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dc.contributor.authorYounis, Sohaib
dc.contributor.authorWeiland, Claus
dc.contributor.authorHoehndorf, Robert
dc.contributor.authorDressler, Stefan
dc.contributor.authorHickler, Thomas
dc.contributor.authorSeeger, Bernhard
dc.contributor.authorSchmidt, Marco
dc.date.accessioned2018-04-15T07:13:36Z
dc.date.available2018-04-15T07:13:36Z
dc.date.issued2018-03-13
dc.identifier.citationYounis S, Weiland C, Hoehndorf R, Dressler S, Hickler T, et al. (2018) Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks. Botany Letters: 1–7. Available: http://dx.doi.org/10.1080/23818107.2018.1446357.
dc.identifier.issn2381-8107
dc.identifier.issn2381-8115
dc.identifier.doi10.1080/23818107.2018.1446357
dc.identifier.urihttp://hdl.handle.net/10754/627471
dc.description.abstractHerbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains suggests substantial potential of this approach for supporting taxonomy and natural history collection management. Trait recognition is also promising for applications in functional ecology.
dc.description.sponsorshipSY, MS and SD received funding from the DFG Project Mobilization of trait data from digital image files by deep learning approaches (grant 316452578). Parts of RH’s & CW’s work were funded by the National Bioscience Database Center (NBDC) and the Database Center for Life Science (DBCLS) Biohackathon 2017 grants. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the TITAN Xp GPU to CW used for this research.
dc.publisherInforma UK Limited
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/23818107.2018.1446357
dc.subjectHerbarium specimens
dc.subjectspecies recognition
dc.subjectconvolutional neural networks
dc.subjectmorphological traits
dc.subjecttrait recognition
dc.subjectdigitization
dc.titleTaxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBotany Letters
dc.contributor.institutionDepartment of Mathematics and Computer Science, University of Marburg, Marburg, Germany
dc.contributor.institutionData and Modelling Centre, Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
dc.contributor.institutionDepartment of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt, Frankfurt am Main, Germany
dc.contributor.institutionDepartment of Physical Geography, Goethe University, Frankfurt am Main, Germany
dc.contributor.institutionPalmengarten der Stadt Frankfurt am Main, Frankfurt am Main, Germany
dc.identifier.arxivid1803.07892
kaust.personHoehndorf, Robert
dc.date.published-online2018-03-13
dc.date.published-print2018-10-02


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