AI identifies Senegal's smallholder crops 84% of the time using limited training data
Researchers at the University of Cambridge have developed an AI model called Tessera that accurately maps smallholder crops in Senegal using limited satellite data. This technology could improve food security in the Global South by providing industrial-level agricultural insights to small-scale farmers.
Why it matters
AI-driven crop mapping can help developing nations mitigate the impact of climate change on food production by optimizing resource allocation.
edited by Swati Mestri , reviewed by Andrew Zinin
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Add as preferred source A Tessera crop map of Senegal's groundnut basin in 2021, showing millet (dark green), groundnut (pink), sorghum (yellow), cowpea (orange), rice (purple), trees (light green), fallow land (dark brown) and other crops (red), with areas excluded from the analysis in black. Contains modified Copernicus Sentinel data (2021). Credit: Madeline Lisaius / Tessera, University of Cambridge Food in Senegal is mostly grown on small-scale, rain-dependent farms, leaving much of the population vulnerable to climate shocks, according to the World Food Programme . However, satellite crop-mapping technologies designed to monitor the impact of climate on farming are largely beyond the reach of the West African country.
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