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Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras
PREMISE OF THE STUDY: Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study. METHODS: Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426157/ https://www.ncbi.nlm.nih.gov/pubmed/30937225 http://dx.doi.org/10.1002/aps3.1233 |
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author | Lorieul, Titouan Pearson, Katelin D. Ellwood, Elizabeth R. Goëau, Hervé Molino, Jean‐Francois Sweeney, Patrick W. Yost, Jennifer M. Sachs, Joel Mata‐Montero, Erick Nelson, Gil Soltis, Pamela S. Bonnet, Pierre Joly, Alexis |
author_facet | Lorieul, Titouan Pearson, Katelin D. Ellwood, Elizabeth R. Goëau, Hervé Molino, Jean‐Francois Sweeney, Patrick W. Yost, Jennifer M. Sachs, Joel Mata‐Montero, Erick Nelson, Gil Soltis, Pamela S. Bonnet, Pierre Joly, Alexis |
author_sort | Lorieul, Titouan |
collection | PubMed |
description | PREMISE OF THE STUDY: Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study. METHODS: Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time‐consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras. RESULTS: Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer‐scale information (84.3% for flower and 80.5% for fruit detection). DISCUSSION: The method described has the potential to allow fine‐grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed. |
format | Online Article Text |
id | pubmed-6426157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64261572019-04-01 Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras Lorieul, Titouan Pearson, Katelin D. Ellwood, Elizabeth R. Goëau, Hervé Molino, Jean‐Francois Sweeney, Patrick W. Yost, Jennifer M. Sachs, Joel Mata‐Montero, Erick Nelson, Gil Soltis, Pamela S. Bonnet, Pierre Joly, Alexis Appl Plant Sci Application Articles PREMISE OF THE STUDY: Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study. METHODS: Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time‐consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras. RESULTS: Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer‐scale information (84.3% for flower and 80.5% for fruit detection). DISCUSSION: The method described has the potential to allow fine‐grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed. John Wiley and Sons Inc. 2019-03-20 /pmc/articles/PMC6426157/ /pubmed/30937225 http://dx.doi.org/10.1002/aps3.1233 Text en © 2019 Lorieul et al. Applications in Plant Sciences is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Application Articles Lorieul, Titouan Pearson, Katelin D. Ellwood, Elizabeth R. Goëau, Hervé Molino, Jean‐Francois Sweeney, Patrick W. Yost, Jennifer M. Sachs, Joel Mata‐Montero, Erick Nelson, Gil Soltis, Pamela S. Bonnet, Pierre Joly, Alexis Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras |
title | Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras |
title_full | Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras |
title_fullStr | Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras |
title_full_unstemmed | Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras |
title_short | Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras |
title_sort | toward a large‐scale and deep phenological stage annotation of herbarium specimens: case studies from temperate, tropical, and equatorial floras |
topic | Application Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426157/ https://www.ncbi.nlm.nih.gov/pubmed/30937225 http://dx.doi.org/10.1002/aps3.1233 |
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