Peter Wagstaff of Self Help Africa in Ireland will build an advanced machine learning algorithm that automatically analyzes high-resolution satellite images for near real-time, low-cost detection of crop pests and diseases across wide, varied landscapes. Current detection methods are either resource- or cost-intensive and limited in their ability to provide up-to-date information across large and complex geographic areas. Crop pests and diseases can alter leaf color and expose soil, which can be detected by very high-resolution satellite imaging. They will combine satellite images provided by their partner with field data on the fall armyworm crop pest collected by their project team over 18 months on smallholder plots in the Balaka district in Malawi. These data will be used to train an algorithm to detect pests and diseases. They will use cloud-based workflows to enable computationally intensive processing of large quantities of high-resolution images in near real-time. The accuracy of the algorithm will be evaluated by an independent field survey. Note: This grant is funded by the Foundation for Food and Agriculture Research (FFAR).
More information about Tools and Technologies for Broad-Scale Pest and Disease Surveillance of Crop Plants in Low-Income Countries (Round 22)