This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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.
A new analysis of Senegalese croplands using an artificial intelligence model developed at the University of Cambridge shows how technological benefits enjoyed by industrial agriculture could be brought to smallholder farms in Senegal and other countries in the Global South. Researchers used Cambridge's Tessera, an open-source AI model trained on satellite images, to map crops in Senegal's groundnut basin. They found that Tessera was more accurate than current methods, getting the right result 84% of the time in tests while using only a fraction of the computational resources and prelabeled data.
In one scenario, it performed 28% better than the next-best model. This suggests that Tessera could become an important tool for governments and food security organizations, particularly in areas in the Global South where ground data is difficult or expensive to gather, said lead author Madeline Lisaius, who helped develop Tessera when she was a doctoral student at Cambridge's Department of Computer Science and Technology. "Accurate and up-to-date crop statistics can guide food security planning and help decide where best to target support.
But most local governments and bodies can only afford to collect ground data every few years," Lisaius said. "With Tessera, you can train on the data you already have and extend it into the years in between, with more accurate crop information than baseline methods have ever been able to provide." Governments, NGOs and other food security organizations can begin using the technology to produce their own crop statistics now, she added. The research is given added urgency by this year's El Niño, which scientists say is the strongest ever recorded.
The climate phenomenon is known to disrupt rainfall patterns in West Africa, and past strong events have brought prolonged drought to the region. "Reliable agricultural data is essential to anticipate food security and climate-related risks. In Senegal, WFP is working with national partners to explore how geospatial data and artificial intelligence can strengthen food security monitoring systems and support faster, more informed decision-making," said Pierre Lucas, representative and country director of the United Nations World Food Programme in Senegal.
The study, "Embedding-based Crop Type Classification in the Groundnut Basin of Senegal," was published Sept. 29 in the journal Environmental Research: Food Systems. Like much of West Africa, Senegal's agriculture is dominated by smallholder producers, who grow crops on plots not much larger than a football field. As the paper makes clear, knowing what's grown where allows for "informed decision-making at regional, national and global scales that can mean survival for vulnerable people." Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. d research that matter—daily or weekly.
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