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Deep learning‐based methods for individual recognition in small birds
Open accessPublished version of a preprint
Abstract
Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well‐established, but often make data collection and analyses time‐consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large‐scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata , representing both wild and captive contexts. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual. Second, we describe how to train a CNN to uniquely re‐identify each individual in new images. Third, we illustrate the general applicability of CNNs for studies in animal biology by showing that trained CNNs can re‐identify individual birds in images collected in contexts that differ from the ones originally used to train the CNNs. Finally, we present a potential solution to solve the issues of new incoming individuals. Overall, our work demonstrates the feasibility of applying state‐of‐the‐art deep learning tools for individual identification of birds, both in the laboratory and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual recognition of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.
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“More recently, artificial intelligence deep learning models, such as Convolutional Neural Networks (CNN), have been developed to identify birds, bats or amphibians36–40.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [37]
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Zhao, Z., Gao, G., Zhang, Z., Huang, H., & Liu, H. (2024) · International Conference on Machine Learning and Machine Intelligence (MLMI) · doi:10.1145/3696271.3696317
“Ferreira et al. employed Convolutional Neural Networks (CNNs) for the recognition of three bird individuals [ 8 ] .”
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Community science as a potential tool to monitor animal demography and human-animal interactions
Maréchal, L., Lobo, R., McAdam, F., Simpson, E., Woolston, J., Stanley, M., Romero, T., & Dunn, J. C. (2025) · Scientific Reports · doi:10.1038/s41598-024-80171-1
“In other wildlife systems, where individuals may be identifiable using artificial intelligence 7,8,22 , the collection of a photo alongside additional project-specific data may be more effective at providing individual-level data than reading external markers, such as leg rings.”
A scalable two-stage model for real-time Wetland bird recognition
Xia, W., Zhou, Q., Wu, D.-Y., Wang, S., & Zhou, M. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07061-9
“Some wetland bird species are highly similar [18].”
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Prasad, C. V. H., & Arul, U. (2025) · AIP conference proceedings · doi:10.1063/5.0266130
Detection of Bird Species Found in Bhutan Using Vision Transformer-based Transfer Learning
Wangchuk, K., Wangchuk, T., & Dorji, T. (2025) · Zorig Melong. · doi:10.17102/zmv8.i1.008
Yang, A., Xue, Y., Han, N., Zheng, J., Zhang, L., & Luo, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-09146-0
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Prototypical Contrastive Network for One-Shot Animal Individual Identification
Zhang, Y., Ma, S., He, J., Li, Y., & Liu, H. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090778
“Ferreira et al. [6] successfully trained a convolutional neural network (CNN) model for individual bird identification by implanting PIT tags in bird individuals and capturing images using RFID technology.”
Enhanced Human Detection in Challenging Environments Using Visible and Infrared Thermal Imaging
Shehab, M. M. I., Ghosh, D. K., Hosscn, S., Jany, M. R., & Shufian, A. (2025) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp63728.2025.11144930
Multilevel-consistency of social behaviour in a cockroachPreprint
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“Additionally, new capabilities are being unlocked, such as marker-less individual recognition (Ferreira et al., 2020), the automatic detection of social interactions (Robie et al., 2017; Schofield et al., 2023), and image recognition in more complex environments such as in the wild is becoming more…”
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Do stable neural networks exist for classification problems? – A new view on stability in AI
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Social preferences, kinship and helping behaviour in a colonial cooperatively breeding bird
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Why we need more individual-based parasite research
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UV-Guided Match Verification for Animal Re-Identification
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Non-Invasive Individual Re-Identification of Water Monitors (Varanus salvator) Using Deep Learning
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Metadata
- Authors
- Ferreira, A. C., Silva, L. R., Renna, F., Brandl, H. B., Renoult, J. P., Farine, D. R., Covas, R., & Doutrelant, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Universidade do Porto (PT)
- University of Konstanz (DE)
- University of Cape Town (ZA)
- Corresponding author
- Ferreira, André C.
- ORCID
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2020-07-26
- Volume
- 11
- Issue
- 9
- Pages
- 1072-1085
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W3038699438
- Cited by
- 203
- References
- 47
- Citations by year
- 2026: 15 · 2025: 38 · 2024: 43 · 2023: 39 · 2022: 27 · 2021: 35 · 2020: 6
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Methods in Ecology and Evolution (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- HAL (submittedVersion, cc-by)
- KOPS (University of Konstanz) (submittedVersion)
- KOPS (University of Konstanz) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Max-Planck-Gesellschaft
- Fundação para a Ciência e a Tecnologia (CEECIND/01970/2017, IF/01411/2014/CP1256/CT0007, PTDC/BIA‐EVF/5249/201, SFRH/BD/122106/2016)
- Agence Nationale de la Recherche (ANR‐ 15‐CE32‐0012‐02, ANR 19‐CE02‐0014‐02)
- Deutsche Forschungsgemeinschaft (FA 1402/4‐1, 422037984)
- University of Cape Town
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- individual recognition, small birds, convolutional neural networks, sociable weaver, Parus major, zebra finch, re-identification
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