India feeds over a billion people. Yet the farmer who makes that possible navigating unpredictable monsoons, volatile input costs, pest pressures, and shrinking margins does so with diminishing support. The average Indian farm has contracted steadily over decades, reaching just 1.08 hectares, according to the Agriculture Census. The number of operational holdings has grown to 146.5 million, but the system designed to support those farmers has not kept pace.
At the heart of this is a structural gap in human expertise. India's extension worker-to-farmer ratio currently falls well below national guidelines which prescribe 1:1,100 in irrigated areas and 1:400 in hilly regions. The agronomist, the extension officer, the cooperative field worker these are the knowledge carriers whose advice shapes what a farmer plants, sprays, and harvests. There simply aren't enough of them to go around.
This is precisely where artificial intelligence becomes not a threat to human expertise, but its most powerful amplifier.
The global imperative is urgent. Across leading food security projection studies, total global food demand is expected to rise by 35 to 56 percent by 2050. India, as one of the world's largest agricultural producers, sits at the centre of this challenge. The question is not whether technology will transform farming it already is but whether that transformation will work with farmers and agronomists or bypass them entirely.
The answer must be the former.
An experienced agronomist carries decades of contextual knowledge: how a particular crop variety behaves in a specific soil type, how a region's microclimate shifts between kharif and rabi, how a cooperative's input supply chain tends to break down under pressure. This kind of embedded, relational knowledge cannot be replicated by a model. What AI can do is take that expertise and extend its reach from one agronomist advising a hundred farmers, to that same expert effectively guiding thousands through AI-assisted decision support, automated scouting alerts, and predictive crop advisory delivered to the farmer's phone in their own language.
The farmer, too, carries irreplaceable knowledge. Generational understanding of local soil behavior, observation-based pest recognition, an intuitive sense of when the rains will arrive this is not folklore. It is data that formal agricultural science has historically undervalued. AI systems trained on ground-level observations, annotated by experienced agronomists and informed by farmer feedback, become substantially more accurate and relevant than those built on satellite data alone. The human is not removed from the loop. The human is the loop.
McKinsey estimates the agriculture industry stands to gain $100 billion at the farm level and $150 billion at the enterprise level through the integration of AI solutions. Yet precision agriculture adoption in India stands at less than 5 percent the lowest among major agricultural economies tracked. The opportunity gap is enormous. So is the responsibility to close it thoughtfully.
The Government of India has recognised this. The Digital Agriculture Mission, approved in September 2024 with an outlay of ₹2,817 crore, is building AgriStack as a farmer-centric Digital Public Infrastructure creating digital identities for 11 crore farmers over three years and launching a nationwide Digital Crop Survey covering all districts. Initiatives like the Krishi Decision Support System, integrating satellite, weather, and soil data, are creating the data infrastructure on which intelligent advisory systems can be built. The Kisan e-Mitra AI chatbot is already addressing an average of 8,000 farmer queries per day in eleven regional languages. These are meaningful signals that India's agricultural data ecosystem is maturing.
But government infrastructure alone will not close the gap between policy intent and field-level impact. That is where agribusinesses, cooperatives, farmer producer organisations, and agri-tech platforms have a critical role to play. When a cooperative digitises its member farms crop plans, input usage, scouting observations, yield records it creates a feedback loop that benefits every farmer in the network. AI models trained on that aggregated data can identify early stress signals across thousands of acres simultaneously, flag pest outbreaks before they spread, and recommend intervention timing with a precision no single agronomist could achieve at scale. At Khetibuddy, we see this dynamic play out across enterprise customers managing large farm networks: the platforms that drive the most value are those where agronomist expertise is embedded into the advisory logic, not replaced by it.
The fear that AI will displace the farmer or make the agronomist redundant misreads what the technology actually does in practice. A well-designed agricultural AI system does not issue commands it surfaces information at the right moment so that a farmer or extension worker can make a better-informed decision. The judgment, the local knowledge, the relationship with the land those remain human.
India's agricultural future will be shaped by how well we integrate these two forces. The imperative for agribusinesses is clear: invest in digital infrastructure now, not as an experiment, but as operational backbone. Digitise your farm networks. Embed your agronomic expertise into AI-assisted advisory systems. Build data assets that compound in value over time.
The farmer's knowledge built over generations. AI's job is to make sure that knowledge reaches every field that needs it.
(Vinay Nair is Founder & CEO, Khetibuddy)