Previous studies investigating the performance of transmission and reflection datasets for disease detection showed inconsistent results. Within the studies, the performance of transmission imaging varied significantly for the detection of biotroph and necrotrophy plant pathogens, while reflection imaging showed excellent results in both studies. The current study explores the hypothesis that the disparity between these results might be correlated with the different interactions of the respective pathogens with the host plants and the way light interacts with the plant tissue. Pyrenophora teres f. teres and Puccinia hordei—the causative agents of net blotch and brown rust in barley—have been investigated with focus on early-stage detection and quantification (disease severity) of symptoms. Datasets of hyperspectral imaging time-series measurements were analysed through application of multiple data analysis methods (support vector machines; principal component analysis with following distance classifier; spectral decomposition) in order to compare the performance of both datasets for the detection of disease symptoms. It could be shown that transmittance-based brown rust detection (e.g. 12% disease severity) is outperformed by reflectance-based detection (e.g. 36% disease severity) regardless of the algorithm. However, both the detection and quantification of brown rust through transmittance were more accurate than those of powdery mildew in earlier studies. Transmittance and reflectance performed similar for the detection of net blotch disease during the experiments
(~ 1% disease severity for reflection and transmission). Each data analysis method outperformed manual rating in terms of disease detection (e.g. 15% disease severity according to manual rating and 36% through support vector machines for rust reflection data). Except for the application of a distance classifier on net blotch transmittance data, it could be shown that pixels, which were classified as symptomatic through the data analysis methods while estimated to represent healthy tissue during manual rating, correlate with areas at the edges of manually detected symptoms. The results of this study support the hypothesis that transmission imaging results are highly correlated with the type of plant–pathogen interaction of the respective pathogens, offering new insights into the nature of transmission-based hyperspectral imaging and its application range.