Every June, roughly the same scene plays out across lakhs of villages in India. A farmer wakes up before sunrise, walks out to the field, looks at the sky for a while, maybe crushes a bit of soil in his palm, and decides whether to sow now or wait a few more days. This decision has always rested on instinct, what his father did, what the elders in the village remember, and a bit of hope mixed with a lot of guesswork. And when the monsoon doesn’t behave, he’s the one who bears the cost of that guess.
That guesswork is finally getting some help. And the source of that help is, oddly enough, Artificial Intelligence.
I say “oddly” because when most of us hear “AI,” we think of chatbots, self-driving cars, maybe an image generator someone showed us on their phone. Nobody’s first thought is a cotton farmer in Vidarbha or a paddy grower somewhere near the Sundarbans. But spend some time looking at what’s actually being rolled out in Indian agriculture right now, and there’s quite a lot happening: pest alerts, rainfall predictions down to the district level, soil advisories, faster insurance claims. None of it makes headlines the way a new AI chatbot does, but it’s arguably doing more real-world good.
It’s Not Just About IT Parks and Bengaluru Startups
We already know India does well in tech; that part isn’t new. Stanford’s 2025 Global AI Vibrancy Tool placed India third worldwide in AI competitiveness, looking at how the country built up its AI capacity between 2017 and 2024 across talent, research output, startups and infrastructure. Usually, when this stat comes up, it’s in some article about software exports or a unicorn startup’s funding round.
What gets talked about far less is agriculture. With the India-AI Impact Summit 2026 focused on “AI for Humanity,” farming has quietly become one of the better examples of whether India can take this technology and actually put it in the hands of people who need it, not just the ones who can already afford smartphones and data plans, though, to be honest, that’s still a big part of who benefits first.
The numbers, at least, are hard to argue with.
First, You Need to Know Who the Farmer Is
None of this works without basic data. You can’t tell a farmer what fertiliser to use or when to sow if you don’t even know which piece of land is theirs or what they grew on it last season.
This is where the Digital Agriculture Mission comes in, launched in 2024 with a budget of ₹2,817 crore. It’s building something called AgriStack, which is basically an Aadhaar-style digital identity system, except for farmers and their land. Each farmer gets a Farmer ID tied to their land records, cropping history and the government schemes they’ve availed.
By late November 2025, over 7.63 crore Farmer IDs had been issued, against a target of 11 crore by 2026-27. About 1.93 crore of those are women farmers, a number that matters more than it might seem, because women’s work in farming has historically gone unrecorded in official data, even though they do a huge share of it.
There’s also a mobile-based Digital Crop Survey running alongside this, which mapped more than 23.5 crore individual plots across 492 districts in the 2024-25 Rabi season alone. It’s not the kind of project that gets written about much because there’s nothing flashy about it. But it’s the groundwork everything else sits on.
A Chatbot That Actually Speaks Your Language
Kisan e-Mitra is probably the most relatable example on this list. It’s a voice-based chatbot, running since 2023, that answers questions about PM-KISAN, Kisan Credit Card, crop insurance and similar schemes, and it does this in 11 regional languages.
That language part is a bigger deal than it sounds. A lot of India’s digital tools fail not because they’re badly built, but because they assume everyone’s comfortable typing in English or Hindi, which isn’t true across most of rural India. Kisan e-Mitra gets around that. By December 2025, it had answered over 93 lakh queries, averaging more than 8,000 questions a day, which is 8,000 farmers a day who didn’t have to travel somewhere or wait in a queue to ask a simple question about their scheme eligibility.
Spotting Pests Before They Wipe Out the Crop
Talk to any farmer about what worries them during the growing season, and pest attacks come up almost immediately. A bad infestation can undo weeks of work within days, and often the damage is already done by the time it’s visible.
The National Pest Surveillance System, or NPSS, launched in 2024, tries to catch this earlier. A farmer photographs the affected plant or the pest itself using a mobile app, and the system, using image recognition trained through machine learning, identifies it and tells them what to do next. As of December 2025, it covers 66 crops and over 432 types of pests, and more than 10,000 extension workers are actively using it in the field. It’s essentially a diagnostic tool sitting in a farmer’s pocket, something that didn’t exist even five years back.
Telling Farmers When the Rain Is Actually Coming
Of everything on this list, the one that impressed me most is the AI-based monsoon onset forecasting pilot from Kharif 2025. This wasn’t the usual “monsoon expected in June” line we’ve all heard on the news since childhood. It was far more specific than that built by combining an open-source climate model called NeuralGCM with the European Centre’s AI Forecasting System, on top of 125 years of India Meteorological Department rainfall data.
