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Sensor-based phenotyping of above-ground plant-pathogen interactions
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant pheno...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8935837/ https://www.ncbi.nlm.nih.gov/pubmed/35313920 http://dx.doi.org/10.1186/s13007-022-00853-7 |
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author | Tanner, Florian Tonn, Sebastian de Wit, Jos Van den Ackerveken, Guido Berger, Bettina Plett, Darren |
author_facet | Tanner, Florian Tonn, Sebastian de Wit, Jos Van den Ackerveken, Guido Berger, Bettina Plett, Darren |
author_sort | Tanner, Florian |
collection | PubMed |
description | Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease. |
format | Online Article Text |
id | pubmed-8935837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89358372022-03-23 Sensor-based phenotyping of above-ground plant-pathogen interactions Tanner, Florian Tonn, Sebastian de Wit, Jos Van den Ackerveken, Guido Berger, Bettina Plett, Darren Plant Methods Review Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease. BioMed Central 2022-03-21 /pmc/articles/PMC8935837/ /pubmed/35313920 http://dx.doi.org/10.1186/s13007-022-00853-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Tanner, Florian Tonn, Sebastian de Wit, Jos Van den Ackerveken, Guido Berger, Bettina Plett, Darren Sensor-based phenotyping of above-ground plant-pathogen interactions |
title | Sensor-based phenotyping of above-ground plant-pathogen interactions |
title_full | Sensor-based phenotyping of above-ground plant-pathogen interactions |
title_fullStr | Sensor-based phenotyping of above-ground plant-pathogen interactions |
title_full_unstemmed | Sensor-based phenotyping of above-ground plant-pathogen interactions |
title_short | Sensor-based phenotyping of above-ground plant-pathogen interactions |
title_sort | sensor-based phenotyping of above-ground plant-pathogen interactions |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8935837/ https://www.ncbi.nlm.nih.gov/pubmed/35313920 http://dx.doi.org/10.1186/s13007-022-00853-7 |
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