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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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“These sources ensured diversity in species representation, environmental conditions, and image quality, which are crucial to building a high-performance detection model [31].”
Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings
Rathore, A., Sharma, A., Shah, S., Sharma, N., Torney, C. J., & Guttal, V. (2023) · PeerJ · doi:10.7717/peerj.15573 · Cited by 14
“Ferreira et al. (2020) proposed a CNN-based deep learning tracking tool for individual recognition of birds in semi-natural settings (birds kept in cages outdoors).”
Paulet, J., Molina, A., Beltzung, B., Suzumura, T., Yamamoto, S., & Sueur, C. (2024) · Primates · doi:10.1007/s10329-024-01137-5 · Cited by 13
“…(Hou et al. 2020), in Amur tigers, Panthera tigris altaica (Shi et al. 2020), in the small birds Philetairus socius , Parus major and Taeniopygia guttata (Ferreira et al. 2020), in giraffes, Giraffa camelopardalis (Miele et al. 2021), and in Asian elephants, Elephas maximus (de Silva et al. 2022).”
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
Hicks, D. M., Baude, M., Kratz, C. C., Ouvrard, P., & Stone, G. N. (2021) · Ecological Solutions and Evidence · doi:10.1002/2688-8319.12099 · Cited by 17
A Benchmark Database for Animal Re-Identification and Tracking
Kuncheva, L. I., Williams, F. E., Hennessey, S. L., & Rodríguez, J. J. (2022) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas55744.2022.10052988 · Cited by 13
“[21] Sociable weaver 17,500 50 https://besjournals.”
Yovel, G., Grosbard, I. D., & Abudarham, N. (2023) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2023.0093 · Cited by 14
Martin, K., Adam, O., Obin, N., & Dufour, V. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101818 · Cited by 12
Drivers of alloparental provisioning of fledglings in a colonially breeding bird
Ogino, M., Maldonado‐Chaparro, A. A., & Farine, D. R. (2020) · Behavioral Ecology · doi:10.1093/beheco/araa137 · Cited by 12
Self-citationLonati, M., Jahanbakht, M., Atkins, D., Bierwagen, S. L., Chin, A., Barnett, A., & Rummer, J. L. (2024) · Journal of Fish Biology · doi:10.1111/jfb.15887 · Cited by 14
“Importantly, epaulette sharks change patterns as they mature from their neonatal stage, allowing this study to assess the implications of unstable patterns during early ontogeny (Ferreira et al., 2020).”
Computerized assessment of dominance hierarchy in baboons (Papio papio)
Gullstrand, J., Claidière, N., & Fagot, J. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01539-z · Cited by 11
Face recognition of a Lorisidae species based on computer vision
Guan, Y., Lei, Y., Zhu, Y., Li, T., Xiang, Y., Dong, P., Jiang, R., Luo, J., Huang, A., YuMai, F., et al. (2023) · Global Ecology and Conservation · doi:10.1016/j.gecco.2023.e02511 · Cited by 18
Takaya, K., Taguchi, Y., & Ise, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-40814-1 · Cited by 10
Automatic individual recognition of Japanese macaques (Macaca fuscata) from sequential images
Ueno, M., Kabata, R., Hayashi, H., Terada, K., & Yamada, K. (2022) · Ethology · doi:10.1111/eth.13277 · Cited by 10
Using pose estimation to identify regions and points on natural history specimens
He, Y., Cooney, C. R., Maddock, S., & Thomas, G. H. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010933 · Cited by 10
Ramos‐Gonzalez, D., & Davison, A. (2021) · Ecology and Evolution · doi:10.1002/ece3.7443 · Cited by 10
“Similar methods have already been implemented to identify, count, and describe the behaviors of animals in a images from motion- sensor cameras in the Serengeti, and to recognize individual song- birds (Ferreira et al., 2020; Norouzzadeh et al., 2018).”
MethodAges of giant panda can be accurately predicted using facial images and machine learning
Zang, H.-X., Su, H., Yu, Q., Feng, L., Hou, R., He, M., Liu, P., Xu, P., Yu, Y., & Chen, P. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101892 · Cited by 11
Knowing a fellow by their bellow: acoustic individuality in the bellows of the American alligator
Jensen, T. R., Anikin, A., Osvath, M., & Reber, S. A. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.11.009 · Cited by 9
“…deep learning arti fi cial neural networks have been shown to be adept at identifying and differentiating between species and/or individuals (Ferreira et al., 2020; Martin et al., 2022; Parsons & Jones, 2000; Trapanotto et al., 2022), and they may prove especially useful for analysing large…”
BackgroundDeep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage
Wang, Z., Jiang, Y., Diallo, A. B., & Kembel, S. W. (2024) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14293 · Cited by 9
“Training GAN models requires a huge amount of training data of paired images that include damaged and intact leaves (Ferreira et al., 2020).”
BackgroundMethodAutomatic Bird Species Recognition using Audio and Image Data: A Short Review
Kumar, S., Kondaveeti, H. K., Simhadri, C. G., & Reddy, M. Y. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10262973 · Cited by 9
“[52] demonstrated individual bird recognition using deep learning without visual indicators.”
