This is a streamlit-based tool that predicts crop yields based on historical weather data for a given state and crop type. It helps farmers and crop enthusiasts make informed decisions about planting and resource allocation.
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Upload Weather Data: Users upload historical weather data files in NetCDF format containing information on temperature, humidity, precipitation, and more.
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Select Your State: Users specify the state where they plan to cultivate the crops. Different regions have different weather patterns, which affect crop yields.
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Choose Your Crop: Users select the crop they intend to grow, either corn or soybeans.
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Prediction Process: The program processes the data, applies machine learning models, and predicts the expected crop yield based on historical weather patterns.
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Get Your Prediction: The predicted crop yield is displayed to the user, providing valuable insights for planning and decision-making.
To use this tool, follow the instructions in the program. You can run the program and interact with the user interface to make predictions.
Before you can run this app, ensure you have the following prerequisites:
- Python (Version 3.11)
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Clone this repository to your local machine:
git clone https://github.com/atishay-gwari/Satellite-Harvest.git
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Install the required Python packages using pip:
pip install -r requirements.txt
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Start the PDFPal web application
streamlit run app.py