Selector Analysis

Inspiration

Sustainability is an important issue in our day and age. With deforestation and other environmental issues becoming more and more of a concern, we believe that our application can help combat these problems.

What it does

Our application takes in a crop and a state that the user inputs, and it outputs the top three counties that match the crop. This is decided by the moisture level, temperature, etc. that is best suited for the crop. By matching the crop to the right soil, we can ensure that we are responsibly and efficiently using the resources available to us.

How we built it

We used HTML and CSS to build our frontend. Between our frontend and backend, we enabled communication between JavaScript and Python. We used Python to query our Firebase Realtime Database and we send this data to our JavaScript files, which then display this on our frontend. This whole application is hosted on AWS.

Challenges we ran into

We ran into challenges with finding proper datasets to base our project off of. A lot of the data that is available is hidden behind paid API subscriptions or invalid links. On top of that, our varied tech stack made it difficult sometimes to enable communication between the various moving parts.

Accomplishments that we're proud of

We were able to successfully find a dataset that matched our requirements, and we were able to write this data to our Firebase Realtime Database. We successfully built our web application and were able to host this application on AWS, where it is publicly accessible.

What we learned

We learned about web scraping, JavaScript to Python communication, AWS security measures, and much more.

What's next for Selector Analysis

We hope to expand our program to encompass our own ML algorithm to predict areas that crops could grow well in. On top of this, we hope to encompass more area and crops that the user could query.