India has the AI (artificial intelligence) money, models, and national infrastructure. Maharashtra, a major agricultural state, provides the operating manual: data exchange, geospatial intelligence, farmer-focused advice, traceability, and outcome-driven scale-ups. Combine them, and AI translates into higher yields, lower risk, and better prices for farmers.
Recently announced, Maharashtra’s new Agri-AI policy takes a unique approach to agricultural innovation in India. Instead of flashy apps, it starts with shared infrastructure. In the new Agri-AI policy, the state suggests an Agriculture Data Exchange (ADeX) as a federated, consent-based platform. It offers access to weather, soil, crops, market, and post-harvest datasets through standard APIs. It includes identification, authorisation, and consent to ensure responsible innovation. A key component of the policy is the Sandboxing Environment, which allows start-ups and public agencies to test models using anonymised or synthetic data that mimic real farming conditions before any money is spent on field deployment. Together, ADeX and Sandbox form a practical digital infrastructure that lowers costs, reduces risks, and speeds up scaling.
The second key component of MahaAgri-AI policy is geospatial intelligence as a shared public benefit. Maharashtra’s plan sets up a unified, AI-powered Remote Sensing & Geospatial Intelligence Engine. This engine processes satellite, UAV, and IoT data to estimate acreage and yields, map drought and flood risks, assess vegetation and soil moisture indices, track irrigation assets, and verify PMFBY compliance. Since all departments use the same source of truth through modular APIs and field-off ..
While most initiatives in the country stop at providing information, Maharashtra moves forward with a farmer-focused advisory method. Through VISTAAR (Virtually Integrated System to Access Agricultural Resources), the policy offers real-time, hyperlocal, multilingual guidance. This advice is delivered through chat, voice, and vernacular content tools, along with simulation features that let farmers virtually test spacing, crop varieties, nutrients, or irrigation options before making decisions. It is designed to connect with national systems, such as AgriStack for data exchange and BHASHINI for language support, ensuring advice reaches farmers in their own context and language, instead of being a generic broadcast.
A fourth focus aims directly at premiums: AI-powered farm-to-fork traceability and certification. This includes QR-coded batches, digitised records, and AI anomaly checks. It starts with export-focused chains like grapes, bananas, pomegranates, and specialty rice, expanding as systems evolve. This approach helps states move from generic value-chain discussions to actual price realisation that smallholders can experience.
However, none of these can succeed without strong institutions. For this, Maharashtra, in the new policy, proposes a three-tier structure: a State Level Steering Committee (SLSC), a State Level Technical Committee (SLTC), and a full-time AI & Agritech Innovation Centre. The centre collaborates with stakeholders to identify challenges, organises hackathons, incubates solutions with state agriculture universities, and promotes the adoption of validated tools. Government support flows through Direct Benefit Transfer (DBT), with approvals managed by the SLSC to ensure only field-tested, impactful ideas are scaled. It functions as a conveyor belt for moving from pilot projects to larger implementations, rather than a “launch and forget” press release.
Importantly, the policy invests directly in key areas. The Maharashtra government allocates an initial Rs 500 crore, covering expenses for ADeX, Sandbox, Cloud, VISTAAR, the geospatial engine, traceability, capacity building, and a Global AI in Agriculture Conference & Investor Summit to attract talent and investment. A significant portion is set aside to co-finance projects within the state’s pipeline, with a mid-term review to make adjustments. This approach supports public goods and reduces risks for private innovation at the same time.
If New Delhi is looking for a national model, now is the perfect time. On March 7, 2024, the Union Cabinet approved the IndiaAI Mission, which provides funding for public AI compute (over 10,000 GPUs), indigenous models, start-up investment, and responsible AI tools—for Rs 10,300 crore. This represents the resources for AI: compute, models, and funding. What Maharashtra’s new Agri-AI policy offers is the practical application where those capabilities lead to higher farm incomes, lower risks, and ..
Meanwhile, the IDEA/AgriStack framework is taking shape, demonstrating how farmers’ data can be shared across different schemes, while e-NAM continues to strengthen market links. The official dashboard currently features over 1,400 mandis onboarding. Add BHASHINI, the government’s language AI platform, which provides voice-first access to every Indian language, giving India the national framework to link state-level innovations. Maharashtra’s policy views these as essential components, not just buzzwords—an approach worth replicating.
Finally, for any AI program to gain public trust, it must ensure privacy and fairness. The Digital Personal Data Protection Act, 2023, offers India a framework for consent and purpose limitations on personal data in public services. Maharashtra integrates this by incorporating consent-driven access, model benchmarking, and controlled sandboxing into its design—safeguards that the Union should mandate for any tools funded by public money.
What should a national AgriAI program do tomorrow?
First, adopt ADeX and the Sandbox as the standard for state-level agricultural data exchanges. Avoid reinventing the wheel; formalise Maharashtra’s consent, discovery, authorisation, and identity interfaces along with benchmark protocols. This way, innovators everywhere follow the same rules.
Second, create a shared National Agri-Geospatial Service based on the Maharashtra model. Ensure it is accessible to all states under the same conditions for yield estimation, drought alerts, and insurance validation. This ensures disaster relief and PMFBY payouts rely on solid evidence rather than anecdotes.
Third, connect VISTAAR to real-world outcomes, not just messages. Fund advisory services that lead to adoption, input savings, and quality premiums—and require BHASHINI integration so voice-first, multilingual assistance becomes the standard interface for smallholders.
Fourth, develop a nationwide traceability system for export-focused and food-safety-critical value chains, starting with grapes, bananas, spices, and basmati. Use Maharashtra’s QR-linked workflow as the standard and coordinate with the Agricultural & Processed Food Products Export Development Authority (APEDA) and import market requirements to minimise rejections and enable premiums for farmers' producer organisations.
Fifth, replicate the procurement process. Copy the Innovation Centre, SLTC, and SLSC pipeline, publish leaderboards of successful models, and route funding through DBT. This way, the best-performing tools advance from pilot projects to larger implementations within set time frames.
If the Union government combines IndiaAI’s computing power and funding with Maharashtra’s execution abilities, India could shift from pilot projects to real results this decade. The headline writes itself: 100 million farmers on AI-ready infrastructure by 2030, with clear improvements in productivity, resilience, and price discovery that don’t need a press release to notice.
Source Name : Economic Times