Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

Plant pathogens cause significant losses to agricultural yields, and increasingly threaten food security, ecosystem integrity, and societies in general. Xylella fastidiosa (Xf) is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates a dramatically broadened geographic range. The Xf pathogen has thus re-emerged as a global threat, with its poorly contained expansion in Europe creating a socio-economic, cultural, and political disaster. Xf represents a threat of global proportion because it can infect over 350 plant species worldwide, and the early detection of Xf has been identified as a critical need for its eradication. Here, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography reveal Xf infection in trees before symptoms are visible. We obtained accuracies of disease detection exceeding 80% when high-resolution solar-induced fluorescence quantified by 3D simulations and thermal-based stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected via spectral plant trait alterations (presumed false positives) developed Xf symptoms four months later at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant trait alterations caused by Xf infection are detectable at the landscape scale before symptoms are visible, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.JRC.D.1-Bio-econom