There are a lot of problems waiting to be solved, some easy, some deeply complex. And with the rise of AI, we’re seeing exponential growth across sectors. AI is blooming, thriving off the data we feed it through our clicks, our choices, our language, our habits. It’s trained, and then it evolves. Because we, the users are constantly handing it billions of data points.
But what happens in places where the data doesn’t exist?
India, an agriculture based economy, still runs heavily on manual processes, and scattered supply chains. We rarely pause to think about where our food really comes from. Take the cucumber or vegetable, there will be no traceability. No indication of where it was grown, what inputs were used, or how far it traveled. It’s not just about one vegetable. It’s about a deeper gap in the system. A missing layer of infrastructure we’ve learned to overlook.
Without accessible data from the field, it becomes incredibly difficult to build solutions that can strengthen and scale both agricultural exports and domestic consumption.
As AI systems advance, they’ll need structured, reliable, on-ground data to solve meaningful problems in food and farming. This isn’t just about automating for efficiency. It’s about including sectors that have long been left out of the digital revolution.
There’s a growing need for tools that collect, verify, and process agricultural data IoT devices, lightweight farm tracking systems, simple software made for farmers, not engineers.
The opportunity isn’t just in building smarter algorithms. It’s in building deeper connections to the field.
If we want intelligence to scale, we need to first make the ground visible. A few agritech startups are working on this, but we need more solutions that are grounded in local realities and built with farmers in mind. Until then, the promise of data-driven agriculture will stay out of reach for most.
Cheers
Check out the other post: Accountability Ends. Guilt Doesn’t!
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