Flora Capture: a citizen science application for collecting structured plant observations
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Flora Capture: a citizen science application for collecting structured plant observations David Boho1, Michael Rzanny2, Jana Wäldchen2*, Fabian Nitsche1, Alice Deggelmann2, Hans Christian Wittich1, Marco Seeland1 and Patrick Mäder1 *Correspondence: jwald@bgc‑jena.mpg.de 2 Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans‑Knöll‑Str. 10, 07745 Jena, Germany Full list of author information is available at the end of the article
Abstract Background: Digital plant images are becoming increasingly important. First, given a large number of images deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about plant morphological characteristics. Finally in the course of digitalization, digital plant collections receive more and more interest in schools and universities. Results: We developed a freely available mobile application called Flora Capture allowing users to collect series of plant images from predefined perspectives. These images, together with accompanying metadata, are transferred to a central project server where each observation is reviewed and validated by a team of botanical experts. Currently, more than 4800 plant species, naturally occurring in the Central European region, are covered by the application. More than 200,000 images, depicting more than 1700 plant species, have been collected by thousands of users since the initial app release in 2016. Conclusion: Flora Capture allows experts, laymen and citizen scientists to collect a digital herbarium and share structured multi-modal observations of plants. Collected images contribute, e.g., to the training of plant identification algorithms, but also suit educational purposes. Additionally, presence records collected with each observation allow contribute to verifiable records of plant occurrences across the world. Keywords: Structured plant observations, Multi-organ plant identification, Mobile app, Citizen science, Digital plant collection, Digital herbariumn
Background Efforts to automatically identify species from images have substantially increased in recent years [1, 2]. Deep learning methods revolutionize our ability to train computers in identifying organisms from image data, such as insects [3], fishes [4], plankton [5], mammals [6] and plants [7]. Specifically, convolutional neural networks (CNNs) allow for superior recognition performance [8, 9] and form the basis for successful automated plant species identification [1, 10]. Deep CNNs have been demonstrated to facilitate classification accuracies that are on par with human performance for general object recognition tasks [8] as well as for fine-grained species identification tasks [11]. © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the orig
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