Harv-estimate

Inspiration

We were inspired to create a web-based app for farmers to see past crop yield because we recognized the importance of historical data in making informed decisions about crop management and marketing. By providing access to past yield data in a user-friendly format, farmers can gain valuable insights into trends and patterns that can help them optimize their farming practices, improve profitability, and manage risks. Additionally, the app can help farmers make more informed decisions about which crops to plant based on past performance, market trends, and other factors.

What it does

This code is a Python script that creates a web application using the Streamlit library. The application displays an interactive map of the United States that shows the crop yield per acre for different types of crops such as barley, corn, oats, and soybeans. The map also includes a tooltip that displays more information about each state's crop yields. Users can select a crop to display on the map and can click on a state to view more detailed information about the crop yields for that state.

How we built it

We structured our application using the Folium and Streamlit libraries. The application loads the required data from CSV files and uses libraries such as pandas, folium, matplotlib, and streamlit_folium to create the visualizations.

Challenges we ran into

The primary challenge we faced during the project was determining how to deal with missing data from each API pull. Ultimately we ran out of time to do everything we wanted to do and there was not enough data readily available but hope to accomplish more in the future.

Accomplishments that we're proud of

Were proud we were able to pull together the necessary data in order to produce the final outcome of this project.

What we learned

This was the first time we had ever used some of these Python libraries, such as Folium and Streamlit. While challenging initially, we have learned invaluable mapping and data wrangling skills which will inevitably assist in future Hackathon endeavors.

What's next for Harv-estimate

We would love to have more time to flesh out all of the data presented on the website. We believe Harv-estimate could be an even more useful tool when given a bit more time to learn.

Built With

• folium
• python
• streamlit