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      Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management


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          Digitisation of natural history collections has evolved from creating databases for the recording of specimens’ catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.

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          Applications of deep convolutional neural networks to digitized natural history collections

          Abstract Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools.
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            Automatic extraction of leaf characters from herbarium specimens

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              Leveraging the fullest potential of scientific collections through digitisation.


                Author and article information

                Biodivers Data J
                Biodivers Data J
                Biodiversity Data Journal
                Pensoft Publishers
                26 March 2020
                : 8
                [1 ] School of Computer Science and Informatics - Cardiff University, Cardiff, United Kingdom School of Computer Science and Informatics - Cardiff University Cardiff United Kingdom
                [2 ] Meise Botanic Garden, Meise, Belgium Meise Botanic Garden Meise Belgium
                Author notes
                Corresponding author: Abraham Nieva de la Hidalga ( nievadelahidalgaa@ 123456cardiff.ac.uk ).

                Academic editor: James Macklin

                47051 12459
                Abraham Nieva de la Hidalga, Paul L Rosin, Xianfang Sun, Ann Bogaerts, Niko De Meeter, Sofie De Smedt, Maarten Strack van Schijndel, Paul Van Wambeke, Quentin Groom

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 8, Tables: 13, References: 44
                Funded by: Vlaamse Overheid 501100002913 http://doi.org/10.13039/501100002913


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