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Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees. In this study, Dutch elm disease (DED; caused by Ophiostoma novo-ulmi,) and American elm (Ulmus americana) was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper- and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection. Hyper- and multi-spectral images were collected from leaves of American elm genotypes with varied disease susceptibilities after mock-inoculation and inoculation with O. novo-ulmi under greenhouse conditions. Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes. Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED. Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees. In addition, spectral and spatial feature patterns identified in the machine learningbased models were found relative to the DED susceptibility of elm genotypes. Though further studies are needed to assess applications in other pathosystems, hyper- and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees. [ABSTRACT FROM AUTHOR]

Austrian pine (Pinus nigra) is a valuable component of the urban landscape in the Midwestern USA. In this area, it is impacted by the fungal pathogen Diplodia sapinea, which causes a tip blight and canker on infected trees. While the disease can be managed through the application of fungicides and/or by preventing environmental conditions that are favorable for the pathogen, these practices only temporarily alleviate the problem. A more sustainable solution is to use resistant trees. The objective of this study was to evaluate whether Fourier-transform infrared (FT-IR) spectroscopy combined with chemometric analysis can distinguish between trees that vary in susceptibility to D. sapinea. Trees were phenotyped for resistance to D. sapinea by artificially inoculating shoots and measuring ensuing lesions seven days following inoculation. Then, three different chemometric approaches, including a type of machine learning called support vector machine (SVM), were used to evaluate whether or not trees that varied in susceptibility could be distinguished. Trees that varied in susceptibility could be discriminated based on FT-IR spectra collected prior to pathogen infection using the three chemometric approaches: soft independent modeling of class analogy, partial least squares regression, and SVM. While further validation of the predictive models is needed, the results suggest that the approach may be useful as a tool for screening and breeding Austrian pine for resistance to D. sapinea. Furthermore, this approach may have wide applicability in other tree/plant pathosystems of concern and economic value to the nursery and ornamental industries. [ABSTRACT FROM AUTHOR]

Oaks (Quercus spp.) are keystone species in many ecosystems and are ecologically as well as economically valuable. The objective of this study was to gather and evaluate information from a diverse group of oak experts on current and future biotic and abiotic threats to oaks in the eastern United States. Using a Delphi survey method with three iterative surveys, we found that oak decline and climate change were identified as critical current and future threats, respectively, in this region. Focusing on climate change, experts were asked a series of questions to assess its potential future temporal and spatial impact on oaks. With respect to climate change, the majority of experts surveyed believe climate change will manifest gradually, although they were generally uncertain about the geographical distribution of climate change in the future, i.e., the areas where oaks are likely to be impacted by climate change in the future. New/emerging pests and pathogens were seen as the most critical future threat by the third survey round. Results from this study can be used to better inform management practices and research priorities for ensuring resilient oak resources for the future. [ABSTRACT FROM AUTHOR]

Due to the devastating effects of butternut canker disease, efforts to protect the endangered butternut ( Juglans cinerea ) tree through resistance breeding have been a primary focus of forest restoration efforts. Walnut witches'-broom (WWB) disease poses a serious threat to these restoration efforts. This study sought to confirm the presence of the WWB disease phytoplasma, ' Candidatus Phytoplasma pruni', in butternut research plantings in Indiana using molecular methods and Sanger sequencing and to identify butternut families affected by the disease. We also sought to better understand the incidence of the WWB phytoplasma in asymptomatic trees and asymptomatic branches of symptomatic trees to better direct management decisions. Sanger sequencing confirmed the presence of the WWB phytoplasma in the butternut restoration plantings, the first confirmation in Indiana based on sequencing to our knowledge, in both symptomatic and some asymptomatic trees. In addition, the WWB phytoplasma was detected in asymptomatic branches of symptomatic trees, indicating that phytoplasma infection is not necessarily localized to symptomatic tissues in a tree. Trees with positive molecular confirmation of the WWB phytoplasma consisted of 23 different butternut families and one family of Japanese walnut ( J. ailantifolia ), which is considered to be one of the most susceptible species to WWB disease. Based on these findings, future studies should prioritize identifying the hybridity and pedigrees of families and their susceptibility to WWB disease to aid in butternut restoration efforts., Competing Interests: The author(s) declare no conflict of interest.