Results of the remote sensing system for detection and monitoring of Xf

Plant pathogens cause significant losses to agricultural yields, and increasingly threaten food security (Paini et al., 2016), ecosystem integrity, and societies in general (Boyd et al., 2013; Fisher et al., 2012; Flood, 2010; Strange & Scott, 2005). Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment (Purcell, 1997). Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that Xf’s geographic range has broadened considerably, positioning Xf as a re-emerging global threat that has caused socio-economic and cultural damage (Stokstad, 2015; Almeida, 2016). Xf can infect over 560 plant species worldwide (EFSA, 2018), and its early detection is critical for its eradication. The work carried out demonstrates that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal Xf infection in olive trees before symptoms are visible. The accuracy of disease detection, confirmed by quantitative polymerase chain reaction assay (qPCR), exceeds 80% when high-resolution fluorescence quantified by 3D simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Visually asymptomatic trees originally scored as affected via spectral plant trait alterations developed Xf symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. Then, the fact that spectral plant trait alterations caused by Xf infection are detectable pre-visually at the landscape scale is demonstrated, which is a critical requirement to help eradicate one of the most devastating plant diseases worldwide.