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Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm.

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Title: Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm.
Authors: Yu, Phoenix1 (AUTHOR) ho19002@bristol.ac.uk, Burghardt, Tilo1 (AUTHOR), Dowsey, Andrew W.2 (AUTHOR), Campbell, Neill W.1 (AUTHOR)
Source: Computers & Electronics in Agriculture. Oct2025:Part B, Vol. 237, pN.PAG-N.PAG. 1p.
Subjects: Holstein-Friesian cattle, Biometric identification, Cattle, Supervised learning, Computer vision, Livestock, Farm management
Abstract: We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101,329 images of 90 cows, plus underlying original CCTV footage. The dataset is provided with full computer vision recognition baselines, that is both a supervised and self-supervised learning framework for individual cow identification trained on cattle tracklets. We report a performance above 96% single image identification accuracy from the dataset and demonstrate that combining data from multiple cameras during learning enhances self-supervised identification. We show that our framework enables automatic cattle identification, barring only the simple human verification of tracklet integrity during data collection. Crucially, our study highlights that multi-camera, supervised and self-supervised components in tandem not only deliver highly accurate individual cow identification, but also achieve this efficiently with no labelling of cattle identities by humans. We argue that this improvement in efficacy has practical implications for livestock management, behaviour analysis, and agricultural monitoring. For reproducibility and practical ease of use, we publish all key software and code including re-identification components and the species detector with this paper, available at https://tinyurl.com/MultiCamCows2024. [Display omitted] • Cattle Re-ID achieves ∼ 96% accuracy on a farm using contrastive loss function. • Requiring no human labelling, self-supervised cattle ID matches supervised accuracy. • Adding more cameras boosts the Re-ID system's accuracy. • First multi-view on-farm cattle Re-ID dataset and source code released. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 187263950
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm.
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  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Phoenix%22">Yu, Phoenix</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ho19002@bristol.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Burghardt%2C+Tilo%22">Burghardt, Tilo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dowsey%2C+Andrew+W%2E%22">Dowsey, Andrew W.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Campbell%2C+Neill+W%2E%22">Campbell, Neill W.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Electronics+in+Agriculture%22">Computers & Electronics in Agriculture</searchLink>. Oct2025:Part B, Vol. 237, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Holstein-Friesian+cattle%22">Holstein-Friesian cattle</searchLink><br /><searchLink fieldCode="DE" term="%22Biometric+identification%22">Biometric identification</searchLink><br /><searchLink fieldCode="DE" term="%22Cattle%22">Cattle</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Livestock%22">Livestock</searchLink><br /><searchLink fieldCode="DE" term="%22Farm+management%22">Farm management</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101,329 images of 90 cows, plus underlying original CCTV footage. The dataset is provided with full computer vision recognition baselines, that is both a supervised and self-supervised learning framework for individual cow identification trained on cattle tracklets. We report a performance above 96% single image identification accuracy from the dataset and demonstrate that combining data from multiple cameras during learning enhances self-supervised identification. We show that our framework enables automatic cattle identification, barring only the simple human verification of tracklet integrity during data collection. Crucially, our study highlights that multi-camera, supervised and self-supervised components in tandem not only deliver highly accurate individual cow identification, but also achieve this efficiently with no labelling of cattle identities by humans. We argue that this improvement in efficacy has practical implications for livestock management, behaviour analysis, and agricultural monitoring. For reproducibility and practical ease of use, we publish all key software and code including re-identification components and the species detector with this paper, available at https://tinyurl.com/MultiCamCows2024. [Display omitted] • Cattle Re-ID achieves ∼ 96% accuracy on a farm using contrastive loss function. • Requiring no human labelling, self-supervised cattle ID matches supervised accuracy. • Adding more cameras boosts the Re-ID system's accuracy. • First multi-view on-farm cattle Re-ID dataset and source code released. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.compag.2025.110568
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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        Type: general
      – SubjectFull: Biometric identification
        Type: general
      – SubjectFull: Cattle
        Type: general
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Livestock
        Type: general
      – SubjectFull: Farm management
        Type: general
    Titles:
      – TitleFull: Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm.
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            NameFull: Yu, Phoenix
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            NameFull: Burghardt, Tilo
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            – D: 05
              M: 10
              Text: Oct2025:Part B
              Type: published
              Y: 2025
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              Value: 237
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