Document Type
Article
Publication Date
12-2025
Abstract
Farmers face the critical challenge of monitoring cattle well-being for both ethical and economic success, relying on signals like body temperature and facial expressions to assess their animals' health. However, these indicators have traditionally relied on human observation with manual measurement, which is time-consuming and subjective. Despite this clear need, no automated system currently exists for monitoring cattle body temperature, and available datasets remain limited in scope. To address these challenges, we make two key contributions: (i) We introduce CattleFace-RGBT, a novel RGB-Thermal (RGB-T) Cattle Facial Landmark dataset consisting of 2,300 paired RGB and thermal images (4,600 images in total), each with associated ground-truth temperature measurements. The dataset was developed through a structured pipeline involving data acquisition, data processing, and semi-automated annotation with 13 facial keypoints; (ii) We propose CattleFever, an automated framework that uses thermal imagery to predict core body temperature. Our CattleFever system comprises three main components: facial keypoint detection, region-of-interest segmentation, feature selection, and temperature estimation. For the temperature estimation component, we explore two approaches: heuristic methods and machine learning approaches. Extensive experiments across various combination of facial regions of interest demonstrate the effectiveness of our framework, with machine learning models significantly outperforming traditional methods. Notably, Random Forest Regression achieves the highest accuracy in predicting core body temperature with a mean square error of 0.13-0.21 square degrees Fahrenheit, highlighting the potential of RGB-T data and AI-driven analysis for advancing precision livestock health monitoring.
Citation
Pham, T. T., Coffman, E., Kegley, B., Powell, J. G., Zhao, J., & Le, N. (2025). CattleFever: An automated cattle fever estimation system. Smart Agricultural Technology, 12 https://doi.org/10.1016/j.atech.2025.101434
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Keywords
Cattle, Fever, Keypoints, Automated fever estimation, Machine learning
Included in
Agriculture Commons, Animal Sciences Commons, Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons
Comments
Web of Science
Elseiver