SAGDA (Synthetic Agriculture Data for Africa) is an open-source initiative designed to address the data scarcity challenges in African agriculture. By leveraging advanced data science techniques, SAGDA provides tools for generating, augmenting, and validating synthetic agricultural datasets, covering key variables such as climate, soil properties, crop yields, and fertilizer use. These synthetic datasets enable researchers, policymakers, and agribusinesses to simulate realistic agricultural conditions, supporting data-driven decisions and innovation in the field.

The SAGDA Python library provides:

• Synthetic Data Generation: Tools to generate synthetic datasets for agricultural data including climate, soil, and crop yields.
• Data Augmentation: Enhance existing datasets using machine learning models like regression, autoencoders, and GANs.
• Demos and Use Cases: Examples and tutorials demonstrating how to apply synthetic data for various agricultural research scenarios.

Check out our repository here to explore the library and see demos in action.

We welcome contributors! Whether you’re a developer, researcher, or data scientist, you can contribute by forking the repo, submitting pull requests, or engaging in discussions.