Considerations for AI-EO for agriculture in Sub-Saharan Africa
Considerations for AI-EO for agriculture in Sub-Saharan Africa
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Date
2023-03-24
Authors
Nakalembe, Catherine
Kerner, Hannah
Advisor
Citation
Catherine Nakalembe and Hannah Kerner 2023 Environ. Res. Lett. 18 041002.
Abstract
Adapting to and mitigating climate change while
addressing food insecurity are top priorities in SubSaharan Africa that require technologies to improve
rural livelihoods with minimal environmental costs
[1]. Artificial intelligence (AI) offers great promise
for climate-smart solutions that improve food security outcomes. While precision agriculture is often the
foremost use case for AI in agriculture (e.g. automation of farm equipment or nutrient application), precision agriculture is out of reach for most African
farmers due to the required capital and infrastructure.
AI solutions using satellite Earth observations
(EOs), which we call AI-EO, are more accessible in
the near term. EO enables agricultural analyses and
insights at global scales, and many datasets are freely
available, making EO-based solutions affordable [2].
AI-EO-derived products such as crop type maps and
yield estimates are necessary to forecast food production surpluses or deficits, inform trade, and aid
decisions. These products can support policies that
accelerate the design and adoption of climate-smart
agriculture and impact farmer livelihoods by increasing access to actionable early warning, risk financing or insurance [3], farm inputs, markets, and costreducing interventions [2, 4].
Despite their promise, AI-EO solutions for agriculture in Africa are still limited. Most techniques are
not generalizable across heterogeneous landscapes.
In this paper, we describe the principal sub-fields of
research in AI-EO for agriculture in Africa and discuss examples and limitations of existing work. We
also propose ten considerations for future work to
help increase the impact of AI-EO research in Africa.