Evaluating early detection of grapevine trunk diseases from asymptomatic leaves based on hyperspectral imagery

Background Horticultural crops propagated vegetatively are at risk of infection by wood-colonizing and vascular pathogens from infected cuttings. Diseases caused by such pathogens are difficult to diagnose. Because their chronic infections cannot be cured, disease diagnosis in the nursery could be an efficient approach to prevent spread to perennial plantings. Early detection of grapevine trunk diseases is confounded by a delay of up to a year before symptoms appear. This incubation period exceeds the 6 to 8 months grapevines are grown in the nursery; visual inspection for leaf symptoms is thus not a means of trunk-disease diagnosis. We evaluated hyperspectral imagery as a non-destructive means. Anatomical, physiological, and transcriptomic host responses occur within weeks of infection, and may be associated with changes in hyperspectral reflectance of asymptomatic leaves. If so, hyperspectral imagery might have promise in trunk-disease diagnosis in the nursery. For 14 weeks, we compared hyperspectral reflectance (410 to 1,000 nm) of asymptomatic leaves on potted plants, the woody stems of which were inoculated with fungi that cause trunk diseases Botryosphaeria dieback ( Neofusicoccum parvum ) and Esca ( Phaeomoniella chlamydospora and Tropicoporus texanus ), to those of non-inoculated controls. Results Destructive sampling of woody stems, at weeks 2, 8, and 14, confirmed the largest internal wood lesions in N . parvum -inoculated plants. Normalized difference spectral indices (NDSIs) of wavelengths in the visible (VIS) spectrum (e.g., 670 nm) and at the ‘red edge’ (700 – 730 nm) distinguished controls from inoculated plants, at weeks 8 and 9. By week 14, two pairs of treatments ( N . parvum and P. chlamydospora versus control and T. texanus ) were distinguished, based on separate Principal Component Analyses (PCAs) of the VIS and near-infrared (NIR) spectra, on the strength of associated NDSIs, and on overlap of their spectral curves. Partial least-squares discriminant analyses (PLS-DAs), under a 2-class model, identified VIS and NIR wavelengths that distinguished leaves of control plants versus each inoculation treatment, albeit with discriminant accuracies of 55 to 79%. Conclusions Further research is needed to substantiate the prospects of hyperspectral imaging as an early detection tool of grapevine trunk diseases with potential application at a commercial scale, under nursery conditions.