SAGDA: Synthetic Agriculture Data in Africa
Domaine:
agricultureType de record:
project
SAGDA (Synthetic Agriculture Data in Africa) is a Python library for generating, augmenting, and validating synthetic agricultural data for African regions. The library allows users to create realistic agricultural datasets, augment them using machine learning techniques, and validate the generated data against real data. SAGDA can also integrate real-world data from APIs like NASA POWER and OpenWeatherMap to enhance synthetic data generation.
Key Features
- Generate Synthetic Agricultural Data: Create time-series datasets with geospatial, climate, soil, crop yield, and fertilizer information.
- Augment Data: Augment real agricultural datasets using machine learning techniques such as random sampling, interpolation, linear regression, autoencoders, and GANs.
- Validate Data: Validate synthetic data using statistical tests (e.g., KS test) and compare it against real data.
- API Integration: Fetch real-world climate data using NASA POWER and OpenWeatherMap APIs, or generate synthetic data based on user-specified parameters.