Inspiration
What we wanted to understand was how certain weather conditions affects the annual crop yields for any given year.
What it does
Our implementation looks at historical data to identify which particular weather condition in the Midwest has the largest effects on the crop yields.
How we built it
After analyzing a large dataset, we were able to identify three factors that had the most profound influence on the crop yields outputs. This allowed us to leverage statistical analysis techniques to effectively display a 2-Dimensional visualization.
Challenges we ran into
Our prediction model faced challenges to effectively analyze imported datasets. In order to facilitate our data processing, we combined our crop yields dataset with our weather condition dataset.
Accomplishments that we're proud of
We successfully visualized our datasets using industry-level and accessible technologies.
What we learned
We received preliminary exposure to Databricks platform and gained valuable hands-on data. We learned how to utilize resources and work cohesively as a team.
What's next for Weather-to Yield signal detection
We look forward to identifying current weather conditions influencing upcoming crops yields can help make predictions to better prepare the food supply chain.
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