ArticlePloS one2018
Ant genera identification using an ensemble of convolutional neural networks.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae).Cladistics : the international journal of the Willi Hennig Society · 2026Article
- Progress in developing a bark beetle identification tool.PloS one · 2025Article
- Comparing ant morphology measurements from microscope and online AntWeb.org 2D z-stacked images.Ecology and evolution · 2023Article
- From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring.Frontiers in plant science · 2023Review
- Assessing the potential for deep learning and computer vision to identify bumble bee species from images.Scientific reports · 2021Article
- Article
- Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale.Ecology and evolution · 2020Article
- Species-level image classification with convolutional neural network enables insect identification from habitus images.Ecology and evolution · 2020Article
- Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks.PloS one · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Works requiring taxonomic knowledge face several challenges, such as arduous identification of many taxa and an insufficient number of taxonomists to identify a great deal of collected organisms. Machine learning tools, particularly convolutional neural networks (CNNs), are then welcome to automatically generate high-performance classifiers from available data. Supported by the image datasets available at the largest online database on ant biology, the AntWeb (www.antweb.org), we propose here an ensemble of CNNs to identify ant genera directly from the head, profile and dorsal perspectives of ant images. Transfer learning is also considered to improve the individual performance of the CNN classifiers. The performance achieved by the classifiers is diverse enough to promote a reduction in the overall classification error when they are combined in an ensemble, achieving an accuracy rate of over 80% on top-1 classification and an accuracy of over 90% on top-3 classification.
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