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Machine learning for image based species identification
Open accessAbstract
Accurate species identification is the basis for all aspects of taxonomic research and is an essential component of workflows in biological research. Biologists are asking for more efficient methods to meet the identification demand. Smart mobile devices, digital cameras as well as the mass digitisation of natural history collections led to an explosion of openly available image data depicting living organisms. This rapid increase in biological image data in combination with modern machine learning methods, such as deep learning, offers tremendous opportunities for automated species identification. In this paper, we focus on deep learning neural networks as a technology that enabled breakthroughs in automated species identification in the last 2 years. In order to stimulate more work in this direction, we provide a brief overview of machine learning frameworks applicable to the species identification problem. We review selected deep learning approaches for image based species identification and introduce publicly available applications. Eventually, this article aims to provide insights into the current state‐of‐the‐art in automated identification and to serve as a starting point for researchers willing to apply novel machine learning techniques in their biological studies. While modern machine learning approaches only slowly pave their way into the field of species identification, we argue that we are going to see a proliferation of these techniques being applied to the problem in the future. Artificial intelligence systems will provide alternative tools for taxonomic identification in the near future.
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MERRC: A Memristor-Enabled Reconfigurable Low-Power Reservoir Computing Architecture at the Edge
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Metadata
- Authors
- Wäldchen, J., & Mäder, P.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2018-09-06
- Volume
- 9
- Issue
- 11
- Pages
- 2216-2225
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2885150722
- Cited by
- 504
- References
- 10
- FWCI
- 17.57
- Type
- journal article
- Language
- en
- Open access
- bronze
- Funders
- Bundesministerium für Bildung und Forschung (01LC1319A and 01LC1319B)
- Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (3514 685C19)
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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