveillance System (NPSS) uses AI image recognition covering 66 crops and over 432 pest types, delivering real-time advisories to more than 10,000 extension workers — eliminating dependence on lab infrastructure.
5. Climate Resilience and Yield Estimation
- YES-TECH (Yield Estimation System based on Technology): AI and remote sensing for accurate yield assessment; fully adopted by Madhya Pradesh.
- CROPIC: Mobile tool for uploading geotagged, time-stamped crop images for evidence-based damage evaluation.
- Backed by the WINDS platform (hyper-local weather data) and an AI-driven WhatsApp chatbot, these have made Pradhan Mantri Fasal Bima Yojana (PMFBY) a credible safety net.
6. Improving Price Realisation
- AI predictive analytics using e-NAM and AGMARKET datasets help farmers time sales and avoid distress selling.
India's Key AI-Driven Agriculture Initiatives
| Initiative | Key Features |
|---|---|
| AgriStack | Unique digital Farmer ID linked to land, livestock and crop records. Over 7.63 crore IDs generated (target: 11 crore by 2026-27). Drives the Digital Crop Survey covering 23.5 crore plots (Rabi 2024-25). |
| Krishi Decision Support System (KDSS) | Integrates satellite, weather and soil data; 29 million hectares mapped by the Soil and Land Use Survey of India (SLUSI); generates digital crop maps, yield estimates and drought assessments. |
| Bharat-VISTAAR (Virtually Integrated System to Access Agricultural Resources) | Multilingual AI tool proposed in the Union Budget 2026-27; integrates AgriStack portals and ICAR packages into a unified advisory system. |
| Kisan e-Mitra | AI-powered voice-enabled chatbot in 11 regional languages; answered over 93 lakh queries (as of December 2025), handling 8,000 daily queries on PM-KISAN and PMFBY. |
| Nationwide Soil Resource Mapping | SLUSI-led high-resolution (1:10,000) village-level soil inventories using a 'camp-mode' approach; ~29 million ha mapped (target: 142 million ha) across six funded states. |
| Agri-Tech Startups | Under RKVY, Knowledge Partners and RKVY Agribusiness Incubators (R-ABIs) give up to Rs 5 lakh (idea stage) and Rs 25 lakh (seed stage); 2,282 agri-startups supported in AI, drones and robotics. |
IMPACT AI Framework
The "Future Farming in India: AI Playbook for Agriculture" (October 2025), released with WEF, IndiaAI and BCG, rests on three pillars:
- Enable: Clear AI strategies, supportive policies and DPI such as AgriStack for scalable adoption.
- Create: Collaboration among start-ups, tech providers and institutions like ICAR to design and validate localised AI solutions.
- Deliver: Integrating AI into extension services with field feedback so solutions reach small and marginal farmers.
Challenges of AI in Indian Agriculture
- Smallholder Data Gap and Algorithmic Bias: 86% of landholdings are small and marginal (under 2 hectares); AI models trained on large mechanised global farms produce skewed advisories for India's fragmented, multi-cropped, rain-dependent micro-climates.
- Trust Deficit and Information Clutter: The WEF 'Future Farming in India' (2025) report flags technology adoption anxiety and fear of abandoning proven traditional practices without physical validation.
- Cadastral Digitisation Discrepancies: A 2026 parliamentary report notes 97.4% of cadastral maps are digitised, but un-updated spatial records, unresolved tenancy disputes and lack of conclusive titling hinder a seamless 'single source of truth' under DILRMP.
- Data Sovereignty Risks: Absence of agricultural data protection may enable predatory pricing and discriminatory credit scoring.
- CapEx Barrier: High initial capital costs for IoT sensors, drones and autonomous machinery risk concentrating benefits among wealthy landowners.
- AI's Water Footprint: Water-intensive data centres may consume 9.3 trillion litres annually by 2030.
- AI-Driven E-Waste: Rapid hardware replacement could generate 2.5 million tonnes of e-waste annually by 2030, risking soil and groundwater contamination.
Way Forward: Inclusive AI Adoption
- Farming-as-a-Service (FaaS): Institutionalise AI access via Custom Hiring Centres (CHCs), FPOs and Kisan Drones — a pay-per-use 'Uberisation' of agriculture, including drones operated by Women SHGs.
- 'Phygital' Extension Network: Merge Krishi Vigyan Kendras (KVKs) with digital systems; equip Krishi Sakhis (aligned with Lakhpati Didi) with AI diagnostic tablets.
- Scale DPI: Expedite full rollout of AgriStack and the Digital Crop Survey across all districts by FY 2026.
- Capacity Building: Upskill extension workers into 'digital agropreneurs'.
- Global Collaboration: Use GI-AI4FS to share open-source AI models and set global standards for agricultural data governance.
- Agri-Data Protection Architecture: Extend the Data Empowerment and Protection Architecture (DEPA) to agriculture, giving farmers consent-based control over farm-level data.
Conclusion
AI can transform Indian agriculture into a climate-resilient, data-driven sector, but inclusive technology access, agricultural data protection and a human-centric phygital approach are essential to empower smallholders and strengthen food security.
Previous Year Questions
- Prelims (2021): Climate-Smart Village approach and CGIAR/ICRISAT — Ans: (d).
- Mains (2017): Explain various agricultural revolutions after Independence and their role in poverty alleviation and food security.