This project addresses the global issue of hunger by leveraging machine learning and satellite data to identify regions where rice is cultivated and forecast crop yields. With rice serving as a staple food for over half of the world's population, understanding its cultivation patterns and predicting yield variations due to factors like climate change are crucial for food security efforts.
Crop Identification: Utilize machine learning models to analyze satellite imagery and accurately identify areas where rice is cultivated.
Crop Forecasting: Forecast rice crop yields in identified areas using climate and satellite data, providing insights into the impact of climate change on agricultural productivity.
Microsoft's Planetary Computer
Sentinel-1 (radar)
Sentinel-2 (optical)
Landsat (optical)
Python
Machine Learning Libraries (e.g., TensorFlow, scikit-learn)
Data Processing Libraries (e.g., pandas, NumPy)
Satellite Image Processing Libraries (e.g., rasterio)
Git and GitHub for version control
Researchers and scientists interested in agricultural modeling and food security.
Developers looking to contribute to projects addressing global challenges.
Policy-makers and organizations involved in food security initiatives.
By accurately identifying regions where rice is cultivated and forecasting crop yields, this project contributes to efforts aimed at mitigating hunger on a global scale. By understanding the dynamics of rice production and its response to changing environmental conditions, stakeholders can make informed decisions to ensure food security for vulnerable populations.