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Recent Developments:

• The second meeting of the United Nations Global Initiative on AI for Agri-Food Systems (GI-AI4FS) was held on 10 October 2026 alongside the India Mobile Congress 2026 in New Delhi. It was jointly hosted by the Telecommunication Engineering Centre under the Department of Telecommunications and the Indian Council of Agricultural Research.
• Coordinated by the International Telecommunication Union (ITU) with the Food and Agriculture Organization, International Fund for Agricultural Development and World Food Programme, the initiative promotes trustworthy, interoperable and farmer-centric artificial intelligence (AI).
• The meeting followed an international AI hackathon held on 8–9 October 2026 and focused on responsible AI applications, common standards, international cooperation and practical agricultural innovations.
• India’s digital agriculture ecosystem is expanding through AgriStack, Kisan e-Mitra, the National Pest Surveillance System and Bharat-VISTAAR. Notably, Bharat-VISTAAR moved beyond the proposal stage when its first phase was launched in March 2026.

Global Initiative on AI for Agri-Food Systems:

Objectives and significance:

• GI-AI4FS is a United Nations-backed multilateral initiative that promotes the application of AI and the Internet of Things (IoT) across agricultural and food systems.
• Its objectives include developing interoperable digital solutions, strengthening international cooperation on AI governance and standards, building institutional capacity, and accelerating farmer-oriented innovations.
• It supports Sustainable Development Goal 2 (Zero Hunger) and Sustainable Development Goal 12 (Responsible Consumption and Production) by encouraging productive, resilient and resource-efficient food systems.
• India’s participation strengthens its potential role in shaping inclusive AI governance and digital public infrastructure, particularly for developing countries facing climate risks, fragmented landholdings and limited agricultural technology access.

Applications of AI in Indian Agriculture:

Productivity, climate resilience and resource efficiency:

• Precision farming: AI combines satellite imagery, global positioning systems, drones and field sensors to generate farm-specific recommendations. Targeted application of water, fertilisers and pesticides can reduce input costs, minimise environmental damage and improve productivity.
• Climate-responsive advisories: AI-based weather models use historical and real-time meteorological data to improve localised rainfall and monsoon forecasts. During the Kharif 2025 pilot, forecasts reached 3.88 crore farmers across 13 states, while surveys indicated that 31–52% of respondents adjusted land preparation or sowing decisions.
• Smart irrigation: Sensor-based systems monitor soil moisture and automate irrigation. A Farm Again initiative in Tamil Nadu demonstrated the potential of indigenous solar-powered sensors to double coconut yields while saving water and reducing emissions.
• Pest and disease surveillance: Image recognition can identify crop diseases and pests from photographs, enabling timely interventions and reducing avoidable crop losses. The National Pest Surveillance System (NPSS) supports 66 crops and more than 432 pest types, providing advisories to over 10,000 agricultural extension workers.
• Crop insurance: Technology-based yield estimation under YES-TECH, geotagged crop photographs through CROPIC, and weather information through the WINDS platform can improve crop-loss assessment, transparency and insurance-claim settlement under the Pradhan Mantri Fasal Bima Yojana.
• Market intelligence: AI can analyse price movements, arrivals and regional demand using information from e-NAM and AGMARKET. Better market intelligence can help farmers choose appropriate selling times and locations, reducing distress sales and improving price realisation.

India’s Digital Agriculture Initiatives:

Digital Public Infrastructure and advisory services:

• Digital Agriculture Mission: Approved in 2024 with an outlay of ₹2,817 crore, the mission aims to establish a comprehensive digital foundation for agricultural services using verified farmer, land and crop data.
• AgriStack: It creates digital Farmer IDs linked to relevant agricultural records and supports the Digital Crop Survey. By 27 November 2025, more than 7.63 crore Farmer IDs had been generated against a target of 11 crore by 2026–27; the crop survey covered more than 23.5 crore plots during Rabi 2024–25.
• Krishi Decision Support System: It integrates satellite imagery, weather, soil, water and crop information to support digital crop mapping, yield estimation, drought monitoring and evidence-based policy decisions.
• Bharat-VISTAAR: The Virtually Integrated System to Access Agricultural Resources is a multilingual, AI-powered platform launched in March 2026. It integrates agricultural information and scientific practices developed by the Indian Council of Agricultural Research to provide personalised advice on crop management, weather, market prices, pests and diseases, and soil health.
• Kisan e-Mitra: This voice-enabled AI chatbot answers queries about government schemes, including the Pradhan Mantri Kisan Samman Nidhi and crop insurance. By December 2025, it had answered over 93 lakh queries and was handling more than 8,000 queries daily in 11 regional languages.
• Soil resource mapping: The Soil and Land Use Survey of India is undertaking village-level soil mapping at a resolution of 1:10,000. Approximately 29 million hectares had been mapped against a target of 142 million hectares, supporting scientific land-use planning and sustainable agriculture.
• Agri-tech start-ups: Under the Rashtriya Krishi Vikas Yojana, eligible start-ups receive support of up to ₹5 lakh at the idea stage and ₹25 lakh at the seed stage. A total of 2,282 start-ups received financial and technical assistance between financial years 2019–20 and 2025–26.

