A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions

We present a method for multispectral seed phenotyping as a fast and robust tool for managing genebank accessions. A multispectral vision system was used to take images of the seeds of 20 diverse varieties of rice (approximately 30 seeds for each variety). This was followed by extraction of feature...

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Autores principales: Hansen, Michael Adsetts Edberg, Hay, Fiona R., Carstensen, Jens Michael
Formato: Journal Article
Lenguaje:Inglés
Publicado: Cambridge University Press 2016
Acceso en línea:https://hdl.handle.net/10568/165399
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author Hansen, Michael Adsetts Edberg
Hay, Fiona R.
Carstensen, Jens Michael
author_browse Carstensen, Jens Michael
Hansen, Michael Adsetts Edberg
Hay, Fiona R.
author_facet Hansen, Michael Adsetts Edberg
Hay, Fiona R.
Carstensen, Jens Michael
author_sort Hansen, Michael Adsetts Edberg
collection Repository of Agricultural Research Outputs (CGSpace)
description We present a method for multispectral seed phenotyping as a fast and robust tool for managing genebank accessions. A multispectral vision system was used to take images of the seeds of 20 diverse varieties of rice (approximately 30 seeds for each variety). This was followed by extraction of feature information from the images. Multivariate analysis of the feature data was used to classify seed phenotypes according to accession. The proportion of correctly classified rice seeds was 93%. We conclude that the multispectral image analysis could play a role in comparing incoming seeds against existing accessions, identifying different seed types within a sample of seeds and/or in checking whether regenerated seeds match the original seeds.
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institution CGIAR Consortium
language Inglés
publishDate 2016
publishDateRange 2016
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spelling CGSpace1653992024-12-19T14:13:06Z A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions Hansen, Michael Adsetts Edberg Hay, Fiona R. Carstensen, Jens Michael We present a method for multispectral seed phenotyping as a fast and robust tool for managing genebank accessions. A multispectral vision system was used to take images of the seeds of 20 diverse varieties of rice (approximately 30 seeds for each variety). This was followed by extraction of feature information from the images. Multivariate analysis of the feature data was used to classify seed phenotypes according to accession. The proportion of correctly classified rice seeds was 93%. We conclude that the multispectral image analysis could play a role in comparing incoming seeds against existing accessions, identifying different seed types within a sample of seeds and/or in checking whether regenerated seeds match the original seeds. 2016-09 2024-12-19T12:55:02Z 2024-12-19T12:55:02Z Journal Article https://hdl.handle.net/10568/165399 en Cambridge University Press Hansen, Michael Adsetts Edberg; Hay, Fiona R. and Carstensen, Jens Michael. 2016. A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions. Plant Genet. Resour., Volume 14 no. 3 p. 238-241
spellingShingle Hansen, Michael Adsetts Edberg
Hay, Fiona R.
Carstensen, Jens Michael
A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions
title A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions
title_full A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions
title_fullStr A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions
title_full_unstemmed A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions
title_short A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions
title_sort virtual seed file the use of multispectral image analysis in the management of genebank seed accessions
url https://hdl.handle.net/10568/165399
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