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Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases

Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected....

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Autores principales: Mahlein, Anne-Katrin, Steiner, Ulrike, Hillnhütter, Christian, Dehne, Heinz-Wilhelm, Oerke, Erich-Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3274483/
https://www.ncbi.nlm.nih.gov/pubmed/22273513
http://dx.doi.org/10.1186/1746-4811-8-3
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author Mahlein, Anne-Katrin
Steiner, Ulrike
Hillnhütter, Christian
Dehne, Heinz-Wilhelm
Oerke, Erich-Christian
author_facet Mahlein, Anne-Katrin
Steiner, Ulrike
Hillnhütter, Christian
Dehne, Heinz-Wilhelm
Oerke, Erich-Christian
author_sort Mahlein, Anne-Katrin
collection PubMed
description Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected. Light microscopy was used to describe the morphological changes in the host tissue due to pathogen colonisation. Under controlled conditions a hyperspectral imaging line scanning spectrometer (ImSpector V10E) with a spectral resolution of 2.8 nm from 400 to 1000 nm and a spatial resolution of 0.19 mm was used for continuous screening and monitoring of disease symptoms during pathogenesis. A pixel-wise mapping of spectral reflectance in the visible and near-infrared range enabled the detection and detailed description of diseased tissue on the leaf level. Leaf structure was linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, the pathogens caused disease-specific spectral signatures. The influence of the pathogens on leaf reflectance was a function of the developmental stage of the disease and of the subarea of the symptoms. Spectral reflectance in combination with Spectral Angle Mapper classification allowed for the differentiation of mature symptoms into zones displaying all ontogenetic stages from young to mature symptoms. Due to a pixel-wise extraction of pure spectral signatures a better understanding of changes in leaf reflectance caused by plant diseases was achieved using HSI. This technology considerably improves the sensitivity and specificity of hyperspectrometry in proximal sensing of plant diseases.
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spelling pubmed-32744832012-02-08 Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases Mahlein, Anne-Katrin Steiner, Ulrike Hillnhütter, Christian Dehne, Heinz-Wilhelm Oerke, Erich-Christian Plant Methods Methodology Hyperspectral imaging (HSI) offers high potential as a non-invasive diagnostic tool for disease detection. In this paper leaf characteristics and spectral reflectance of sugar beet leaves diseased with Cercospora leaf spot, powdery mildew and leaf rust at different development stages were connected. Light microscopy was used to describe the morphological changes in the host tissue due to pathogen colonisation. Under controlled conditions a hyperspectral imaging line scanning spectrometer (ImSpector V10E) with a spectral resolution of 2.8 nm from 400 to 1000 nm and a spatial resolution of 0.19 mm was used for continuous screening and monitoring of disease symptoms during pathogenesis. A pixel-wise mapping of spectral reflectance in the visible and near-infrared range enabled the detection and detailed description of diseased tissue on the leaf level. Leaf structure was linked to leaf spectral reflectance patterns. Depending on the interaction with the host tissue, the pathogens caused disease-specific spectral signatures. The influence of the pathogens on leaf reflectance was a function of the developmental stage of the disease and of the subarea of the symptoms. Spectral reflectance in combination with Spectral Angle Mapper classification allowed for the differentiation of mature symptoms into zones displaying all ontogenetic stages from young to mature symptoms. Due to a pixel-wise extraction of pure spectral signatures a better understanding of changes in leaf reflectance caused by plant diseases was achieved using HSI. This technology considerably improves the sensitivity and specificity of hyperspectrometry in proximal sensing of plant diseases. BioMed Central 2012-01-24 /pmc/articles/PMC3274483/ /pubmed/22273513 http://dx.doi.org/10.1186/1746-4811-8-3 Text en Copyright ©2012 Mahlein et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Mahlein, Anne-Katrin
Steiner, Ulrike
Hillnhütter, Christian
Dehne, Heinz-Wilhelm
Oerke, Erich-Christian
Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
title Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
title_full Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
title_fullStr Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
title_full_unstemmed Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
title_short Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
title_sort hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3274483/
https://www.ncbi.nlm.nih.gov/pubmed/22273513
http://dx.doi.org/10.1186/1746-4811-8-3
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