Relevance: GS Paper 3 (Agriculture; Science & Technology; E-Technology in aid of farmers)

Source: The Hindu / PIB / NITI Aayog Reports

1. Context: From Tradition to Technology

India has emerged as a global leader in Artificial Intelligence (AI), ranking 3rd worldwide in AI competitiveness (Stanford Global AI Vibrancy Tool 2025). The integration of AI into the agrarian sector marks a pivotal shift from traditional “input-intensive” methods to a “data-driven, precision-based” ecosystem. This transition aligns with the vision of Sarvajan Hitay (Welfare for All), aiming to de-risk agriculture for the smallholder farmer.

2. Key Applications: Revolutionizing the Agrarian Landscape

AI is not just a buzzword but a functional tool currently transforming four major domains:

• Precision Farming & Resource Optimization:
  • Mechanism: AI utilizes data from GPS, IoT sensors, and satellites to determine site-specific inputs (water, fertilizer).
  • Impact: Instead of blanketing a field with urea, farmers apply it only where needed. This reduces input costs, minimizes environmental footprint, and improves productivity.
• Climate Resilience & Hyper-local Forecasting:
  • The Shift: Moving from “District-level” to “Village-level” forecasts.
  • Success Story: AI models (combining NeuralGCM and IMD data) provided monsoon onset forecasts to 3.88 crore farmers across 13 states.
  • Impact: Surveys indicated 31–52% of farmers adjusted their sowing and land preparation dates based on these advisories, saving crops from erratic weather.
• Soil Health & Pest Management:
  • Soil: AI analyzes satellite/drone imagery to detect nutrient deficiencies, eliminating the need for slow physical labs.
  • Pests: The National Pest Surveillance System (NPSS) uses image analytics for early detection. It currently supports 66 crops and detects over 432 pest types via a simple mobile photo.
• Price Discovery & Market Intelligence:
  • function: AI strengthens demand-supply forecasting by integrating data from e-NAM and Agmarket.
  • Benefit: It reduces distress sales by predicting price movements, empowering farmers to decide when to sell.

3. Government Initiatives: Building the Digital Backbone

The government is constructing a Digital Public Infrastructure (DPI) for agriculture to ensure these benefits reach the last mile.

4. Case Study: Scalable Precision Farming

• Farmer: Rajaratnam Kanakarajan (Tamil Nadu).
• Intervention: Adopted an AI-enabled system by startup Farm Again using solar-powered sensors for real-time irrigation monitoring.
• Outcome:
  • Yield: Doubled coconut yields.
  • Savings: Saved 4,00,000 cubic meters of water and 1.75 lakh kWh of energy annually.
  • Cost: Indigenous equipment cost ₹2.5 Lakh vs. ₹25 Lakh for imported alternatives.

5. The IMPACT Framework (WEF & NITI Aayog)

The “Future Farming in India” playbook outlines a roadmap for AI adoption:

1. Enable: Creating policies and data-sharing frameworks (AgriStack).
2. Create: Collaboration between startups and research bodies to design solutions.
3. Deliver: Ensuring last-mile connectivity and integrating AI into extension services.

UPSC Value Box 

Significance:

• Breaking the Cycle: AI helps break the cycle of low productivity and high risk in rainfed agriculture (which covers >50% of India’s net sown area).
• Lab to Land: Bridges the gap between scientific research (ICAR) and farm-level application.

Challenges:

• Digital Divide: While 3.88 crore farmers were reached, millions remain offline.
• Data Privacy: Aggregation of granular land data raises concerns regarding data sovereignty.
• Fragmented Landholdings: Small land sizes (avg. 1.08 ha) make expensive IoT sensors economically unviable without collectivization (FPOs).

Way Forward:

• Phygital Approach: Combine AI tools with physical extension workers (Krishi Sakhis) to build trust.
• Democratization: Focus on “AI for Humanity”—ensuring low-cost solutions for small and marginal farmers.

Summary

India is leveraging its AI prowess to transform agriculture from a gamble on the monsoon to a data-driven science. Through initiatives like AgriStack, Kisan e-Mitra, and National Pest Surveillance, the government is creating a digital ecosystem that empowers farmers with precise inputs, weather resilience, and market intelligence. However, success depends on bridging the digital divide and ensuring data privacy.

One Line Wrap: AI in agriculture is shifting the paradigm from “Input-Intensive” to “Knowledge-Intensive” farming.

” The integration of Artificial Intelligence in Indian agriculture represents a paradigm shift from ‘input-intensive’ to ‘knowledge-intensive’ farming.” Discuss this statement in the light of recent government initiatives like AgriStack and Digital Agriculture Mission. (10 Marks, 150 Words)

Model Hints

• Introduction: Define the shift—Input intensive (more fertilizer/water) vs Knowledge intensive (precise data on when and where to use).
• Body:
  • Precision: Explain how AI (YES-TECH, NPSS) optimizes inputs (Ref: Rajaratnam Case Study).
  • Access: Discuss Kisan e-Mitra breaking literacy barriers.
  • Infrastructure: Explain role of AgriStack (Farmer ID) and Krishi-DSS in policy making.
• Challenges: Mention Digital Divide and Small landholdings.
• Conclusion: Conclude with the goal of doubling farmers’ income and climate resilience.