A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.
