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Advances continue to be made by plant pathologists on topics in plant health, environmental protection and food security. Many advances have been made for individual crops, pathogens and diseases that in many cases have led to their successful management. A wider impact of research depends on recognition of the multifaceted challenges posed by plant diseases and the need to integrate studies in a systems level approach. The adoption of high‐throughput sequencing for diagnosis and detection is widespread but impact depends upon the agricultural and ecological context combined with improved surveillance. Deployment of host resistance in the field needs to be aligned with a greater appreciation of plant genetic diversity and the complementary contribution made by tolerance of disease. Epidemiological understanding of the spatiotemporal spread of plant diseases has improved through population dynamic and genetic analyses. Research emphasis on the plant microbiome has invigorated soil microbial studies, especially for disease complexes and declines, but the challenge is to move to interventions that benefit plant health. Analysis of the impacts of climate change has been made for single‐crop disease studies, but seldom have these been placed in the context of pathogen adaptation, new crops, wild plants, vectors and soil microbes. Advances in informatic analysis illustrate not only the global impacts of plant disease introductions, but also the challenges inherent in marshalling and integrating information. Advances have been made in applying artificial intelligence technologies across many areas of plant pathology but have yet to be integrated within any coordinated research agenda. [ABSTRACT FROM AUTHOR]

An advanced phenotyping protocol using a hyperspectral spectrometer to better understand foliar chemical composition and physiological processes in relation to grapevine yield, quality, biotic and abiotic resistance was initiated with in-house and public spectral resources. The initial result for foliar pigments calibration was promising for classification and measurements to support breeding. We demonstrated the potential of adapting public spectral resources in supporting modern phenotyping in programs with limited resources and when combined with our current effort in deploying marker-assisted selection, the dual innovations provide new information to fast-tracking grapevine research and trait improvement. [ABSTRACT FROM AUTHOR]

Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs. [ABSTRACT FROM AUTHOR]

As advances in sequencing technology continue to reduce the time and cost of generating genomewide genotypic data, the need to develop complementary efficient technologies to likewise increase phenotyping throughput non-destructively and cost-effectively has become imperative in plant breeding. Hyperspectral reflectance, acquired by either imaging or non-imaging spectrometers, measures the interaction of light with vegetation at single leaves or entire canopy. This technology can be used to detect structural, chemical, and functional traits useful in accelerating crop breeding. We deployed a portable field spectroradiometer (SVC-HR1024i) with a wavelength range of 350 nm to 2500 nm to acquire grapevine foliar spectral signatures to improve our phenotyping throughput of foliar chemical and physiological properties including pigments, phenolics, flavonoids, sugars, and to better understand the variations, genetic architecture, and correlations with yield, fruit quality, vine health, and nutrition. Data were collected in Cornell University Grape Breeding Program mapping populations and the diverse genetic collection of the USDA Plant Genetic Resources Unit in Geneva, NY. Preliminary results regarding the accuracy of using the hyperspectral device as an alternative analytical tool for leaf chemical attributes, developed using Partial Least Square regression, showed a correlation between actual and predicted values of 0.46 to 0.99 in the training population (n~79 samples) and 0.28 to 0.63 in the test set (n~32) for leaf pigments - Neoxanthin, Violaxanthin, Lutein, Zeaxanthin, Chlorophyll a, Chlorophyll b and Beta-Carotene. Combined with publicly available resources (foliar prediction models in several other plants from the Ecological Spectral Model Library, ecosml.org), we are exploring QTL associated with phenotypic variation in grape foliar traits and hope to further understand how they relate to fruit quality, vineyard health, and nutrition. As more data points become available and using advanced machine learning models, integrating highthroughput spectral phenomics will help to facilitate the simultaneous improvement of leaf physiological and chemical composition of grape, complementing ongoing efforts to improve disease resistance, other biotic as well as abiotic stress resistances, fruit quality traits and yield. [ABSTRACT FROM AUTHOR]

The U.S. wine and grape industry loses S3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon afterimage acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [ABSTRACT FROM AUTHOR]

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola , can cause 100% yield loss and vine death under conducive conditions. High resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, NY, USA from June to August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July-early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license., Competing Interests: The author(s) declare no conflict of interest.

Nighttime applications of germicidal ultraviolet were evaluated as a means to suppress three diseases of grapevine. In laboratory studies, UV-C light (peak 254 nm, FWHM 5 nm) applied during darkness strongly inhibited the germination of conidia of Erysiphe necator , and at a dose of 200 J/m 2 , germination was zero. Reciprocity of irradiance and duration of exposure with respect to conidial germination was confirmed for UV-C doses between 0 and 200 J/m 2 applied at 4 or 400 s. When detached grapevine leaves were exposed during darkness to UV-C at 100 J/m 2 up to 7 days before they were inoculated with zoospores of Plasmopara viticola , infection and subsequent sporulation was reduced by over 70% compared to untreated control leaves, indicating an indirect suppression of the pathogen exerted through the host. A hemicylindrical array of low-pressure discharge UV-C lamps configured for trellised grapevines was designed and fitted to both a tractor-drawn carriage and a fully autonomous robotic carriage for vineyard applications. In 2019, in a Chardonnay research vineyard with a history of high inoculum and severe disease, weekly nighttime applications of UV-C suppressed E. necator on leaves and fruit at doses of 100 and 200 J/m 2 . In the same vineyard in 2020, UV-C was applied once or twice weekly at doses of 70, 100, or 200 J/m 2 , and severity of E. necator on both leaves and fruit was significantly reduced compared to untreated controls; twice-weekly applications at 200 J/m 2 provided suppression equivalent to a standard fungicide program. None of the foregoing UV-C treatments significantly reduced the severity of P. viticola on Chardonnay vines compared to the untreated control in 2020. However, twice-weekly applications of UV-C at 200 J/m 2 to the more downy mildew-resistant Vitis interspecific hybrid cultivar Vignoles in 2021 significantly suppressed foliar disease severity. In commercial Chardonnay vineyards with histories of excellent disease control in Dresden, NY, E. necator remained at trace levels on foliage and was zero on fruit following weekly nighttime applications of UV-C at 200 J/m 2 in 2020 and after weekly or twice-weekly application of UV-C at 100 or 200 J/m 2 in 2021. In 2019, weekly nighttime applications of UV-C at 200 J/m 2 also significantly reduced the severity of sour rot, a decay syndrome of complex etiology, on fruit of 'Vignoles' but not the severity of bunch rot caused by Botrytis cinerea . A similar level of suppression of sour rot was observed on 'Vignoles' vines treated twice-weekly with UV-C at 200 J/m 2 in 2021. Nighttime UV-C applications did not produce detectable indications of metabolic abnormalities, phytotoxicity, growth reduction, or reductions of fruit yield or quality parameters, even at the highest doses and most frequent intervals employed., Competing Interests: The author(s) declare no conflict of interest.