BackgroundSeimandi‐Corda, G., Hood, T., & Cook, S. M. (2024) · Agricultural and Forest Entomology · doi:10.1111/afe.12646 · Cited by 8
“Studies on mammals and birds can usually identify individuals at species level or even at the individual level (Ferreira et al., 2020; Schneider et al., 2019).”
Kern, D., Schiele, T., Klauck, U., & Ingabire, W. (2024) · Animals · doi:10.3390/ani15010001 · Cited by 9
A Transfer Learning Approach to Bird Species Recognition using MobileNetV2
Kondaveeti, H. K., Guturu, S. V. N. S. V., Praveen, K. S. J., & Kumar, S. V. S. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142795 · Cited by 8
“[9] showed recognition of birds in both field and lab settings through deep learning.”
BackgroundBanerjee, S., Gupta, R., Pandian, J. A., Saranya, S., & Mariappan, L. T. (2024) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee59897.2024.10467695 · Cited by 8
MEWC: A user-friendly AI workflow for customised wildlife-image classification
Brook, B. W., Buettel, J. C., van Lunteren, P., Rajmohan, P. P., & Aandahl, Z. (2025) · Peer Community Journal · doi:10.24072/pcjournal.565 · Cited by 12
An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset
Aminuddin, N. F., Tukiran, Z., Joret, A., Tomari, R., & Morsin, M. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130750 · Cited by 8
“In order to do so, manual data collection and labelling [6] are required to get enormous data, which is resource-intensive and time-consuming.”
BackgroundApplication of multi-species photo-ID database management systems: a user’s perspective
Chan, S. C. Y., Chui, S. Y. S., & Karczmarski, L. (2022) · Mammalian Biology · doi:10.1007/s42991-023-00345-8 · Cited by 8
Image-Based Bird Species Identification Using Machine Learning
Persia Abishal, B., & Juliet, S. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10113103 · Cited by 8
“[5] Deep Learning algorithms are used for individual recognition in small birds, which can help overcome the difficulties of collecting large volumes of data from varied contexts.”
Backgroundda Fonseca, E. M., Pope, N. S., Peterman, W. E., Werneck, F. P., Colli, G. R., & Carstens, B. C. (2024) · Heredity · doi:10.1038/s41437-024-00682-5 · Cited by 6
“…applied to several biological questions, ranging from detecting natural selection (Flagel et al. 2019; Torada et al. 2019) and reconstructing phylogenetic and phylogeographic history (Suvorov et al. 2020; Fonseca et al. 2021) to song annotation and individual recognition (Ferreira et al. 2020).”
BackgroundLi, C., He, Z., Lü, K., & Fang, C. (2025) · Sensors · doi:10.3390/s25010291 · Cited by 7
“Ferreira et al. [13] developed a method for identifying individual birds using data on captive birds.”
Evaluating the method reproducibility of deep learning models in biodiversity research
Ahmed, W., Kommineni, V. K., König‐Ries, B., Gaikwad, J., Gadelha, L. M. R., Jr., & Samuel, S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2618 · Cited by 7
Chimento, M., Chan, A. H. H., Aplin, L. M., & Kano, F. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70051 · Cited by 7
“…limitation involves the use of computer vision and machine learning tools (A. Mathis et al. 2020; Mackenzie Weygandt Mathis et al. 2020; Luxem et al. 2023), a development facilitated by a concomitant increase in the volume and resolution of image data (Griebling et al. 2022; Ehlman et al. 2023).”
Finding Nemo’s Giant Cousin: Keypoint Matching for Robust Re-Identification of Giant Sunfish
Pedersen, M., Nyegaard, M., & Moeslund, T. B. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11050889 · Cited by 7
A simple tool for linking photo-identification with multimedia data to track mammal behaviour
Machado, A. M. S., & Cantor, M. (2021) · Mammalian Biology · doi:10.1007/s42991-021-00189-0 · Cited by 7
“…as photographic, acoustic and video recording (Karczmarski et al. 2022a, b), rely on systematic comparison of natural marks or behaviours (e.g. Karanth and Nichols 1998; Muller et al. 2018; Longden et al. 2020) to track individuals from a distance (e.g. Clapham et al. 2020; Ferreira et al. 2020).”
BackgroundLü, Y., Li, J., Zhao, Z., Zhang, Y., Tong, Y., Teng, B., Liu, N., Shaw, J. J., & Bai, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3832815/v1 · Cited by 7
Similarity learning networks uniquely identify individuals of four marine and terrestrial species
Kabuga, E., Langley, I., Civil, M. A., Measey, J. G., Bah, B., & Durbach, I. N. (2024) · Ecosphere · doi:10.1002/ecs2.70012 · Cited by 6
BEHAVE - facilitating behaviour coding from videos with AI-detected animals
Elhorst, R., Syposz, M., & Wojczulanis‐Jakubas, K. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103106 · Cited by 6
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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