AI identifies Senegal's smallholder crops 84% of the time using limited training data
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.
Researchers have developed an AI model called Tessera that can accurately identify crops in Senegal's groundnut basin using limited training data. The model, which uses satellite images, achieved 84% accuracy in crop identification, outperforming current methods. Tessera requires significantly fewer computational resources and prelabeled data compared to other methods, making it a more accessible tool for governments and food security organizations in the Global South.
This technology can provide accurate and up-to-date crop statistics, which are crucial for food security planning and target allocation. The research was conducted by lead author Madeline Lisaius, who developed Tessera during her doctoral studies at the University of Cambridge. The findings are particularly relevant given the current El Niño event, which is causing severe drought in West Africa and threatening food security.
Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