IMPACT AI framework:

The Future Farming in India: AI Playbook for Agriculture, released in October 2025, presents a three-pillar framework for scaling responsible AI adoption.

• Enable: Establish supportive policies, clear AI strategies, data-sharing frameworks and digital infrastructure.
• Create: Promote collaboration among start-ups, technology providers, agricultural research institutions and the private sector to develop and validate locally relevant solutions.
• Deliver: Integrate AI into agricultural extension services, strengthen last-mile access and use farmers’ feedback to improve solutions.

Challenges of AI Adoption in Agriculture:

• Smallholder data gap and algorithmic bias: Small and marginal holdings account for approximately 86% of India’s operational landholdings. Models trained on large, mechanised farms may produce inaccurate recommendations for fragmented, multi-cropped and rain-dependent Indian farms.
• Digital divide and affordability: Limited connectivity, digital literacy, reliable electricity and access to smartphones can prevent smallholders from benefiting. High initial costs of sensors, drones and automated machinery may concentrate gains among wealthier farmers.
• Data privacy and sovereignty: Unregulated access to farm-level, landholding and financial data could enable discriminatory credit scoring, exploitative pricing and excessive corporate control over agricultural information.
• Land-record inconsistencies: Inaccurate or outdated cadastral maps, unresolved tenancy disputes and incomplete land-title records can undermine the reliability of digital farmer databases and targeted service delivery.
• Trust deficit: Farmers may hesitate to replace established practices with algorithmic advice, particularly when recommendations are difficult to understand or lack local field validation.
• Environmental costs: Energy-intensive data centres and the growing volume of electronic waste may increase emissions, consume water and contaminate soil or groundwater if equipment is improperly discarded.
• Interoperability and accountability: Disconnected platforms, incompatible datasets and unclear responsibility for incorrect recommendations can reduce the effectiveness of digital agricultural services.

Measures to Promote Inclusive AI-Driven Agriculture:

• Farming-as-a-Service: Expand Custom Hiring Centres, Farmer Producer Organisations and drone services so smallholders can rent AI-enabled equipment on a pay-per-use basis rather than purchase costly machinery. Women’s self-help groups can also be supported as service providers.
• Phygital agricultural extension: Combine digital advisories with human assistance through Krishi Vigyan Kendras, agricultural extension workers and community resource persons such as Krishi Sakhis. Local validation and demonstrations can strengthen trust and overcome digital-literacy barriers.
• Farmer-centric data governance: Apply consent-based data-sharing principles, purpose limitation, secure access controls and grievance redressal to agricultural platforms. Farmers should have meaningful control over how their data is accessed and used.
• Inclusive digital infrastructure: Improve rural connectivity, local-language interfaces, offline access and assisted digital services, while ensuring that traditional extension services remain available.
• Reliable and accountable AI: Validate models across different agro-climatic zones and cropping systems, disclose uncertainty and establish mechanisms for correcting harmful or inaccurate recommendations.
• Capacity building and innovation: Train extension workers to interpret AI outputs, support locally developed agricultural technologies and expand affordable access to research, finance and markets.
• International cooperation: Use GI-AI4FS to share validated tools, open-source models, technical standards and best practices for agricultural data governance, while respecting national priorities and local farming conditions.

Conclusion:

AI can make Indian agriculture more productive, climate-resilient, resource-efficient and market-oriented. However, technological deployment alone cannot ensure inclusive development. Reliable data, affordable access, farmer consent, human-centred extension and environmental safeguards are essential to prevent digital exclusion and ensure that smallholders benefit from agricultural innovation.

Value Addition for UPSC:

• GS Paper III: Agriculture, technology in agriculture, crop insurance, food security, inclusive growth and environmental sustainability.
• GS Paper II: Digital governance, international institutions, public service delivery and accountability.
• Key concept: Digital Public Infrastructure for Agriculture combines trusted digital identities, interoperable datasets and accessible services to improve agricultural decision-making.
• Analytical perspective: AI should complement farmers’ knowledge and public agricultural extension rather than replace them; its success should be measured through improved farm incomes, reduced risk, resource conservation and equitable access.
• Potential Mains question: Discuss the opportunities and challenges associated with the use of artificial intelligence in Indian agriculture. Suggest measures to ensure that AI-driven agricultural transformation remains inclusive and sustainable. (250 words)