Agrivision AI

Project Story — AgriVision AI

Farming decisions shouldn't require farmers to become experts in complicated software.

A farmer sees something wrong with a crop. They take a picture, but then they have to figure out what the problem is, check the weather, research treatments, calculate costs, and decide what to do next.

We built AgriVision AI to bring all of those decisions together.

But for the WebMCP Challenge, we wanted to go one step further.

Instead of making the farmer learn how to navigate every feature, AgriVision can now work with an AI agent.

A farmer can simply say:

“Analyze my tomato crop and create the cheapest recovery plan under ₹500 per acre.”

The agent can use AgriVision's WebMCP tools to analyze the crop, check relevant weather conditions, find treatment options, calculate costs, and build a recovery plan.

The farmer stays in control throughout the process. They can change constraints, ask the agent to optimize the plan, review the recommendation, and approve it before it is saved.

For example, if the first plan costs ₹700, the farmer can simply say:

“I can only spend ₹300.”

The agent can then use AgriVision again to find a better option.

That's what WebMCP changes for us: AgriVision isn't just a website that a farmer operates. It's a set of useful capabilities that an AI agent can operate with the farmer.

We designed AgriVision around a simple principle:

The farmer sets the goal. The agent handles the complexity. The farmer makes the final decision.

Our goal is to make agricultural technology feel less like another complicated tool to learn and more like an intelligent assistant that works alongside the people who need it.

AgriVision AI — AI-powered agricultural intelligence, built for humans and agents.