India’s agricultural sector presents a complex environment for artificial intelligence. Diverse crops, soil conditions, weather patterns, farming practices and fragmented landholdings make it difficult to develop AI systems that work consistently across regions. Limited connectivity, multiple languages and gaps in farm-level data add to the challenge.
However, this complexity could also create an opportunity for India to develop agricultural intelligence with applications beyond its domestic market. In this episode of the Analytics Insight Podcast, host Priya Dialani speaks with Vinay Nair, Founder and CEO KhetiBuddy , about how AI can improve agricultural decision-making, strengthen supply chains and support farmers. Nair discusses the importance of reliable farm data, last-mile accessibility, responsible AI deployment, and India's potential to become a global exporter of agricultural intelligence. Here are the key excerpts:
What makes Indian agriculture a challenging environment for AI, and what can AI systems learn from it?
India is one of the hardest agricultural environments in the world in which to build AI. But that is precisely what makes it one of the best environments for developing robust systems.
India included close to 146 million operational farm holdings, according to the 10th Agricultural Census, with the majority comprising smallholdings of less than two acres. The country also has around 15 agroclimatic zones, further divided into 72 subzones. Conditions range from Himalayan valleys to coastal plains and arid plateaus.
What agricultural data is most valuable for building intelligent systems, and where are the biggest gaps?
The most valuable data connects farm-level realities with business decisions. This includes field-level input application records, agronomist observations, crop growth-stage tracking and actual yields compared with estimates.
Such information helps procurement teams make better purchasing decisions and enables sustainability teams to produce more credible disclosures. The biggest gap in India is not the technology required to collect data. It is the lack of structured, verified, farmer- and farm-identity-linked data that remains consistent across seasons.
How can agricultural AI overcome the last-mile challenge for farmers?
The key lesson is to stop designing technology around the farmer being the direct user of an interface and instead focus on making the farmer the beneficiary of its output.
Farmers do not necessarily need to open an application to benefit from AI. Field agents, agronomists and cooperative extension workers can serve as the interface between technology and farming communities. Farmers can receive recommendations through voice messages in their languages, guidance from trusted local professionals or in-person explanations of recommended practices.
We have seen field data completeness increase from around 40% to approximately 85%, not because we changed the technology, but because we changed who was accountable for capturing the data and embedded that responsibility into existing workflows.
Can India move from adopting AI to creating agricultural intelligence for global markets?
I think India is already doing that, and agriculture is one of the clearest examples. AI systems developed to handle Indian farming conditions can address challenges that other developing agricultural economies face as they digitise. Agricultural AI could become an important area in which India creates domain-specific intelligence for international use. The opportunity extends beyond implementing AI to developing systems that understand local farming conditions and can adapt to different agricultural economies.
What safeguards are necessary when AI influences decisions that affect farmers' livelihoods?
This is a critical question that the industry needs to address more seriously. When AI recommends a pesticide application, advises a farmer to sell produce or influences an agribusiness's decision to remove a supplier, the consequences can directly affect people's incomes.
Farmers and agronomy teams must understand why an AI system has made a recommendation. Black-box decisions are not acceptable when livelihoods are at stake. AI should identify risks and provide recommendations, but qualified professionals should make the final decisions when the consequences are significant.
What could the Indian farm of 2035 look like with integrated AI and emerging technologies?
The farm itself may look similar, but the intelligence surrounding it could be transformed. By 2035, I expect enterprise agribusinesses operating at a meaningful scale to use continuous satellite- and sensor-based monitoring across their contracted farmland.
AI will help field agents identify where to go, what to inspect and which recommendations to consider before reaching a farm. Soil health and carbon data could automatically flow into sustainability reporting systems connected to global buyers and compliance platforms. The interfaces may change, but the relationships will remain important. The objective is to make advanced intelligence accessible without requiring every farmer to become a technology expert.
Can agricultural intelligence become India's next major export?
Agronomic models trained on Indian farming conditions, multilingual advisory platforms and farm data infrastructure could become exportable products. Developing economies could deploy these solutions without having to build every component from scratch.
If India approaches this opportunity with the ambition it brought to IT services and pharmaceutical generics, agricultural intelligence could become a significant global export category. The foundation for that opportunity is being built today.
Listen to the full discussion on the Analytics Insight Podcast.