AI to Power India’s Next Agricultural Transformation

Syllabus: GS3/ Agriculture; Science and Technology

Context

• The Union Minister of Science and Technology, at the AI4Agri 2026 Summit in Mumbai said that India’s next agricultural revolution will be driven by artificial intelligence.

How is AI in Agriculture Critical for India?

• Indian agriculture faces structural constraints such as fragmented landholdings, climate variability, price volatility, and low productivity.
• Climate change has increased the frequency of extreme weather events, making predictive technologies essential.
• Small and marginal farmers (over 85% of total farmers) require affordable, data-driven advisory systems.
• Efficient risk management and market access are necessary to enhance farmer incomes in line with national development goals.

Role of Artificial Intelligence in Agriculture

• Soil Health Diagnostics: AI uses deep learning and image recognition to monitor soil health by analysing signals from satellite imagery, drone-based observations, and farm-level images.
• Farm Mechanisation Efficiency: AI technologies, such as machine learning, drone applications, and remote sensing, are revolutionizing farming efficiency.
  • In horticulture, where crops require continuous monitoring across multiple growth stages, AI-based systems offer round-the-clock surveillance of high-value crops. 
• Price Realisation for Farmers: AI-driven predictive analytics leverage large datasets from platforms such as e-NAM, AGMARKET, the Agricultural Census, and the Soil Health Card programme to assess price movements, arrival trends, and regional demand patterns.
• Climate-Smart Agriculture: AI can predict weather patterns and provide early warnings for extreme weather events, enabling farmers to take preventive measures.
  • Integration with platforms like WINDS (Weather Information and Network Data System) strengthens risk assessment.

Government Initiatives in AI-Driven Agriculture

• Kisan e-Mitra, launched in 2023, is a voice-enabled, AI-powered chatbot designed to support farmers by answering queries on key government schemes, including PM Kisan Samman Nidhi, the Kisan Credit Card, and the Pradhan Mantri Fasal Bima Yojana. 
  • The platform operates in 11 regional languages and currently addresses over 8,000 farmer queries each day.
• The National Pest Surveillance System (NPSS), launched in 2024, utilises AI and Machine Learning (ML) to enable early detection of pest infestations and crop diseases.
• The Union Budget 2026-27 proposed Bharat-VISTAAR, a multilingual AI tool to integrate the AgriStack portals and the ICAR package with AI systems.
  • AgriStack is a core component of the Digital Agriculture Mission, providing farmers with a unique digital identity (Farmer ID) linked to land records, livestock ownership, crops cultivated, and benefits availed, enabling secure identification and access to agricultural services.
  • The ICAR (Indian Council of Agricultural Research) package refers to scientific, evidence-based farming practices and crop management advice.
• AI-Enabled Crop Insurance:
  • CROPIC (Collection of Real-Time Observations and Photographs of Crops) uses geotagged, time-stamped images uploaded via mobile apps, enhancing transparency in crop damage assessment.
  • YES-TECH (Yield Estimation System based on Technology) uses remote sensing and AI analytics for scientific yield estimation.
• The Krishi Decision Support System (KDSS) integrates data from multiple sources, to generate comprehensive analytical outputs such as digital crop maps, soil maps, yield estimates, and drought and flood monitoring assessments.

Challenges in AI Adoption in Indian Agriculture

• Rural Connectivity Gaps: Small and marginal farmers often lack access to smartphones, IoT devices, or digital infrastructure, creating an access asymmetry.
  • Power supply disruptions in rural areas further constrain the effective use of AI-enabled devices.
• Data Privacy: AI systems rely on large datasets including land records, crop patterns, financial details, and yield data collected under platforms like AgriStack.
  • Absence of a clearly defined farmer-centric data ownership framework may lead to misuse or commercial exploitation of farm-level data.
• High Cost of Advanced Technologies: Precision agriculture tools such as drones, AI-based sensors, robotics, and automated machinery involve high initial capital investment.
  • Small landholdings (average size ~1–1.2 hectares) reduce economies of scale, making individual adoption financially unviable.

Way Ahead

• Artificial intelligence has the potential to transform Indian agriculture from a risk-prone livelihood into a data-driven, resilient, and profitable enterprise. 
• If supported by inclusive policies, robust infrastructure, and farmer-centric implementation, AI could usher in a new agricultural revolution comparable in impact to the Green Revolution, this time powered not by seeds and fertilizers, but by data and intelligence.

Source: PIB