These forecasts went out as SMS in five regional languages through the mKisan portal, reaching 3.88 crore farmers across 13 states. And here’s the part that actually tells you whether it worked or not: follow-up surveys in Madhya Pradesh and Bihar found that somewhere between 31 and 52 percent of farmers changed their sowing plans based on this information. That’s not a small number for something this new. A forecast is only useful if people trust it enough to act on it, and clearly, a fair few did.
Making Crop Insurance Feel Less Like a Black Box
Anyone who’s spoken to a farmer about crop insurance has probably heard the same set of complaints: claims take forever, the assessment process feels arbitrary, and by the time money actually arrives, the financial damage has already spread to the next season’s inputs.
The Pradhan Mantri Fasal Bima Yojana has been trying to fix parts of this using a few AI-based tools:
YES-TECH uses remote sensing and AI analytics to estimate crop yields without relying entirely on manual crop-cutting experiments. Nine states have adopted it so far, and Madhya Pradesh has gone fully tech-based for its yield estimation now.
CROPIC works off geotagged, time-stamped photographs uploaded by farmers and field staff to verify crop damage claims. It’s a simple idea that adds a layer of transparency that wasn’t there before.
And there’s a PMFBY WhatsApp Chatbot too, which gives farmers scheme information through an app most of them already use, instead of asking them to download something new.
The scale of PMFBY overall is honestly staggering. From 2016-17 to October 2025, PMFBY, together with its companion scheme RWBCIS, covered over 78.51 crore farmer applications, paying out ₹1,90,374 crore in claims against ₹35,919 crore collected in premiums. Whatever one thinks of insurance schemes in general, this is one of the largest crop insurance operations anywhere in the world, and AI now sits quietly in the background of how it’s assessed and settled.
One Farmer, One Coconut Farm, and a Doubled Yield
Statistics can only take you so far, so it’s worth talking about one actual person. Rajaratnam Kanakarajan grows coconuts in Tamil Nadu. He started using an AI-based precision farming setup built by a local startup called Farm Again. Solar-powered sensors on his land now track soil moisture, irrigation and fertiliser use in real time, feeding all of it into an app that makes decisions he used to make purely by feel.
His yield doubled. And this isn’t a one-time success story picked for a press note; the same system has spread to over 3,500 farmers across more than 4,000 acres in Tamil Nadu, helped along by the fact that the Indian-made version of this equipment costs around ₹2.5 lakh, against ₹25 lakh for imported alternatives. On top of the yield gains, there are environmental savings too: over 4 lakh cubic metres of water saved every year, and close to 20,000 tonnes of CO₂-equivalent emissions avoided.
If you’re looking for the actual point of all this AI-in-agriculture push, it’s probably right here, not in the dashboards or the policy papers, but in whether a coconut farmer in Tamil Nadu can afford the tech and actually see his yield go up because of it.
What’s Coming: Bharat-VISTAAR
The Union Budget for 2026-27 introduced Bharat-VISTAAR (Virtually Integrated System to Access Agricultural Resources), a multilingual AI tool meant to bring together the various AgriStack portals and ICAR’s knowledge base into one place. The thinking here is fairly simple: instead of a farmer having to check five different portals for five different pieces of information, there’s one AI-powered point they can go to instead.
There’s also a policy document from October 2025 worth mentioning, “Future Farming in India: AI Playbook for Agriculture,” put together by the World Economic Forum along with the Office of the Principal Scientific Adviser and IndiaAI. It sets out something called the IMPACT AI framework, structured around three ideas: Enable, which is about building the policy and infrastructure base; Create, which is about developing and testing tools with startups and research institutions; and Deliver, which is about making sure these tools actually reach farmers through extension workers instead of dying out as pilot projects that never scale.
That last one, Deliver, tends to be where most government tech initiatives fall short. Going by the numbers above, at least so far, this particular effort seems to be taking that seriously rather than treating it as a formality.
So, Does Any of This Actually Matter?
The technology itself isn’t unique to India; plenty of countries are experimenting with AI-driven forecasting and crop monitoring. What stands out here is the scale of it, and who it’s being built for: over 7 crore farmers, in 11 languages, at a fraction of the cost of imported systems, with success measured by whether farmers actually change what they do because of it.
There’s still a long way to go, and it would be dishonest to pretend otherwise. Internet connectivity gaps, affordability, and getting these tools to the last farmer in the last village all remain genuine problems, and even the government’s own playbook admits as much. But in a country where a majority of the rural population still depends on farming, small gains add up to a slightly better sowing decision here, a faster insurance payout there, a pest caught a week earlier than it would have been. None of that shows up on the news, but it shows up in a farmer’s field.
Somewhere, a farmer will step outside next June, look up at the sky out of habit, and check his phone anyway. He probably won’t think about the model behind that forecast, trained on over a century of rainfall data. He’ll sow a little more confidently than his father used to.
