145 research outputs found

    Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

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    The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales

    Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellite-Based Monitoring Data

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    Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and vary widely in their dispersal pattern, prevalence, and severity. It remains unclear what the impact disease will have on sustainable crop yields in the future. Agricultural stakeholders are increasingly under pressure to adapt their decision-making to make more informed and efficient use of irrigation water, fertilizers, and pesticides. They also face increasing uncertainty in how best to respond to competing health, environment, and (sustainable) development impacts and risks. Disease dynamics involves a complex interaction between a host, a pathogen, and their environment, representing one of the largest risks facing the long-term sustainability of agriculture. New airborne inoculum, weather, and satellite-based technology provide new opportunities for combining disease monitoring data and predictive models—but this requires a robust analytical framework. Integrated model-based forecasting frameworks have the potential to improve the timeliness, effectiveness, and foresight for controlling crop diseases, while minimizing economic costs and environmental impacts, and yield losses. The feasibility of this approach is investigated involving model and data selection. It is tested against available disease data collected for wheat stripe (yellow) rust (Puccinia striiformis f.sp. tritici) (Pst) fungal disease within southern Alberta, Canada. Two candidate, stochastic models are evaluated; a simpler, site-specific model, and a more complex, spatially-explicit transmission model. The ability of these models to reproduce an observed infection pattern is tested using two climate datasets with different spatial resolution—a reanalysis dataset (~55 km) and weather station network township-aggregated data (~10 km). The complex spatially-explicit model using weather station network data had the highest forecast accuracy. A multi-scale airborne surveillance design that provides data would further improve disease risk forecast accuracy under heterogeneous modeling assumptions. In the future, a model-based forecasting approach, if supported with an airborne surveillance monitoring plan, could be made operational to provide agricultural stakeholders with reliable, cost-effective, and near-real-time information for protecting and sustaining crop production against multiple disease threats

    Crop Disease Detection Using Remote Sensing Image Analysis

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    Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

    Plant Biodiversity and Genetic Resources

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    The papers included in this Special Issue address a variety of important aspects of plant biodiversity and genetic resources, including definitions, descriptions, and illustrations of different components and their value for food and nutrition security, breeding, and environmental services. Furthermore, comprehensive information is provided regarding conservation approaches and techniques for plant genetic resources, policy aspects, and results of biological, genetic, morphological, economic, social, and breeding-related research activities. The complexity and vulnerability of (plant) biodiversity and its inherent genetic resources, as an integral part of the contextual ecosystem and the human web of life, are clearly demonstrated in this Special Issue, and for several encountered problems and constraints, possible approaches or solutions are presented to overcome these

    A genome-wide association study of resistance to the yellow rust pathogen (Puccinia striiformis f. sp. tritici) in elite UK wheat germplasm

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    ABSTRACT Pauline G. M. Bansept-Basler, 2013 A genome wide association study of resistance to the yellow rust pathogen (Puccinia striiformis f. sp. tritici) in elite UK wheat germplasm Identification of marker-trait associations (MTA) in germplasm relevant to breeding program via association mapping (AM) can be an effective way to identify loci useful for selection. This approach does not require the generation of specific mapping populations and takes advantage of historical phenotypic data. In the present study, an association panel of 327 bread wheat varieties have been assembled and genotyped with 1806 DArT markers. Genetic structure analysis revealed a low stratification of the panel based on geographical origin (UK versus mixed European varieties) and a close relatedness between lines, which is confirmed by pedigree information. Historical evaluations against the yellow rust pathogen (Puccinia striiformis f.sp. tritici (Pst)) carried in the United Kingdom between 1990 and 2009, as well as de novo evaluations against recent Pst races have been collected and analysed for MTAs. Association scans considering historical data focused on specific Pst pathotypes and de novo seedling tests identified markers linked to known racespecific Yr genes Yr6, Yr7, Yr9, Yr17 and Yr32. When evaluated against current Pst races in the field, 35% of the lines from the panel presented repeatedly a high level of resistance (Area Under the Disease Progress Curve relative<0.2) which is due to the presence of seedling resistances as well as adult plant resistances within the lines. AM with de novo phenotypes revealed 23 MTA groups pointing to potential resistance loci, 14 of them were also identified with historical data and six seemed to point to adult plant resistance loci on chromosomes 2A, 2B, 3A, 6A, 6B and 7A. These results confirm the value of AM using historical data for QTL discovery and suggest the availability of diverse sources of yellow rust resistances within wheat elite UK germplasm

