When sending crops and other food products to third world countries, knowing where certain crops will thrive is valuable data.

What it does

Given crop information such as ideal temperatures and humidity we plot the most ideal locations for the crop in the surrounding climate. The climate data is aggregated and we calculated the "survival score" based on if the climate factors of the area are within an acceptable range of the crop. We base our result on a 4 point scale. points are accumulated if one climate factor (average humidity as an example) is within the bounds for the crop in question. 4 points = PLANT HERE 0 points = AVOID AT ALL COSTS

How we built it

Python flask web app with json file parsing to generate the appropriate data points to feed into plotly to present a model.

Challenges we ran into

Acquiring the right kinds of data.

Accomplishments that we're proud of

Dynamically processing data and displaying it in a user friendly way ...And finishing.

What we learned

Pandas! Geospatial data is no joke.

What's next for iDealStor

Crazzzzy launch party

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