Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes

Abstract

Hyperspectral reflectance tools have been used to detect multiple pathogens in agricultural settings and single sources of infection or broad declines in forest stands. However, differentiation of any one disease from other sources of tree stress is integral for stand and landscape-level applications in mixed species systems. We tested the ability of spectral models to differentiate oak wilt, a fatal disease in oaks caused by Bretziella fagacearum ``Bretz'', from among other mechanisms of decline. We subjected greenhouse-grown oak seedlings (Quercus ellipsoidalis ``E.J. Hill'' and Quercus macrocarpa ``Michx.'') to chronic drought or inoculation with the oak wilt fungus or bur oak blight fungus (Tubakia iowensis ``T.C. Harr. & D. McNew''). We measured leaf and canopy spectroscopic reflectance (400–2400 nm) and instantaneous photosynthetic and stomatal conductance rates, then used partial least-squares discriminant analysis to predict treatment from hyperspectral data. We detected oak wilt before symptom appearance, and classified the disease with high accuracy in symptomatic leaves. Classification accuracy from spectra increased with declines in photosynthetic function in oak wilt-inoculated plants. Wavelengths diagnostic of oak wilt were only found in non-visible spectral regions and are associated with water status, non-structural carbohydrates and photosynthetic mechanisms. We show that hyperspectral models can differentiate oak wilt from other causes of tree decline and that detection is correlated with biological mechanisms of oak wilt infection and disease progression. We also show that within the canopy, symptom heterogeneity can reduce detection, but that symptomatic leaves and tree canopies are suitable for highly accurate diagnosis. Remote application of hyperspectral tools can be used for specific detection of disease across a multi-species forest stand exhibiting multiple stress symptoms.

Leaf hyperspectral reflectance as a potential tool to detect diseases associated with vineyard decline

“…Many vineyards show mixed infections by GTDs and GLRaV, making it challenging to inspect visually all acreage within the optimal period for disease expression (Mac Donald et al 2016). The photosynthetic process and the plant tissue cell structures attacked by pathogens suffer alterations that modify the vegetation and electromagnetic radiation interaction and, consequently, leaf and canopy reflectances in the visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR) (Prabhakar et al 2012;Calcante et al 2012;Martinelli et al 2015;Mahlein 2016;Heim et al 2018;Zarco-Tejada et al 2018;Gold et al 2019Gold et al , 2020Fallon et al 2020). Light reflection in the VIS, NIR, and SWIR provides a comprehensive assessment of plant responses to diseases due to changing in biochemical (e.g., leaf pigments, nutrient composition, and secondary metabolism) and physiological (e.g., photosynthetic activity and water) aspects (Couture et al 2018).…”

“…In the vineyears of the Serra Gaúcha, the most common scenario is the cooccurrence of plants with GTDs and GLRaV symptoms leading to plant decline, for which no spectral signature data are available for such situation. The use hyperspectral data to detect crop diseases is a promising and novel approach for classifying disease status as well as for nondestructive assessment of specific responses of the plant to disease infection (Couture et al 2018;Zarco-Tejada et al 2018;Fallon et al 2020). In this study, we hypothesized that (1) leaves infected by fungal or viral disease symptoms associated with vine decline show a distinct spectral behavior in comparison with asymptomatic leaves; (2) the hyperspectral reflectance changes in symptomatic leaves is linked to biophysical or biochemical characteristics associated with the specific pathogen infection; (3) it is necessary to reduce the high dimensionality of hyperspectral data defining the important wavelengths for diseases differentiation.…”

Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

“…3 & 4). These results suggest that oak wilt infection in adult trees in natural ecosystems triggers declines in photosynthetic rate, stomatal conductance, and water content just as in greenhouse seedlings (Fallon et al, 2020). All indices showed greater sensitivity in late August relative to late July suggesting that oak wilt symptoms had progressed.…”

A multi-omics approach to solving problems in plant disease ecology

“…Only recently have plant pathologists begun to take advantage of the spectranomics trail blazed by terrestrial ecologists. Recent studies have helped establish in-situ (or foliar) and imaging spectroscopy (also known as “hyperspectral imaging”) as effective tools for early, non-destructive, and scalable biotic stress detection in natural and agroecosystems [ 83 – 86 ]. Both beneficial [ 87 ] and parasitic plant-microbe interactions [ 88 ] impact a variety of plant traits that can be non-destructively sensed.…”

“…This approach has been successful in increasing efficiency and accuracy in agronomic crop breeding pipelines [ 90 – 92 ]. Thus, spectral quantification of foliar functional traits allows us to detect, map, and model the biochemical and physiological pathosystem processes that engender our capacity to use spectroscopy for plant-microbe interaction sensing [ 86 , 93 – 99 ].…”