    Genetic signatures of variation in population size in a native fungal pathogen after the recent intensive plantation of its host tree

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    Historical fluctuations in forests’ distribution driven by past climate changes and anthropogenic activities can have large impacts on the demographic history of pathogens that have a long co-evolution history with these host trees. Using a population genetic approach, we investigated that hypothesis by reconstructing the demographic history of Armillaria ostoyae, one of the major pathogens of the maritime pine (Pinus pinaster), in the largest monospecific pine planted forest in Europe (south-western France). Genetic structure analyses and approximate Bayesian computation approaches revealed that a single pathogen population underwent a severe reduction in effective size (12 times lower) 1080–2080 generations ago, followed by an expansion (4 times higher) during the last 4 generations. These results are consistent with the history of the maritime pine forest in the region characterized by a strong recession during the last glaciation (~19 000 years ago) and massive plantations during the second half of the nineteenth century. Results suggest that recent and intensive plantations of a host tree population have offered the opportunity for a rapid spread and adaptation of their pathogens

    Génomique des populations et adaptation des champignons pathogènes responsables de la maladie hollandaise de l'orme

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    La Maladie Hollandaise de l'Orme (MHO) est causée par des champignons du genre Ophiostoma. Ceux-ci sont responsables de la mort de plusieurs centaines de milliers d'ormes adultes en Europe ainsi qu'en Amérique du Nord, modifiant de manière drastiques les paysages forestiers et urbains. L'étude de la MHO a permis de caractériser deux espèces différentes, O. ulmi et O. novo-ulmi, qui présentent des phénotypes différents en terne de virulence et de croissance. L'analyse de données de séquençage à haut débit (génomique) associée à l'utilisation de données phénotypiques s'est répandue ces dernières décennies dans le domaine de la phytopathologie et permet de comprendre plus en détails la structure des populations ainsi que les gènes et mécanismes impliqués dans l'adaptation chez les champignons pathogènes. Dans le premier chapitre, nous comparons les caractéristiques évolutives des champignons phytopathogènes des cultures et des forêts. Nous contrastons l'impact des différents degrés de domestication et de gestion des milieux agricoles et forestiers sur ces populations de pathogènes. Les milieux agricoles et les forêts présentent des caractéristiques très différentes, comme le temps de génération ou le niveau de domestication. Cependant, nous trouvons que les mécanismes modelant les populations de pathogènes restent similaires, comme l'hybridation, les sauts d'hôtes, la sélection, la spécialisation et l'expansion clonale. Dans un second temps nous faisons un bilan des méthodes et techniques disponibles pour la gestion et l'amélioration des plantes de ces systèmes afin de prévenir ou lutter contre de futures épidémies. Dans le second chapitre, nous avons utilisé des données de génomiques pour examiner la structure génétique des populations des champignons responsables de la Maladie Hollandaise de l'Orme (MHO) Ophiostoma ulmi et Ophiostoma novo-ulmi. Nous quantifions et caractérisons la diversité génétique au sein des quatre lignées génétiques, ainsi que la divergence et la phylogénie entre chaque taxon. Nous décrivons le rôle de l'hybridation et de l'introgression dans l'histoire évolutive de ces pathogènes comme étant le mécanisme principal générant de la diversité génétique. La production de données phénotypiques nous permet également de caractériser l'impact de l'introgression sur l'adaptation de ces espèces. Dans le troisième chapitre, nous avons utilisé une approche « GWAS » (Genome Wide Analysis Study) pour révéler les marqueurs impliqués dans l'adaptation à la température et à un composé de défense de l'hôte chez O. ulmi et O. novo-ulmi. Nous trouvons d'importants gènes et familles de gènes associés avec les phénotypes de croissance et de virulence comme des transporteurs, des cytochromes, des protéines de choc thermique ou des protéines impliquées dans le système d'incompatibilité végétative qui pourraient jouer un rôle dans la protection contre les virus.Dutch Elm Disease (DED) is a highly destructive tree disease caused by fungi from the Ophiostoma genus. These fungi are responsible for the deaths of hundreds of thousands of mature elm trees both in Europe and in North America. Studies on DED allowed the