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The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples
BACKGROUND: Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools wer...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7149850/ https://www.ncbi.nlm.nih.gov/pubmed/32308727 http://dx.doi.org/10.1186/s13007-020-00591-8 |
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author | Halcro, Keith McNabb, Kaitlin Lockinger, Ashley Socquet-Juglard, Didier Bett, Kirstin E. Noble, Scott D. |
author_facet | Halcro, Keith McNabb, Kaitlin Lockinger, Ashley Socquet-Juglard, Didier Bett, Kirstin E. Noble, Scott D. |
author_sort | Halcro, Keith |
collection | PubMed |
description | BACKGROUND: Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. RESULTS: A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil (Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. CONCLUSIONS: Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use. |
format | Online Article Text |
id | pubmed-7149850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71498502020-04-19 The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples Halcro, Keith McNabb, Kaitlin Lockinger, Ashley Socquet-Juglard, Didier Bett, Kirstin E. Noble, Scott D. Plant Methods Methodology BACKGROUND: Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. RESULTS: A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil (Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. CONCLUSIONS: Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use. BioMed Central 2020-04-10 /pmc/articles/PMC7149850/ /pubmed/32308727 http://dx.doi.org/10.1186/s13007-020-00591-8 Text en © The Author(s) 2020 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/. 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 in a credit line to the data. |
spellingShingle | Methodology Halcro, Keith McNabb, Kaitlin Lockinger, Ashley Socquet-Juglard, Didier Bett, Kirstin E. Noble, Scott D. The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples |
title | The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples |
title_full | The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples |
title_fullStr | The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples |
title_full_unstemmed | The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples |
title_short | The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples |
title_sort | belt and phenoseed platforms: shape and colour phenotyping of seed samples |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7149850/ https://www.ncbi.nlm.nih.gov/pubmed/32308727 http://dx.doi.org/10.1186/s13007-020-00591-8 |
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