This study establishes a method to detect and distinguish between brown rust and yellow rust on wheat leaves based on hyperspectral imaging at the leaf scale under controlled laboratory conditions. A major problem at this scale is the generation of representative and correctly labelled training data, as only mixed spectra comprising plant and fungal material are observed. For this purpose, the pure spectra of rust spores of Puccinia triticina and P. striiformis, causal agents of brown and yellow rust, respectively, were used to serve as a spectral ﬁngerprint for the detection of a speciﬁc leaf rust disease. A least-squares factorization was used on hyperspectral images to unveil the presence of the spectral signal of rust spores in mixed spectra on wheat leaves. A quantiﬁcation of yellow and brown rust, chlorosis and healthy tissue was veriﬁed in time series experiments on inoculated plants. The detection of fungal crop diseases by hyperspectral imaging was enabled without pixel-wise labelling at the leaf scale by using reference spectra from spore-scale observations. For the ﬁrst time, this study shows an interpretable decomposition of the spectral reﬂectance mix-ture during pathogenesis. This novel approach will support a more sophisticated and precise detection of foliar diseases of wheat by hyperspectral imaging.