Agriculture contributes nearly 18% to India’s GDP and supports about 45 – 50% of the workforce, making it central to food security and rural livelihoods. With rising climate risks, fragmented landholdings, and market inefficiencies, Artificial Intelligence in Indian agriculture boosts precision farming, climate resilience and market access, but faces land, data and digital divide challenges.
Role of AI in revolutionising Indian agriculture:
1. Precision Agriculture:
- AI integrates satellite imagery, drones, IoT sensors, and GPS data to deliver farm-level advisories enabling targeted irrigation and fertiliser application, reducing input costs.
- AI-driven farm mechanisation improves labour efficiency amid rural labour shortages.
- For example, ICAR’s IoT-based irrigation systems automate water use based on real-time soil moisture data.
2. Climate-Smart Agriculture:
- AI analyses historical and real-time climate data to predict rainfall variability and temperature changes.
- It supports adaptive cropping patterns and irrigation planning besides providing early warnings for extreme weather events.
- For e.g., AI-powered advisories under the National Pest Surveillance System enable preventive action against climate-induced pest outbreaks.
3. Soil health enhancement:
- Deep learning models analyse soil data from remote sensing and field samples.
- AI supports the Soil Health Card Scheme through better soil diagnostics enabling optimal fertiliser use, reducing environmental degradation.
- For example, Nationwide Soil Resource Mapping by SLUSI strengthens AI-based soil profiling.
4. Market intelligence information:
- AI-driven predictive analytics leverage data from platforms like e-NAM and AGMARKET.
- AI forecasts price trends and demand patterns reducing distress sales and improves farmer bargaining power.
- For example, AI tools assess mandi-level data to recommend optimal sale timing.
5. Productivity enhancement:
- YES-TECH, CROPIC, and WINDS improve yield estimation under PMFBY while AgriStack creates digital IDs linked to land records for targeted service delivery.
- For e.g., Budget 2026–27 launched Bharat-VISTAAR to integrate AgriStack with AI systems for a unified digital interface.
Challenges in AI adoption:
1. Fragmented Landholdings: 86% of farmers are small and marginal while average landholding size is just 1.08 hectares, limiting scale economies for AI-based mechanisation.
2. Digital & Infrastructure Gaps: Out of 5.97 lakh inhabited villages, around 25,000+ villages lack mobile and internet connectivity, restricting access to AI platforms.
3. High Implementation Costs: Lack access to affordable credit and risk capital deter implementation. For e.g., despite agri-tech growth, India’s AI-in-agriculture market was only USD 1.7 billion in 2023, reflecting limited penetration.
4. Data Gaps: AI relies on high-quality historical and real-time data. Without robust datasets, predictive accuracy declines, affecting advisories.
5. Skill & Awareness Deficit: Limited digital literacy among farmers restricts effective usage as only a small proportion of farmers regularly use digital agricultural advisories.
Way forward:
1. Strengthen digital data ecosystem: Operationalise AgriStack and IDEA (India Digital Ecosystem for Agriculture) for seamless data integration.
2. Improve rural connectivity: Expand BharatNet and PM-WANI to bridge the digital divide.
3. Promote region-specific AI innovation: Leverage National AI Centres of Excellence to develop customised agro-climatic models.
4. Capacity building & skilling: Expand training under NeGPA and FutureSkills PRIME and promote Hybrid Agricultural Intelligence (HAI), combining AI with indigenous knowledge.
5. Financial incentives: Provide subsidised loans and incentives under the Digital Agriculture Mission (2021–25) for startups and farmer producer organisations (FPOs).
Conclusion:
AI has the potential to shift Indian agriculture from input-intensive to intelligence-driven farming by enhancing productivity, sustainability, and profitability. By aligning with India’s Digital Public Infrastructure model, AI can become a cornerstone of a climate-resilient and income-secure agricultural transformation.
‘+1’ Value Addition:
- Microsoft–ICRISAT, Telangana: AI-based sowing advisories increased groundnut yields by 10–30%without increasing input cost.
- NITI Aayog (AI in Agriculture Report): Precision agriculture can improve productivity by 15 – 20% and reduce input costs by 10–15%.
- PMFBY – YES-TECH: Use of AI, drones, and satellite imagery for yield estimation has reduced claim settlement delays.
- AI Agriculture Market Projection: Expected to grow from USD 1.7 billion (2023) to USD 4.7 billion by 2028, at 23%+ CAGR, reflecting rapid adoption.
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