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

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Authors P. J. Zarco-Tejada, C. Camino, P. S. A. Beck, R. Calderon, A. Hornero, R. Hernández-Clemente, T. Kattenborn, M. Montes-Borrego, L. Susca, M. Morelli, V. Gonzalez-Dugo, P. R. J. North, B. B. Landa, D. Boscia, M. Saponari & J. A. Navas-Cortes
Journal/Conference Name Nature Plants
Paper Category
Paper Abstract Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security1, ecosystem integrity and societies in general2,3,4,5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment6. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa’s geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage7,8. X. fastidiosa can infect more than 350 plant species worldwide9, and early detection is critical for its eradication8. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal 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 by spectral plant-trait alterations, developed X. fastidiosa symptoms 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 X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.
Date of publication 2018
Code Programming Language R
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