GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens

URN to cite this document:: urn:nbn:de:bvb:355-epub-440677
DOI to cite this document:: 10.5283/epub.44067

Ott, Tankred

; Palm, Christoph

; Vogt, Robert ; Oberprieler, Christoph

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Date of publication of this fulltext: 12 Jan 2021 14:54

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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 ...