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

  • 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.

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Author:Tankred Ott, Christoph PalmORCiDGND, Robert Vogt, Christoph OberprielerORCiD
Parent Title (English):Applications in Plant Sciences
Publisher:Wiley, Botanical Society of America
Document Type:Article
Year of first Publication:2020
Release Date:2020/07/08
Tag:Deep Learning; herbarium specimens; object detection; visual recognition
GND Keyword:Deep Learning; Objekterkennung; Maschinelles Sehen; Pflanzen
First Page:e11351
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Lebenswissenschaften und Ethik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International