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Deep Learning in palynology

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dc.contributor.authorViertel, Philipp
dc.contributor.authorKönig, Matthias
dc.contributor.editorMeyer-Aurich, Andreas
dc.contributor.editorGandorfer, Markus
dc.contributor.editorHoffmann, Christa
dc.contributor.editorWeltzien, Cornelia
dc.contributor.editorBellingrath-Kimura, Sonoko
dc.contributor.editorFloto, Helga
dc.date.accessioned2021-03-02T14:37:32Z
dc.date.available2021-03-02T14:37:32Z
dc.date.issued2021
dc.description.abstractIn this work, we will show a use case for visual pollen classification from honey samples. We discuss the current state of the art in pollen analysis, highlight the importance of data quantity and quality, and elaborate on how to transfer promising Deep Learning methods to the analysis of honey samples. A first experiment with a public data set is shown as well as samples from our work-in-progress data set. Our recommendations and methods show which steps are necessary in order to successfully deploy an automated pollen analysis solution for honey products.en
dc.identifier.isbn978-3-88579-703-6
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/35697
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof41. GIL-Jahrestagung, Informations- und Kommunikationstechnologie in kritischen Zeiten
dc.relation.ispartofseriesLecture Notes in Informatics
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectPalynology
dc.subjectPollen analysis
dc.subjectAutomation
dc.titleDeep Learning in palynologyen
dc.typeText/Conference Paper
gi.citation.endPage336
gi.citation.publisherPlaceBonn
gi.citation.startPage331
gi.conference.date08.-09. März 2021
gi.conference.locationPotsdam, Online
gi.conference.sessiontitleGIL-Jahrestagung - Fokus: Informations- und Kommunikationstechnologien in kritischen Zeiten

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P309 - 41. GIL-Jahrestagung 2021 - Fokus: Informations- und Kommunikationstechnologie in kritischen Zeiten