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...

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Bibliographic Details
Published in:PLoS computational biology 2018-04, Vol.14 (4), p.e1005993-e1005993
Main Authors: Wäldchen, Jana, Rzanny, Michael, Seeland, Marco, Mäder, Patrick
Format: Article
Language:English
Subjects:
Artificial Intelligence
Automation
Biodiversity
Biogeochemistry
Biology and Life Sciences
Cameras
Computational Biology
Computer and Information Sciences
Computer vision
Conservation of Natural Resources
Digital cameras
Ecology
Electronic devices
Encyclopedias
Environmental aspects
Evolutionary biology
Flowers & plants
Flowers - anatomy & histology
Flowers - classification
Identification
Image processing
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - trends
Land management
Multimedia
Neural networks
Object recognition
Pattern recognition
Pattern Recognition, Automated - methods
Pattern Recognition, Automated - trends
Pigmentation
Plant Leaves - anatomy & histology
Plant Leaves - classification
Plant populations
Plant species
Plants - anatomy & histology
Plants - classification
Review
Software engineering
Species
Supervised Machine Learning
Taxonomy
Trends
Wildlife conservation
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1005993