Yellow rust (YR) wheat disease is one of the major threats to worldwide wheat production, and it often spreads rapidly to new and unexpected geographic locations. To cope with this threat, integrated pathogen management strategies combine disease-resistant plants, sensors monitoring technologies, and fungicides either preventively or curatively, which come with their associated monetary and environmental costs. This work presents a methodology for timely detection of YR that cuts down on hardware and computational requirements. It enables frequent detailed monitoring of the spread of YR, hence providing the opportunity to better target mitigation efforts which is critical for successful integrated disease management. The method is trained to detect YR symptoms using reflectance spectrum (VIS–NIR) and a classification algorithm at different stages of YR development to distinguish them from typical defense responses occurring in resistant wheat. The classification method was trained and tested on four different spectral datasets. The results showed that using a full spectral range, a selection of the top 5% significant spectral features, or five typical multispectral bands for early detection of YR in infected plants yielded a true positive rate of ~ 86%, for infected plants. The same data analysis with digital camera bands provided a true positive rate of 77%. These findings lay the groundwork for the development of high-throughput YR screening in the field implementing multispectral digital camera sensors that can be mounted on autonomous vehicles or a drone as part of an integrated disease management scheme.