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Automated plant species identification—Trends and future directions
Author(s) -
Jana Wäldchen,
Michael Rzanny,
Marco Seeland,
Patrick Mäder
Publication year - 2018
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1005993
Subject(s) - identification (biology) , status quo , competence (human resources) , civil servants , data science , plant species , field (mathematics) , biodiversity , process (computing) , computer science , ecology , biology , political science , psychology , social psychology , mathematics , politics , pure mathematics , law , operating system
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.

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