E-Paper
AI, agriculture and the architecture of Viksit Bharat 2047
This article is authored by Suman S, director, Rural Livelihoods, Dr. Reddy’s Foundation and Mukesh Kestwal, chief innovation officer, IIT Ropar (i-Hub-AWaDH).
As New Delhi hosts the India AI Impact Summit 2026, the central question before India is not whether Artificial Intelligence (AI) will shape the future of agriculture, but whether we will design that future with strategic clarity and scientific depth. Agriculture still sustains nearly half of India’s population. If Viksit Bharat 2047 is to be more than a slogan, the transformation of Indian agriculture through AI must be systemic, inclusive and grounded in robust digital infrastructure. AI-led agricultural transformation sits at the heart of Viksit Bharat’s pillars of inclusive growth, environmental sustainability, and future-ready jobs and skills.
Over the past decade, the Government of India has quietly laid foundational building blocks. Under the Digital Agriculture Mission, the creation of AgriStack and its registries, including geo-referenced village maps, crop sown registry and farmer registry, has begun to establish a single source of truth for land, identity and cropping patterns. As of February 2026, more than 8.48 crore Farmer IDs have already been generated, enabling seamless integration with Direct Benefit Transfer systems such as PM Kisan, crop insurance, credit delivery and procurement mechanisms. This is not merely digitisation. It is institutional architecture.
The Krishi Decision Support System integrates satellite imagery, soil health data, weather analytics and GIS layers to enable evidence-based advisories, reducing input misallocation, delays in response, and preventable crop losses. The National Pest Surveillance System uses AI and machine learning to identify pest infestations across 65 crops and more than 400 pest categories, currently supporting over 10,000 extension workers and enabling early intervention before outbreaks escalate. Kisan e Mitra, the AI-powered multilingual chatbot, has already responded to over 95 lakh farmer queries and handles thousands daily, narrowing information asymmetry at scale. The Seed Authenticity Traceability and Holistic Inventory platform is creating a National Seed Grid to unify stakeholders under a single digital ecosystem, improving transparency and minimising leakages in seed distribution. These are not incremental interventions. They represent the early contours of a national agricultural intelligence system.
Yet ambition must be matched by realism. AI in agriculture cannot be reduced to chatbots and dashboards. The deeper challenge lies in ensuring interoperability, data integrity, last-mile usability and farmer trust. A fragmented digital landscape risks becoming a collection of pilot projects rather than a cohesive transformation engine. If India is to truly leverage AI for agriculture, five structural shifts are necessary: interoperable public digital infrastructure, high-quality and verifiable datasets, farmer-centric design, institutional capacity-building, and accountable governance frameworks.
First, data quality and governance must become non-negotiable. AI systems are only as reliable as the datasets that train them. Plot-level crop data, soil profiles, microclimate readings and pest imagery must be standardised and continuously validated. A transparent national protocol like National Agri-AI Standard for agricultural data-sharing, privacy and algorithmic accountability is urgently required. Without clear AI standards and audit mechanisms, scalability will remain fragile.
Second, water intelligence must be integrated with AI-led agronomy. The Per Drop More Crop initiative under the National Mission for Sustainable Agriculture has expanded micro irrigation with substantial financial assistance to farmers. However, the next frontier is predictive irrigation. ICAR’s IoT-based irrigation pilots should be integrated with real-time weather models and soil moisture sensors to create adaptive irrigation systems that automatically optimise water and fertiliser use by reducing at least 10–15 % water use in pilot districts. In a climate-stressed future, AI-driven water management is not optional. It is existential.
Third, AI must move from advisory to execution. The Sub Mission on Agricultural Mechanisation has distributed over 21 lakh machines (like harvesters, planters, residue management equipment) and established thousands of Custom Hiring Centres and high-tech hubs. The logical next step is to connect these assets through intelligent platforms that match machine availability with crop calendars, soil conditions and weather windows. Imagine a district AI cell jointly triggering irrigation advisories, pest alerts and credit top-ups after a predicted dry spell. AI can optimise mechanisation logistics, reduce idle capacity and enhance productivity, particularly for small and marginal farmers.
Fourth, convergence must replace duplication. The Prime Minister Dhan Dhaanya Krishi Yojana seeks to integrate 36 schemes across 11 departments in 100 districts. This convergence framework should become the model for AI deployment. AI models trained in isolation within one scheme will fail to unlock systemic value. Inter-departmental data sharing, unified analytics dashboards and district-level AI cells can ensure that crop insurance, irrigation, mechanisation, soil health and market access are aligned around a common intelligence backbone.
Fifth, capacity building is critical. For instance, training at least 10 lakh frontline workers in AI-enabled advisory over the next five years can be instrumental. AI adoption cannot be technocratic. Extension workers, women farmers, FPO leaders and agri entrepreneurs must be trained not only to use digital tools but to understand their logic and limitations. The credibility of AI in rural India will depend on human intermediaries who can translate data into trust.
The economic multiplier effects are significant. Precision pest detection reduces input costs. Satellite-driven crop estimation improves procurement planning, digital crop surveys enhance credit scoring and insurance underwriting. Seed traceability strengthens supply chains. When combined, these interventions create a more resilient ecosystem benefiting farmers, agri startups, input manufacturers, insurers, logistics providers and financial institutions, potential savings and increased income per acre
However, India must also confront structural constraints or regional heterogeneity. Rural broadband gaps, device affordability, linguistic diversity and algorithmic bias can widen inequality if unaddressed. AI solutions must be designed for low-bandwidth environments, multilingual contexts and heterogeneous farm sizes. Open APIs and public digital infrastructure will be essential to prevent monopolisation of agricultural data by a few large platforms.
The science must remain rigorous. Climate models should be locally calibrated using long-AICRP data. Pest detection algorithms must be periodically retrained with new datasets. Crop yield prediction models must incorporate variability in soil fertility and water stress. Collaboration between ICAR, state agricultural universities, private agri-tech firms and global research institutions can create a continuous innovation loop.
India’s agriculture is no longer defined solely by production targets. It is defined by resilience, sustainability and income security. AI offers the possibility of transforming agriculture from a reactive system to a predictive one. But prediction without institutional reform is insufficient. Digital Public Infrastructure must be accompanied by regulatory clarity, ethical AI standards and measurable impact evaluation frameworks.
Viksit Bharat 2047 demands a shift from incremental reform to systemic design. Agriculture, often perceived as traditional, can become the most technologically integrated sector in the economy if intelligence is embedded across the value chain from seed to market. The AI Impact Summit is an opportunity to move beyond announcements toward architectural thinking.
The future of Indian agriculture will not be determined by how many tools we deploy, but by how intelligently we integrate them. If we align science, policy, technology and farmer agency under a unified strategic framework, AI can become not just a technological intervention, but a catalyst for rural prosperity and national transformation.
This article is authored by Suman S, director, Rural Livelihoods, Dr. Reddy’s Foundation and Mukesh Kestwal, chief innovation officer, IIT Ropar (i-Hub-AWaDH).
