On a Tuesday evening in June, Priya stands at the edge of her small grape plot in Maharashtra, watching a flat grey sky that refuses to make up its mind. The forecast says there might be rain, or there might not. If she irrigates and a storm follows, her vines risk disease. If she waits and the storm drifts elsewhere, the crop will struggle through another dry night.
What finally tips her decision is not a hunch, but a notification. A device in her field has been tracking soil moisture, temperature, and humidity all day. An AI model has combined that stream of data with short term weather forecasts and quietly suggested: “Skip irrigation tonight.” Priya agrees. She is not outsourcing her judgment, only updating it.
Across the tropics, that kind of decision is getting harder. For every 1°C increase in global temperature, average yields of major crops such as maize and wheat are projected to fall by around 6–8 percent, with the largest losses in already hot regions. Smallholder farmers, who grow much of the food in low and middle income countries, are especially exposed because they rely on rain fed agriculture and have few buffers when a harvest fails.
This is the narrow but crucial space where AI in agriculture can help. Farmers do not need grand claims about “smart” machines so much as clear, local advice in the present: what to do with this plot, this crop, today. Tools like Fasal sit between messy climate signals and human judgment, turning data on weather, soil moisture, and crop stress into specific recommendations on irrigation and crop care, without pretending to know more than the farmer about her own land.
Used well, this kind of “small AI” sharpens the everyday decisions that add up to a season. It can help reduce over irrigation, cut unnecessary inputs, and spot trouble earlier, all of which matter when margins are thin and weather is erratic. It cannot, on its own, fix broken insurance schemes, guarantee fair prices, or hold back a flood. For that, farmers still need institutions, finance, and policy that match the new climate reality.
The hard question is no longer whether AI can help farmers, but which tools genuinely work outside the brochure, and for whom. It also raises a second question that is just as important: who can actually access them. Cost, connectivity, language, and digital literacy still keep many of the most vulnerable communities on the wrong side of the innovation gap.
Across India, a growing set of AI enabled tools is trying to close that gap in different ways, from platforms such as KisanGPT, Krishimuni, and Krishify, which offer early warnings, fertilizer guidance, and pest and disease support, to field based systems like Fasal that pair sensors with crop specific advice. Fasal is one of the tools now featured in TEL’s Solutions Toolbox, where you can learn more about how it works alongside other technologies that are helping vulnerable communities turn climate uncertainty into better informed decisions.
If you would like to help expand access to these technologies and support TEL’s mission of using practical innovation to alleviate poverty and address climate change, please consider making a donation.
