Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

Sohaib Younis, Claus Weiland, Robert Hoehndorf, Stefan Dressler, Thomas Hickler, Bernhard Seeger, Marco Schmidt

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Herbaria 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.
Original languageEnglish (US)
Pages (from-to)377-383
Number of pages7
JournalBotany Letters
Volume165
Issue number3-4
DOIs
StatePublished - Mar 13 2018

Fingerprint

Dive into the research topics of 'Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks'. Together they form a unique fingerprint.

Cite this