Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas

© 2015 by the authors. Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.Financial support for this research was provided by Project P08-AGR-03528 from “Consejería de Economía, Innovación y Ciencia” of Junta de Andalucía and the European Social Fund, and projects AGL-2012-37521 and AGL2012-40053-C03-01 from the Spanish “Ministerio de Economía y Competitividad” and the European Social Fund. Rocio Calderón is a recipient of research fellowship BES-2010-035511 from the Spanish “Ministerio de Ciencia e Innovación”.We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Soil temperature determines the reaction of olive cultivars to verticillium dahliae pathotypes

Development of Verticillium wilt in olive, caused by the soil-borne fungus Verticillium dahliae, can be influenced by biotic and environmental factors. In this study we modeled i) the combined effects of biotic factors (i.e., pathotype virulence and cultivar susceptibility) and abiotic factors (i.e., soil temperature) on disease development and ii) the relationship between disease severity and several remote sensing parameters and plant stress indicators. Methodology: Plants of Arbequina and Picual olive cultivars inoculated with isolates of defoliating and non-defoliating V. dahliae pathotypes were grown in soil tanks with a range of soil temperatures from 16 to 32°C. Disease progression was correlated with plant stress parameters (i.e., leaf temperature, steady-state chlorophyll fluorescence, photochemical reflectance index, chlorophyll content, and ethylene production) and plant growth-related parameters (i.e., canopy length and dry weight). Findings: Disease development in plants infected with the defoliating pathotype was faster and more severe in Picual. Models estimated that infection with the defoliating pathotype was promoted by soil temperatures in a range of 16 to 24°C in cv. Picual and of 20 to 24°C in cv. Arbequina. In the non-defoliating pathotype, soil temperatures ranging from 16 to 20°C were estimated to be most favorable for infection. The relationship between stress-related parameters and disease severity determined by multinomial logistic regression and classification trees was able to detect the effects of V. dahliae infection and colonization on water flow that eventually cause water stress. Conclusions: Chlorophyll content, steady-state chlorophyll fluorescence, and leaf temperature were the best indicators for Verticillium wilt detection at early stages of disease development, while ethylene production and photochemical reflectance index were indicators for disease detection at advanced stages. These results provide a better understanding of the differential geographic distribution of V. dahliae pathotypes and to assess the potential effect of climate change on Verticillium wilt development.Financial support for this research was provided by Project P08-AGR-03528 from ‘‘Consejería de Economía, Innovación y Ciencia’’ of Junta de Andalucía and the European Social Fund (JANC), and projects AGL-2012-37521 (JANC) and AGL2012-40053-C03-01 (PJZT) from the Spanish ‘‘Ministerio de Economia y Competitividad’’ and the European Social Fund. R. Calderón is a recipient of research fellowship BES-2010-035511 from the Spanish ‘‘Ministerio de Ciencia e Innovación’’ and C. Lucena was a recipient of a JAE-DOC postdoctoral contract from ‘‘Consejo Superior de Investigaciones Científicas’’ (CSIC) co-funded by the European Social Fund. TPeer Reviewe

Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r2VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.Data collection was partially supported by the European Union's Horizon 2020 research and innovation programme through grant agreements POnTE (635646) and XF-ACTORS (727987). A. Hornero was supported by research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK). The authors would also like to thank QuantaLab-IAS-CSIC (Spain) for laboratory assistance and the support provided during the airborne campaigns and image processing. B. Landa, C. Camino, M. Montes-Borrego, M. Morelli, M. Saponari and L. Susca are acknowledged for their support during the field campaigns, as well as IPSP-CNR and Dipartimento di Scienze del Suolo (Università di Bari, Italy) as host institutions

Role of COQ4 on mitochondrial DNA maintenance

Resumen del póster presentado en Mitochondrial Medicine, celebrado en Hinxton (Inglaterra) del 09 al 11 de mayo de 2018.Coenzyme Q (CoQ) is a lipidic molecule composed by a hydroquinone head and an isoprenoid chain. Since its discovery, several functions have been assigned to CoQ, being the transfer of electrons from complexes I and II to complex III in the mitochondrial respiratory chain the best known. CoQ also receives electrons from other dehydrogenases involved in different cellular processes and it is a potent membrane antioxidant. CoQ is endogenously synthesized by a set of enzymes forming a biosynthetic complex in the mitochondrial inner membrane, which has been mostly studied in yeast models. Defects in any of the genes coding for these proteins result in reduced levels of CoQ and, consequently, defects in energy production. COQ4 is one of the proteins involved in CoQ biosynthesis, but its exact enzymatic activity is still unknown. COQ4 KO HEK 293T-Rex/Flp-In cells generated by CRISPR/Cas9, as well as patient fibroblasts carrying mutations in COQ4 show the accumulation of a yet uncharacterised biosynthetic intermediate that lacks redox activity. Two candidate molecules have emerged from mass spectrometry analysis performed to identify this intermediate. On the other hand, the KO cells show a surprising phenotype related to mtDNA metabolism which may be due either to the lack of de novo synthesis of CoQ, to the biosynthetic complex instability itself, to the presence of the intermediate, or to a different and yet not characterized role of COQ4. Altogether, these results indicate a possible double function of the COQ4 protein

