Deep Learning-Based Classification of Stress in Sows Using Facial Images

Syed U. Yunas, Ajmal Shahbaz, Emma M. Baxter, Marianne Farish, Kenneth M. D. Rutherford, Mark F. Hansen, Melvyn Smith, Lyndon N. Smith

2025

Abstract

Stress in pigs is a significant factor contributing to poor health, increased antimicrobial usage, and the subsequent risk of antimicrobial resistance (AMR), which poses a major challenge for the global pig farming industry. In this paper, we propose using deep learning (DL) methods to classify stress levels in sows based on facial features captured from images. Early identification of stress can enable targeted interventions, potentially reducing health risks and mitigating AMR concerns. Our approach utilizes convolutional neural network (CNN) models, specifically YOLO8l-cls, to classify the stress levels of sows (pregnant pigs) into low-stressed and high-stressed categories. Experimental results demonstrate that YOLO8l-cls outperforms other classification methods, with an overall F1-score of 0.74, Cohen’s Kappa of 0.63, and MCC of 0.60. This highlights the model’s effectiveness in accurately identifying stress levels and its potential as a practical tool for stress management in pig farming, with benefits for animal welfare, the farming industry, and broader efforts to minimize AMR risk.

Paper Citation

in Harvard Style

Yunas S., Shahbaz A., Baxter E., Farish M., Rutherford K., Hansen M., Smith M. and Smith L. (2025). Deep Learning-Based Classification of Stress in Sows Using Facial Images. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 390-396. DOI: 10.5220/0013327900003911

in Bibtex Style

@conference{bioimaging25,
author={Syed Yunas and Ajmal Shahbaz and Emma Baxter and Marianne Farish and Kenneth Rutherford and Mark Hansen and Melvyn Smith and Lyndon Smith},
title={Deep Learning-Based Classification of Stress in Sows Using Facial Images},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={390-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013327900003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Deep Learning-Based Classification of Stress in Sows Using Facial Images
SN - 978-989-758-731-3
AU - Yunas S.
AU - Shahbaz A.
AU - Baxter E.
AU - Farish M.
AU - Rutherford K.
AU - Hansen M.
AU - Smith M.
AU - Smith L.
PY - 2025
SP - 390
EP - 396
DO - 10.5220/0013327900003911
PB - SciTePress