AgriOptimize

Inspiration

I used to watch a lot of documentaries highlighting the food problems in various countries but whenever I went to local vegetable markets, I used to find a lot of decaying veggies. Just a while before when I was preparing for my exams, I found out that the world is producing more food than what is actually needed and a 1/3rd of it was going waste! This problem of 'world hunger' should not even exist in the first place.

What it does

My project works on previously present data and uses ML to predict the various prices for various foods in different areas. Considering many factors like mileage, fuel prices, the market fees and the price/kg, my site gives recommendations to the producers for the location where they should sell their harvest. This minimizes waste and makes sure that the demand is met successfully.

How we built it

Used react for building the frontend with bolt.ai Node.js and express for building the backend Python and facebook's 'Prophet' for ML predictions

Challenges we ran into

To find a suitable prices data for a specific time that needed for accurate predictions Deploying backend

Accomplishments that we're proud of

Precise data predictions and perfect recommendations after just filling a form with expected revenue, market fees and other required details.

What we learned

To combine ML models with web apps and to use python to generate predictions in json format so that everyone can look into it.

What's next for AgriOptimize

We can add features like maps, export options by partnering with trasnportation companies to reach rural areas as well.

Built With

• express.js
• ml
• netlify
• node.js
• python
• react