SAGDA: Open-Source Synthetic Agriculture Data for Africa
This addresses data limitations for ML practitioners in African agriculture, though it is incremental as it builds on existing synthetic data methods.
The paper tackles data scarcity in African agriculture by introducing SAGDA, an open-source toolkit that generates synthetic agricultural datasets, resulting in enhanced yield prediction and fertilizer recommendation applications.
Data scarcity in African agriculture hampers machine learning (ML) model performance, limiting innovations in precision agriculture. The Synthetic Agriculture Data for Africa (SAGDA) library, a Python-based open-source toolkit, addresses this gap by generating, augmenting, and validating synthetic agricultural datasets. We present SAGDA's design and development practices, highlighting its core functions: generate, model, augment, validate, visualize, optimize, and simulate, as well as their roles in applications of ML for agriculture. Two use cases are detailed: yield prediction enhanced via data augmentation, and multi-objective NPK (nitrogen, phosphorus, potassium) fertilizer recommendation. We conclude with future plans for expanding SAGDA's capabilities, underscoring the vital role of open-source, data-driven practices for African agriculture.