Environment AI

Reimagine Farming

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Inspiration

Farming, today, is at risk. Recent trends show that more than 50% of farmers in America have lost large amounts of money since 2013 . Large farming corporations, with millions to shell out, are able to survive, but most farmer-families don't have this sort of funding, and as such, are unable to invest in more modern machinery and equipment.

Anyone can plant a seed, and see something grow; but it takes the many years of experience of a farmer to leverage hundreds (and even thousands) of acres of land to take this "something" and ensure sustainability. This is why I built EnvirAI, a tool that helps farmers manage their farms using AI.

What it does

EnvirAI is a tool that allows farmers to gain deeper insights into their land, leveraging the power of AI. It allows them to plan and structure efficient farm layouts which will ultimately maximize their revenue and efficiency. Using external APIs to pull in real-time data (such as air quality, soil and weather conditions), and leveraging AI to provide smart recommendations, EnvirAI helps farmers determine how suitable a particular region is for growing a particular crop.

Various features & General Procedure:

  • Find any location on the planet, and visualize it via 3D interactive graphs
  • Plot the crops you intend to grow in the region.
  • Foresee future weather conditions using our Weather/Season Prediction Timeline tool
  • Select any plot to get accurate, real-time data about growing this particular crop

How we built it

  • The frontend was built using React.js and SCSS.
  • The backend was built in Node.js, powered by an Express.js server that made use of two external APIs: ambee, and WeatherPlanner to pull in the required real-time data.
  • Our AI recommendation engine was built and hosted in TensorFlow.js.
  • The "Harvest Statistics" portion was a module that was custom-built in TypeScript. This was then accessed via the frontend using Recharts, a library that combines React and D3 to form a reliable library.

Accomplishments that we're proud of

I'm proud of what I've accomplished in an extremely short amount of time. I've had prior experience working on similar projects involving TypeScript, JS, and React, so I was able to dedicate more time on adding more features to my app.

What we learned

Through the extensive research I've performed, I become more appreciative of the massive amounts of information that farmers need to process on a daily basis to ensure that they have sustainable farms, which eventually feed us. I have a deeper respect for the agriculture profession.

What's next for our project?

I plan on taking this idea further as a startup, and scaling it onto a much larger platform so it eventually helps farmers (and laypersons) plan more efficient farm layouts and understand the environment around them. For this, I have to iron out existing bugs, and possibly add more features to make the app more robust. But all in all, I think this app has great potential as a business as well as an affordable tool for people.

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