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      Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease

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          Abstract

          Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.

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          Most cited references40

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space

            Bo-Cai Gao (1996)
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              Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.

              Leaf chlorophyll content provides valuable information about physiological status of plants. Reflectance measurement makes it possible to quickly and non-destructively assess, in situ, the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in leaves with a wide range of pigment content and composition using reflectance in a few broad spectral bands. Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated. It was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda from 520 to 550 nm and 695 to 705 nm related closely to the total chlorophyll content in leaves of all species. Subtraction of near infra-red reciprocal reflectance, (RNIR)-1, from (R lambda)-1 made index [(R lambda)(-1)-(RNIR)-1] linearly proportional to the total chlorophyll content in spectral ranges lambda from 525 to 555 nm and from 695 to 725 nm with coefficient of determination r2 > 0.94. To adjust for differences in leaf structure, the product of the latter index and NIR reflectance [(R lambda)(-1)-(RNIR)-1]*(RNIR) was used; this further increased the accuracy of the chlorophyll estimation in the range lambda from 520 to 585 nm and from 695 to 740 nm. Two independent data sets were used to validate the developed algorithms. The root mean square error of the chlorophyll prediction did not exceed 50 mumol/m2 in leaves with total chlorophyll ranged from 1 to 830 mumol/m2.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                17 August 2018
                2018
                : 9
                : 1195
                Affiliations
                [1] 1Crop Science Group, Institute of Agricultural Sciences , ETH Zurich, Zurich, Switzerland
                [2] 2Plant Pathology Group, Institute of Integrative Biology , ETH Zurich, Zurich, Switzerland
                [3] 3Plant Breeding and Genetic Resources, Strategic Research Division Plant Breeding , Agroscope, Nyon, Switzerland
                Author notes

                Edited by: Chunjiang Zhao, Beijing Academy of Agricultural and Forestry Sciences, China

                Reviewed by: Sébastien Saint-Jean, AgroParisTech Institut des Sciences et Industries du Vivant et de L'environnement, France; Conxita Royo, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Spain

                *Correspondence: Kang Yu kang.yu@ 123456alumni.ethz.ch

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                †Present Address: Kang Yu, KU Leuven, Remote Sensing & Terrestrial Ecology, Department of Earth and Environmental Sciences, Leuven, Belgium

                Article
                10.3389/fpls.2018.01195
                6108383
                30174678
                a0cdcfdd-ea2c-4c19-94df-c18adb7b804f
                Copyright © 2018 Yu, Anderegg, Mikaberidze, Karisto, Mascher, McDonald, Walter and Hund.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 October 2017
                : 26 July 2018
                Page count
                Figures: 9, Tables: 5, Equations: 3, References: 58, Pages: 17, Words: 10209
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                field-based phenotyping,hyperspectral remote sensing,partial least squares discriminant analysis,plant phenotyping,plant spectroscopy,resistance breeding,septoria tritici blotch,wheat disease

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