Researchers from the University of Manchester are leveraging machine learning and satellite imagery to map irrigation systems across Ghana, offering a powerful new tool for improving water management and agricultural resilience. By analyzing high-resolution satellite data through advanced AI models, the team has been able to identify and classify irrigation infrastructure with remarkable accuracy, even in remote and densely vegetated regions where traditional surveys are difficult or impossible. This approach allows for large-scale, cost-effective monitoring of water use across farmland, providing detailed insights into where irrigation is being used, how it’s changing over time, and which areas remain underserved. The resulting maps are helping policymakers, agricultural extension services, and development organizations target interventions more effectively. The project is especially significant for smallholder farmers, who make up the majority of Ghana’s agricultural workforce but often lack access to reliable water sources. By identifying gaps in irrigation coverage, the data can inform the deployment of new technologies—such as solar-powered pumps and drip irrigation systems—tailored to local conditions and needs. Beyond improving water access, the initiative supports broader goals of food security and climate resilience. As climate change increases the frequency of droughts and unpredictable rainfall, efficient irrigation becomes critical to maintaining crop yields and stabilizing incomes for rural communities. The team’s work also highlights the growing role of AI in sustainable development. By transforming vast amounts of satellite data into actionable intelligence, machine learning enables real-time monitoring and long-term planning at a scale previously unattainable. The findings are expected to guide future investments in agricultural infrastructure and digital tools designed to empower farmers and build more resilient food systems across West Africa.