Divergent abiotic spectral pathways unravel pathogen stress signals across species

Abstract

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world’s most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic–abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.

Digital plant pathology: a foundation and guide to modern agriculture

“…The foundational spectranomics approach offers an explanation as to why sensing technologies are capable of disease detection in the first place. Remote imaging spectroscopy assesses the sum impact of the fundamental biochemical, structural and physiological processes that underlie the diseased plant phenotype (Mahlein et al 2012 , Leucker et al 2016 , Kuska et al 2017 , 2018a , 2018b , 2019 , Zarco-Tejada et al 2018 , 2021 ; Asner et al 2018; Sapes et al 2021 ). Further ranges of the electromagnetic spectrum can also provide interesting information, but often it is not possible to characterize the determined changes to a specific cause (Mahlein 2016 ; Simko et al 2016 ).…”

“…Following up on this work, the authors found that NPQI was only indicative of asymptomatic X. fastidiosa infection in irrigated almond groves. This eventually led to the discovery of the existence of divergent pathogen- and host-specific spectral pathways in response to abiotic and biotic stresses that yield a similar visual manifestation (Zarco-Tejada et al 2021 ). Even though both drought and bacterial infection cause the plant to wilt, the mechanisms by which they do so are different, and this difference could be captured with spectroscopy.…”

Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

“…Plant pathogens damage, impair, and/or alter plant function, thus changing foliar composition, by such mechanisms as production of systemic effectors or secondary metabolites or by the physical presence of pathogen structures, such as hyphae and spores ( 21 ). Abiotic and biotic stresses have divergent spectral pathways, which is why spectroscopy can be used to differentiate between them ( 73 ). Broadband and multispectral methods relying on visible (Vis) and near-infrared (NIR) reflectance indices, such as the normalized difference vegetation index (NDVI), have been used to sense late-stage plant disease since the 1980s ( 22 , 23 ).…”

The potential of UAV-based sun-induced chlorophyll fluorescence in understanding crop photosynthesis

“…Reflectance-based VIs, traits inverted from hyperspectral reflectance using RTM, temperaturebased indices from thermal infrared data, and SIF indices can be the input parameters of a machine learning model (Zarco-Tejada et al, 2021). The model keeps the parameters with high importance in stress detection and further combines the diverse input to monitor the presence and severity of a certain stress.…”

“…This approach can be a trend for the development of global disease-detection models. Zarco-Tejada et al (2021) successfully decoupled the biotic stress caused by vascular system-invading pathogens and abiotic stress imposed by water limitation with a random forest (RF) algorithm. The spectral traits estimated by RTM inversion, CWSI, and far-red SIF were used as inputs for the RF models.…”