Recent research accomplishments on early detection of Xylella fastidiosa outbreaks in the Mediterranean Basin

Xylella fastidiosa is a major transboundary plant pest, causing severe socioeconomic impacts. Development of preventive strategies and methods for surveillance, early detection, monitoring, and accurate diagnosis of X. fastidiosa and its vectors, are keys to preventing the effects of this plant pathogen, and assist timely eradication or optimisation of containment measures. This review focuses on approaches for early detection of X. fastidiosa in the Mediterranean Basin, including development of climatic suitability risk maps to determine areas of potential establishment, and epidemiological models to assist in outbreak management through optimized surveillance and targeted responses. The usefulness of airborne hyperspectral and thermal images from remote sensing to discriminate X. fastidiosa infections from other biotic- and abiotic-induced spectral signatures is also discussed. The most commonly used methods for identifying X. fastidiosa in infected plants and vectors, and the molecular approaches available to genetically characterize X. fastidiosa strains, are described. Each of these approaches has trade-offs, but stepwise or simultaneous combinations of these methods may help to contain X. fastidiosa epidemics in the Mediterranean Basin

Genetic diversity and population structure of Ascochyta rabiei from the western Iranian Ilam and Kermanshah provinces using MAT and SSR markers

Knowledge of genetic diversity in A. rabiei provides different levels of information that are important in the management of crop germplasm resources. Gene flow on a regional level indicates a significant potential risk for the regional spread of novel alleles that might contribute to fungicide resistance or the breakdown of resistance genes. Simple sequence repeat (SSR) and mating type (MAT) markers were used to determine the genetic structure, and estimate genetic diversity and the prevalence of mating types in 103 Ascochyta rabiei isolates from seven counties in the Ilam and Kermanshah provinces of western Iran (Ilam, Aseman abad, Holaylan, Chardavol, Dareh shahr, Gilangharb, and Sarpul). A set of 3 microsatellite primer pairs revealed a total of 75 alleles; the number of alleles varied from 15 to 34 for each marker. A high level of genetic variability was observed among A. rabiei isolates in the region. Genetic diversity was high (He = 0.788) within populations with corresponding high average gene flow and low genetic distances between populations. The smallest genetic distance was observed between isolates from Ilam and Chardavol. Both mating types were present in all populations, with the majority of the isolates belonging to Mat1-1 (64%), but within populations the proportions of each mating type were not significantly different from 50%. Results from this study will be useful in breeding for Ascochyta blight-resistant cultivars and developing necessary control measures

Integrating an epidemic spread model with remote sensing for Xylella fastidiosa detection

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

Dafny with traits: verifying object oriented programs

Dafny is a programming language supporting verified high level programming. It has many features that a modern programming language has, like classes, generic classes, functions, and, methods. However, some aspects of object oriented programming do not exist in Dafny. For instance, it is not possible to write programs with classes and subclasses and then verify the subclasses. In order to enrich the language with the mentioned feature, this thesis introduces traits to Dafny. A trait in Dafny may introduce states, methods and functions with or without bodies. A class, then, inherits from a trait and may override the body-less methods and functions. There are also specifications for methods and functions in a trait that specify the intention of a particular method or function. In terms of the specifications, the class must provide the specifications, for annotating the functions and methods, possibly stronger. This has the drawback of repeating the specifications but it also increases readability as one can look at the class and immediately figure out what specifications govern the behavior of a method or a function. The new feature, traits, provides polymorphism, information hiding, and reusability. Dynamic dispatch is now also available with the help of the introduced traits

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)

Divergent abiotic spectral pathways unravel pathogen stress signals across species

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.The study was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from ‘Programa Estatal de I + D + I Orientada a los Retos de la Sociedad’ of Spain and FEDER, Intramural Project 201840E111 from CSIC, and Project ITS2017-095 Consejeria de Medio Ambiente, Agricultura y Pesca de las Islas Baleares, Spain. The views expressed are purely those of the writers and may not in any circumstance be regarded as stating an official position of the European Commission