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

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 pi g farming, with benefits for animal welfare, the farming industry, and broader efforts to minimize AMR risk