GinJinn: An object‐detection pipeline for automated feature extraction from herbarium specimens. (26th June 2020)

• Record Type: Journal Article
• Authors: Ott, Tankred; Palm, Christoph; Vogt, Robert; Oberprieler, Christoph
• Is Part Of: Applications in plant sciences. Volume 8:Number 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-26
• Subjects: deep learning -- herbarium specimens -- object detection -- TensorFlow -- visual recognition; Plants -- Periodicals; Plant physiology -- Periodicals; Plant Physiological Phenomena; Plant physiology; Plants; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 580
• DOI: 10.1002/aps3.11351
• Languages: English
• ISSNs: 2168-0450

Abstract

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.