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Myrtle rust, caused by the fungus Austmpuccinia psidii, is a serious disease, which affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae species are prone to myrtle rust and require a reliable method that allows previsual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants (Syzygium jumbos') that were either inoculated with myrtle rust or maintained disease-free. Using these data, the objectives were to (i) quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for previsual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (Fl score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalized canopy temperature, which was associated with higher stomatai conductance and transpiration. Using NBHI derived from green leaves, excellent previsual classification was achieved 3 DBS, 2 DBS, and 1 DBS (Fl score range = 0.89 to 0.94). The accurate characterization of myrtle rust during previsual and early stages of disease development suggests that a robust detection methodology could be developed within a nursery setting. [ABSTRACT FROM AUTHOR]