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Predicting weather's effect on yield
We predict the yields
[Please read the write up]
We used USDA and NOAA data to identify counties that consistently outperform crop yield expectations under varying weather, helping farmers make smarter, lower-risk land investment decisions
Harvesting insights from chaos — using AI to decode when crops defy the weather.
We picked the first option
Leveling the field for local farmers: turning weather into yield confidence.
Website to view the correlations of weather to crop yields.
Weather-to-Yield Signal Detection: County-level Corn & Soybean yield analysis (2010-2024, 32 US states) built on Databricks + NOAA weather APIs.
N/A
Exploring Historical yield data
We don't measure yield. We measure surprise.
Bringing a clear forecast to your fields.
A weather-to-yield analytics engine that ranks key weather drivers, detects yield anomalies across regions, and provides interpretable insights through dashboards and spatial visualizations.
prediction of crop yields
A machine learning predictor that takes weather data for counties in Nebraska, Illinois, Indiana, Iowa, and Minnesota. This data is then used to predict the crop yields of soybeans and corn.
FarmCast-AI: Explainable climate-to-yield anomaly detection for counties—spot yield shocks and the weather signals behind them.
By reducing a complex data set and implementing LLM approaches, we were able to identify which weather conditions facilitated crop growth across keystone agricultural species.
Agriculture is increasingly volatile due to climate uncertainty. Our solution is a yield forecasting engine that uses historical crop & weather data to predict expected yield for the current season.
A dashboard that analyzes expected yield based on historical data and gives AI-enhanced advice.
Like a harrow removes weeds and makes fields better, our digitized app allows farmers to have weather-to-yield insights for American agriculture.
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