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Lookup NU author(s): Dr Jack O'Sullivan, Dr Helen GrayORCiD, Genevieve Moat, Professor Lucy AsherORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Authors. Abstract – Piling is an understudied group behaviour where chickens gather in dense clusters. It appears to be widespread in commercial laying hens, leads to mortalities (smothers) and impacts production. Automated detection of piling could support: (i) stockpersons to reduce the behaviour and (ii) scientific efforts to study the behaviour. Computer vision-based classification is increasingly used in animal behaviour research to accelerate annotation of datasets. Here we aimed to automatically detect piling using the well-established residual network (ResNet) architecture convolutional neural network (CNN) to classify video frames of piling and non-piling chickens in commercial free-range laying farms. Data from 10 flocks (8 for training, 2 for testing) were used. The model achieved high classification accuracy, correctly identifying 89.6% of individual frames and 90.9% of aggregated events, though misclassifications at both levels were driven primarily by false negatives (16.2% for frames and 16.8% for events). Cases of misclassification could arise from not accounting for the temporal aspect of piling, human annotation error, image quality, flock-specific characteristics or ambiguous cases. ResNet demonstrated good performance for classifying piling from non-piling from still frames in brown laying hens in commercial houses but would need further training data to be robust to different housing types and chicken breeds.
Author(s): O'Sullivan J, Gray HE, Moat G, Asher L
Publication type: Article
Publication status: Published
Journal: Royal Society Open Science
Year: 2026
Volume: 13
Issue: 5
Online publication date: 27/05/2026
Acceptance date: 16/04/2026
Date deposited: 22/06/2026
ISSN (electronic): 2054-5703
Publisher: Royal Society Publishing
URL: https://doi.org/10.1098/rsos.252462
DOI: 10.1098/rsos.252462
Data Access Statement: The methods established here are available in full along with a reduced selection of frame data approved by producers (https://figshare.com/s/407e15e121a8ab97dfef). The full sample of video data collected and analysed is not available due to its collection from commercial laying hen farms and lack of consent for wider distribution. Supplementary material is available online (https://doi.org/10.6084/m9.figshare.c.8449357)
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