Since 2010 Australian ecosystems and managed landscapes have been severely threatened by the invasive fungal pathogen Austropuccinia psidii. Detecting and monitoring disease outbreaks is currently only possible by human assessors, which is slow and labour intensive. Over the last 25 years, spectral vegetation indices (SVIs) have been designed to assess variation in biochemical or biophysical traits of vegetation. However, diagnosis of individual diseases based on classical SVIs is currently not possible because they lack disease specificity. Here, a novel spectral disease index (SDI), the lemon myrtle–myrtle rust index (LMMR), has been developed. The index was designed from hyperspectral leaf‐clip data collected at a lemon myrtle plantation in New South Wales, Australia. A total of 236 fungicide‐treated (disease free) and 228 untreated (diseased) lemon myrtle leaves were sampled and a random forest classifier was used to show that the LMMR discriminates those classes with an overall accuracy of 90%. Compared to three classical SVIs (PRI, MCARI, NBNDVI), commonly applied for stress detection, the LMMR clearly improved classification accuracies (58%, 67%, 60%, respectively). If the LMMR can be validated on independent datasets from similar and different host species, it could enable land managers to reduce disease impact by earlier control. There might also be potential to collect useful data for epidemiology models. Calculating the LMMR based on hyperspectral data collected from aerial platforms (e.g. drones) would allow for rapid and high‐capacity screening for disease outbreaks.
Supplementary notes can be added here, including code and math.