CropWatch | Devpost

Landing Page

Inspiration

We decided to solve topic 1, specifically crop yield prediction in order to develop a tool which will forecast the crop yield as the majority of the third world nations across the globe are dependent on agriculture as their main source of revenue.

What it does

CropWatch uses a Machine Learning model in order to understand and analyze crop yield data across various factors such as region, rainfall, previous crop yield patterns, etc and based of the data predicts the crop yield in various regions while highlighting the chances of a food shortage, the supply chain that caused the shortage and the supply chains affected in future. It used AI algorithms and research in order to highlight different food shortages in various supply chains.

How we built it

We used Python (sklearn, tensorflow) for the Machine Learning Model, we used React for the frontend, and express for the backend. we added modern style to the application through tailwind css. We also made 3d animations in the application through 3d.js and globus modules in javascript.

Challenges we ran into

Building 3D animations for the globe was a hard part, furthermore modifying the globe in order for it to interact with the model and the subsequent errors wasted a lot of time

Accomplishments that we're proud of

This was our first time working with 3d models in general and we'r proud we were able to make classy 3D models for our application. We are also proud that we have a highly effective machine learning system and research work on supply chains in order to back up our claims on food shortages.