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Automated plant species identification—Trends and future directions
Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5886388/ https://www.ncbi.nlm.nih.gov/pubmed/29621236 http://dx.doi.org/10.1371/journal.pcbi.1005993 |
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author | Wäldchen, Jana Rzanny, Michael Seeland, Marco Mäder, Patrick |
author_facet | Wäldchen, Jana Rzanny, Michael Seeland, Marco Mäder, Patrick |
author_sort | Wäldchen, Jana |
collection | PubMed |
description | Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts. |
format | Online Article Text |
id | pubmed-5886388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58863882018-04-20 Automated plant species identification—Trends and future directions Wäldchen, Jana Rzanny, Michael Seeland, Marco Mäder, Patrick PLoS Comput Biol Review Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts. Public Library of Science 2018-04-05 /pmc/articles/PMC5886388/ /pubmed/29621236 http://dx.doi.org/10.1371/journal.pcbi.1005993 Text en © 2018 Wäldchen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Wäldchen, Jana Rzanny, Michael Seeland, Marco Mäder, Patrick Automated plant species identification—Trends and future directions |
title | Automated plant species identification—Trends and future directions |
title_full | Automated plant species identification—Trends and future directions |
title_fullStr | Automated plant species identification—Trends and future directions |
title_full_unstemmed | Automated plant species identification—Trends and future directions |
title_short | Automated plant species identification—Trends and future directions |
title_sort | automated plant species identification—trends and future directions |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5886388/ https://www.ncbi.nlm.nih.gov/pubmed/29621236 http://dx.doi.org/10.1371/journal.pcbi.1005993 |
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