Stripe rust, caused byPuccinia striiformisf. sp.tritici(Pst), is one of the important wheat diseases worldwide. In this study, the spectral data were collected from wheat canopy during the latent period inoculated with three different concentrations of urediniospores and classification models based on discriminant partial least squares (DPLS) were built to differentiate leaves with and without infection of the stripe rust pathogen. The effects of different spectra features, wavebands, and the number of the samples used in modeling on the performances of the models were assessed. The results showed that, in the spectral region of 325–1075 nm, the model with the spectral feature of 2nd derivative of Pseudoabsorption index had better accuracy than others. The average accuracy rate was 97.28% for the training set and 92.98% for the testing set. In the waveband of 925–1075 nm, the model with the spectral feature of 1st derivative Pseudoabsorption index had better accuracy than other models, and the average accuracy rates were 98.27% and 94.33% for the training and testing sets, respectively. The results demonstrated that wheat stripe rust in latent period can be qualitatively identified based on the canopy spectral detection. Thus, the method can be used for early monitoring of infections of wheat stripe rust.
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