Work

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

Year: 2020

Type: article

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: . 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.

Source: Applications in Plant Sciences

DOI: 10.1002/aps3.11351

Authors: Tankred Ott, Christoph Palm, Robert Vogt, Christoph Oberprieler

Institutions: University of Regensburg, OTH Regensburg, Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,319

Funder: Deutsche Forschungsgemeinschaft