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Austropuccinia psidii, cause of myrtle rust, has spread globally where Myrtaceae occur. Multiple strains of A. psidii have been identified, including a unique strain found only in South Africa. The South African strain is a biosecurity concern for species of Myrtaceae worldwide. This is because preliminary testing of South African Myrtaceae suggests it could have a wide host range, and thus has the potential to be invasive. In this study, we assessed the ability of the South African strain to infect other species of Myrtaceae by testing the susceptibility of New Zealand provenance Myrtaceae. Seedlings of four native New Zealand Myrtaceae species (Metrosideros excelsa, Leptospermum scoparium, Kunzea robusta, and Kunzea linearis) were artificially inoculated in South Africa with a single‐uredinium isolate of the South African strain. Fourteen days after inoculation, uredinia, and in many cases telia, had developed on the young leaves and stems of all four host species, which led to shoot tip dieback in the more severe cases. When comparisons were made between the species, K. robusta was the least susceptible to the South African strain of A. psidii, while L. scoparium and M. excelsa were the most susceptible. While only a limited number of seed families were tested, only a small proportion of the seedlings showed resistance to infection by the South African strain. This preliminary testing highlights the potential invasive risk the South African strain poses to global Myrtaceae communities, including New Zealand Myrtaceae. [ABSTRACT FROM AUTHOR]

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]

The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify Metrosideros excelsa (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen Austropuccinia psidii (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models. [ABSTRACT FROM AUTHOR]