In September 2026, I travelled to Malawi to participate in an internal team workshop for the Geo-AI and Citizen Science-Powered Soybean Rust Surveillance and Forecasting Project. The workshop brought project partners together to align activities and strengthen collaboration in addressing soybean rust.
The project brings together NM-AIST, IITA, Alliance Bioversity & CIAT, Solidaridad and Good Nature Agro. Through this collaboration, we aim to combine research expertise, geospatial information and community participation to support disease surveillance and forecasting.
Our discussions focused on coordinating the work ahead and building a shared understanding of each partner’s contribution. Connecting field observations with environmental data and AI methods offers opportunities to provide timely information about soybean rust risks.
We also worked with farmers, extension agents and partners to refine and validate our AI model. These interactions also helped us understand how farmers identify, assess and respond to crop diseases, providing insights to guide the development of tools suited to their needs.
The workshop and field engagement reinforced the importance of connecting research with farmers’ experiences. Reliable data, local knowledge and collaboration are essential to developing agricultural technologies that can support decisions in the field.