Machine learning for image based species identification

By Jana Wäldchen, Patrick Mäder. Methods in Ecology and Evolution, 2018

411 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ecological Modeling, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c9887b9e16380884f2ba6568669883884163551a

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Citations in the corpus

Perspectives in machine learning for wildlife conservation (2021, background): “remote) is to engage community members as wildlife data collectors and processors (92, 93).”
A Survey of Deep Learning-Based Object Detection (2019, background): “[221] J.”
Machine learning and deep learning—A review for ecologists (2022, methodology): “Several reviews have highlighted the potential of the recent advances in DL (Borowiec et al., 2022; Christin et al., 2019; Tuia et al., 2022 ; Wäldchen & Mäder, 2018 ), particularly for processing ecological data such as species recognition from video and audio analysis (Aodha et al., 2018 ; e.g.…”
Interpreting insect declines: seven challenges and a way forward (2020, background): “…the relative costs and benefits of different citizen science approaches (including the opportunities and risks of integrating artificial intelligence; Wäldchen & Mäder, 2018; Ceccaroni et al., 2019) in order to generate recommendations about which tools to adopt in insect population monitoring.”