characterization of two disctinct species, O. ulmi and O. novo-ulmi, that exhibit different virulence and growth phenotypes. Global pathogen genomics data including population genomics and high-quality reference assemblies are crucial for understanding the evolution and adaptation of pathogens. In a first chapter, we review crops and forest pathosystems with remarkably different characteristics, such as generation time and the level of domestication. They also have different management systems for disease control which is more intensive in crops than forest trees. By comparing and contrasting results from pathogen population genomic studies done on widely different agricultural and forest production systems, we can improve our understanding of pathogen evolution and adaptation to different selective pressures. We find that despite these differences, similar processes such as hybridization, host jumps, selection, specialization, and clonal expansion are shaping the pathogen populations in both crops and forest trees. We propose some solutions to reduce these impacts and to lower the probability of global pathogen outbreaks so that we can envision better management strategies to sustain global food production as well as ecosystem services. In a second chapter, we investigate how hybridization and the resulting introgression can drive the success of DED fungi via the rapid acquisition of adaptive traits. Using whole-genome sequences and growth phenotyping of a worldwide collection of isolates, we show that introgression has been the main driver of genomic diversity and that it impacted fitness-related traits. Introgressions contain genes involved in host-pathogen interactions and reproduction. Introgressed isolates have enhanced growth rate at high temperature and produce different necrosis sizes on an in vivo model for pathogenicity. In addition, lineages diverge in many pathogenicity-associated genes and exhibit differential mycelial growth in the presence of a proxy of a host defence compound, implying an important role of host trees in the molecular and functional differentiation of these pathogens. In the third chapter, we performed the identification of O. ulmi and O. novo-ulmi genes potentially associated with virulence and growth using Genome-Wide Association (GWA) analysis. We measured necrosis size induced on apples as a proxy for fungal virulence and measured growth rates at three different temperatures and two different media. We found several candidate genes for virulence, such as a CFEM domain containing protein and a HC-toxin efflux carrier. For growth, we identify several important gene families such as ABC and MFS transporters, cytochromes, transcription factors and proteins from the vegetative incompatibility complex

    Germin and germin-like proteins: evolution, structure, and function

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    Germin and germin-like proteins (GLPs) are encoded by a family of genes found in all plants. They are part of the cupin superfamily of biochemically diverse proteins, a superfamily that has a conserved tertiary structure, though with limited similarity in primary sequence. The subgroups of GLPs have different enzyme functions that include the two hydrogen peroxide-generating enzymes, oxalate oxidase (OxO) and superoxide dismutase. This review summarizes the sequence and structural details of GLPs and also discusses their evolutionary progression, particularly their amplification in gene number during the evolution of the land plants. In terms of function, the GLPs are known to be differentially expressed during specific periods of plant growth and development, a pattern of evolutionary subfunctionalization. They are also implicated in the response of plants to biotic (viruses, bacteria, mycorrhizae, fungi, insects, nematodes, and parasitic plants) and abiotic (salt, heat/cold, drought, nutrient, and metal) stress. Most detailed data come from studies of fungal pathogenesis in cereals. This involvement with the protection of plants from environmental stress of various types has led to numerous plant breeding studies that have found links between GLPs and QTLs for disease and stress resistance. In addition the OxO enzyme has considerable commercial significance, based principally on its use in the medical diagnosis of oxalate concentration in plasma and urine. Finally, this review provides information on the nutritional importance of these proteins in the human diet, as several members are known to be allergenic, a feature related to their thermal stability and evolutionary connection to the seed storage proteins, also members of the cupin superfamily
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