SAGDA: Open-Source Synthetic Agriculture Data for Africa

TL;DR

SAGDA is an open-source Python toolkit that generates and augments synthetic agricultural data to improve machine learning applications in African agriculture, addressing data scarcity issues.

Contribution

It introduces SAGDA, a comprehensive toolkit for synthetic data generation, augmentation, and validation tailored for African agricultural ML applications.

Findings

Enhanced yield prediction through data augmentation

Improved fertilizer recommendation accuracy

Demonstrated effectiveness of synthetic data in ML models

Abstract

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.