Badr-MOUFAD/multivariate-time-series-clustering
Multivariate clustering of weather data. This is part of my research internship.
This project helps agricultural planners and farmers improve their predictions of crop yield. By analyzing historical weather data like temperature, rain, and humidity for specific regions and crops, it groups similar crop years together. The output is a better understanding of how different weather patterns impact yield, which can then be used to enhance existing crop yield prediction models. This is designed for agricultural scientists, farm managers, or researchers focused on optimizing crop production.
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Use this if you are struggling with the accuracy of your crop yield predictions and believe that organizing your historical weather data into meaningful groups could provide a breakthrough.
Not ideal if you are looking for a direct crop yield prediction model, as this tool focuses on an intermediate step of data organization to improve other models.
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Jul 12, 2021
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