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The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters., This research was supported by grant: ITS2017-095: Design and Implementation of control strategies for Xylella fastidiosa, Project 5. Government of the Balearic Islands, Spain. Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through gran agreement XF-ACTORS (727987).

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Remote sensing efforts made as part of European initiatives via POnTE, XF-ACTORS and the JRC, as well as through regional programs, have focused, among others, on the development of algorithms for the early detection of Xylella fastidiosa (Xf)-induced symptoms. Airborne campaigns carried out between 2016 and 2019 collected high-resolution hyperspectral and thermal images from infected areas in the Apulia region (Italy), in the province of Alicante and on the island of Mallorca (Spain). The remote sensing imagery collections were performed alongside field surveys and laboratory analyses to assess the presence of Xf, and the severity and incidence of disease in olive and almond trees. Radiative transfer models and machine learning algorithms were used to quantify spectral plant traits for each individual infected tree, assessing their importance as pre visual indicators of Xf-induced stress. These studies conducted across species have demonstrated that specific spectral plant traits successfully revealed Xf induced symptoms at early stages, i.e., before visual symptoms appear. The results show that spectral plant traits contribute differently to symptom detection across host species (olive vs. almond), and that abiotic-induced stress affects the performance of the algorithms used for detecting infected trees. Together, the different European initiatives studying the use of remote sensing to support the monitoring of landscapes for Xylella fastidiosa detection lead us to conclude that the early detection of Xf-induced symptoms is feasible when high-resolution hyperspectral imagery and physically-based plant trait retrievals are used, obtaining accuracies exceeding 92% (kappa>0.8). These results are essential to enable the implementation of effective control and management of plant diseases using airborne- droneand satellite-based remote sensing technologies. Moreover, these large-scale hyperspectral and thermal imaging methods greatly contribute to the future operational monitoring of infected areas at large scales, well beyond what is possible from field surveys and laboratory analyses alone.

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees.

Trabajo presentado en el 1st Congreso Ibérico de Livicultura, las V Jornadas Nacionales del Grupo de Olivicultura de la Sociedad Española de Ciencias Hortícolas (SECH) y el VII Simpósio Nacional de Olivicultura de la Associação Portuguesa de Horticultura (APH), celebrado en Badajoz del 13 al 15 de abril de 2016., Xylella fastidiosa es una bacteria Gram-negativa, habitante del xilema, de muy lento crecimiento in vitro que se transmite de forma no específica por varias especies de insectos que se alimentan del xilema incluidos en Cicadélidos (Hemiptera: Cicadellidae: Cicadellinae) y Cercópidos (Hemiptera: Cercopidea). Esta bacteria causa grandes pérdidas económicas y es el agente causal de la enfermedad Pierce de la vid PD, Vitis vinifera; el enanismo del melocotonero, Prunus persica; la clorosis variegada de los cítricos (CVC) en Citrus spp., entre otras. Las epidemias de X. fastidiosa han dado lugar a pérdidas severas en cítricos en Sudamérica y vid en Norteamérica desde hace décadas. Hasta hace poco, X. fastidiosa solo se había detectado infectando olivo en California, EEUU y en las regiones de La Rioja y Córdoba en Argentina. Sin embargo, en octubre de 2013, una cepa de X. fastidiosa ha sido asociada con un decaimiento rápido de olivo (OQDS) que está causando devastación en la provincia de Lecce, Apulia, Italia, donde está afectando actualmente a más de 10.000 has. La monitorización exhaustiva de todo el territorio de los estados miembros de la UE siguiendo la normativa de la Decisión (EU) 2015/789 ha demostrado también la presencia de X. fastidiosa en Francia en la isla de Córcega y diversas localidades de la Costa Azul (región de los Alpes Marítimos). En esta charla se dará una visión general de los principales avances científicos que se han producido en relación a las cepas de X. fastidiosa interceptadas o presentes en Europa, haciendo un especial énfasis en la variante genética que afecta a olivo. Se presentará el estado actual de la epidemia en olivo en la región de Apulia, las opciones de contención y control de la enfermedad, y los posibles riesgos existentes para la sostenibilidad del olivar. Finalmente se presentarán los objetivos científicos que se van a abordar en el Proyecto de Investigación POnTE: Pest Organisms Threathening Europe financiado por la UE en su programa H2020 que pretende contribuir a diseñar una estrategia europea para la gestión integrada de la enfermedad, con el objetivo de desarrollar las medidas de prevención, detección, contención y erradicación más adecuadas desde el punto de vista técnico, económico y medioambiental.