How we built it
We built this program based on Python and PyQT5. Messages are sent to the user via the Twilio API.
Challenges we ran into
Our program needs to get different locations depending on where the farmer is located. This was a challenge for us, so we chose to use web scrapping coupled with user customization to capture the data. Also, converting Python into a more aesthetically pleasing interface for user interaction was challenging.
Accomplishments that we're proud of
We are proud to say that as long as the farmer using our program is within the state of California, then our software can accurately and quickly capture the rainfall data, analyze it against other local data, and send the results back to the farmer efficiently.
What we learned
Time management, Teamwork, planning ahead, some specific syntax for new languages, python scraping, and how to use APIs!
What's next for R'Farmers
We plan to add more factors to the analysis of the results, such as dryness of the weather, rainfall in the next ten days, and soil conditions. This will allow us to analyze more accurately. Furthermore, after studying the field of artificial intelligence and machine learning in the future, we can go further and build more accurate models.
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