SAGDA: Open-Source Synthetic Agriculture Data for Africa
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908408699944960 |
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| author | Belgaid, Abdelghani Ennaji, Oumnia |
| author_facet | Belgaid, Abdelghani Ennaji, Oumnia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAGDA: Open-Source Synthetic Agriculture Data for Africa Belgaid, Abdelghani Ennaji, Oumnia Machine Learning 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. |
| title | SAGDA: Open-Source Synthetic Agriculture Data for Africa |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.13123 |