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“Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification
Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress i...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706317/ https://www.ncbi.nlm.nih.gov/pubmed/33313559 http://dx.doi.org/10.34133/2020/5839856 |
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author | Lück, Stefanie Strickert, Marc Lorbeer, Maximilian Melchert, Friedrich Backhaus, Andreas Kilias, David Seiffert, Udo Douchkov, Dimitar |
author_facet | Lück, Stefanie Strickert, Marc Lorbeer, Maximilian Melchert, Friedrich Backhaus, Andreas Kilias, David Seiffert, Udo Douchkov, Dimitar |
author_sort | Lück, Stefanie |
collection | PubMed |
description | Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a “Macrophenomics facility.” The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations. |
format | Online Article Text |
id | pubmed-7706317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-77063172020-12-10 “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification Lück, Stefanie Strickert, Marc Lorbeer, Maximilian Melchert, Friedrich Backhaus, Andreas Kilias, David Seiffert, Udo Douchkov, Dimitar Plant Phenomics Research Article Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a “Macrophenomics facility.” The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations. AAAS 2020-11-05 /pmc/articles/PMC7706317/ /pubmed/33313559 http://dx.doi.org/10.34133/2020/5839856 Text en Copyright © 2020 Stefanie Lück et al. https://creativecommons.org/licenses/by/4.0/ Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0). |
spellingShingle | Research Article Lück, Stefanie Strickert, Marc Lorbeer, Maximilian Melchert, Friedrich Backhaus, Andreas Kilias, David Seiffert, Udo Douchkov, Dimitar “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification |
title | “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification |
title_full | “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification |
title_fullStr | “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification |
title_full_unstemmed | “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification |
title_short | “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification |
title_sort | “macrobot”: an automated segmentation-based system for powdery mildew disease quantification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706317/ https://www.ncbi.nlm.nih.gov/pubmed/33313559 http://dx.doi.org/10.34133/2020/5839856 |
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