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Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape

BACKGROUND: The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale me...

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Autores principales: Veys, Charles, Chatziavgerinos, Fokion, AlSuwaidi, Ali, Hibbert, James, Hansen, Mark, Bernotas, Gytis, Smith, Melvyn, Yin, Hujun, Rolfe, Stephen, Grieve, Bruce
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345015/
https://www.ncbi.nlm.nih.gov/pubmed/30697329
http://dx.doi.org/10.1186/s13007-019-0389-9
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author Veys, Charles
Chatziavgerinos, Fokion
AlSuwaidi, Ali
Hibbert, James
Hansen, Mark
Bernotas, Gytis
Smith, Melvyn
Yin, Hujun
Rolfe, Stephen
Grieve, Bruce
author_facet Veys, Charles
Chatziavgerinos, Fokion
AlSuwaidi, Ali
Hibbert, James
Hansen, Mark
Bernotas, Gytis
Smith, Melvyn
Yin, Hujun
Rolfe, Stephen
Grieve, Bruce
author_sort Veys, Charles
collection PubMed
description BACKGROUND: The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance. RESULTS: The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation. CONCLUSIONS: The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.
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spelling pubmed-63450152019-01-29 Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape Veys, Charles Chatziavgerinos, Fokion AlSuwaidi, Ali Hibbert, James Hansen, Mark Bernotas, Gytis Smith, Melvyn Yin, Hujun Rolfe, Stephen Grieve, Bruce Plant Methods Methodology BACKGROUND: The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance. RESULTS: The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation. CONCLUSIONS: The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability. BioMed Central 2019-01-24 /pmc/articles/PMC6345015/ /pubmed/30697329 http://dx.doi.org/10.1186/s13007-019-0389-9 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Veys, Charles
Chatziavgerinos, Fokion
AlSuwaidi, Ali
Hibbert, James
Hansen, Mark
Bernotas, Gytis
Smith, Melvyn
Yin, Hujun
Rolfe, Stephen
Grieve, Bruce
Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
title Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
title_full Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
title_fullStr Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
title_full_unstemmed Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
title_short Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
title_sort multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345015/
https://www.ncbi.nlm.nih.gov/pubmed/30697329
http://dx.doi.org/10.1186/s13007-019-0389-9
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