The difference in spectral reflectance between healthy and diseased wheat plants infected with Puccinia striiformis (yellow rust) was investigated. In-field spectral images were taken with a spectrograph mounted at spray boom height. A normalisation method based on reflectance and illumination adjustments was applied. To consider the entire canopy reflection, a spatially moving average was introduced. A classification model based on quadratic discrimination was built on a selected group of wavebands obtained by stepwise variable selection. Through this method, confusion rates dropped from 12 to 4% error classification, based on four different wavebands. These results are very encouraging for the development of a cost-effective optical device for recognising diseases, such as yellow rust, in the field in early spring. (C) 2003 Silsoe Research Institute. All rights reserved.