dc.contributor.author
Ott, Tankred
dc.contributor.author
Palm, Christoph
dc.contributor.author
Vogt, Robert
dc.contributor.author
Oberprieler, Christoph
dc.date.accessioned
2020-08-04T08:16:14Z
dc.date.available
2020-08-04T08:16:14Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/27987
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-27740
dc.description.abstract
Premise
The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor‐intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.
Methods and Results
We implemented an extendable pipeline based on state‐of‐the‐art deep‐learning object‐detection methods to collect leaf images from herbarium specimens of two species of the genus Leucanthemum . Using 183 specimens as the training data set, our pipeline extracted one or more intact leaves in 95% of the 61 test images.
Conclusions
We establish GinJinn as a deep‐learning object‐detection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous image‐processing approaches based on hand‐crafted features.
en
dc.format.extent
7 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
deep learning
en
dc.subject
herbarium specimens
en
dc.subject
object detection
en
dc.subject
visual recognition
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::580 Pflanzen (Botanik)::580 Pflanzen (Botanik)
dc.title
GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
e11351
dcterms.bibliographicCitation.doi
10.1002/aps3.11351
dcterms.bibliographicCitation.journaltitle
Applications in Plant Sciences
dcterms.bibliographicCitation.number
6
dcterms.bibliographicCitation.volume
8
dcterms.bibliographicCitation.url
https://doi.org/10.1002/aps3.11351
refubium.affiliation
Botanischer Garten und Botanisches Museum Berlin-Dahlem (BGBM)
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2168-0450
refubium.resourceType.provider
WoS-Alert