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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]
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Database: Engineering Source
Description
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]
ISSN:01681699
DOI:10.1016/j.compag.2025.110568