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Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum)
PREMISE: Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328651/ https://www.ncbi.nlm.nih.gov/pubmed/32626613 http://dx.doi.org/10.1002/aps3.11372 |
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author | Pryer, Kathleen M. Tomasi, Carlo Wang, Xiaohan Meineke, Emily K. Windham, Michael D. |
author_facet | Pryer, Kathleen M. Tomasi, Carlo Wang, Xiaohan Meineke, Emily K. Windham, Michael D. |
author_sort | Pryer, Kathleen M. |
collection | PubMed |
description | PREMISE: Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools. METHODS: We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images. RESULTS: In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy. DISCUSSION: Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available. |
format | Online Article Text |
id | pubmed-7328651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73286512020-07-02 Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) Pryer, Kathleen M. Tomasi, Carlo Wang, Xiaohan Meineke, Emily K. Windham, Michael D. Appl Plant Sci Application Articles PREMISE: Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools. METHODS: We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images. RESULTS: In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy. DISCUSSION: Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available. John Wiley and Sons Inc. 2020-07-01 /pmc/articles/PMC7328651/ /pubmed/32626613 http://dx.doi.org/10.1002/aps3.11372 Text en © 2020 Pryer et al. Applications in Plant Sciences is published by Wiley Periodicals LLC 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 Pryer, Kathleen M. Tomasi, Carlo Wang, Xiaohan Meineke, Emily K. Windham, Michael D. Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) |
title | Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) |
title_full | Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) |
title_fullStr | Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) |
title_full_unstemmed | Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) |
title_short | Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum) |
title_sort | using computer vision on herbarium specimen images to discriminate among closely related horsetails (equisetum) |
topic | Application Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328651/ https://www.ncbi.nlm.nih.gov/pubmed/32626613 http://dx.doi.org/10.1002/aps3.11372 |
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