Abstract
Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature.
Highlights
The pig livestock sector is a main component of Chinese animal husbandry, and the pig industry faces many challenges
This paper investigated into the methodology of utilizing IRT for measuring the core This paper investigated into the methodology of utilizing IRT for measuring the core temperature temperature of pigs
In order to improve the accuracy of non-contact IRT in pig internal body temperature measurement, this paper investigated the influence of air temperature and humidity on the skin temperatures of regions of interest (ROI) and incorporated environmental factors into prediction models
Summary
The pig livestock sector is a main component of Chinese animal husbandry, and the pig industry faces many challenges. Pigs are constantly threatened by infectious diseases, which will cause respiratory, digestive, or reproductive disorders, even leading to death, making the industry vulnerable and less efficient [1]. The recent outbreak of African Swine Fever was a living example of how much cost infectious diseases can cause [2]. With the trend of swine breeding and upbringing developing towards a large-scale and digitalized style [3], the early detection and prevention of swine epidemics has become a core issue for the swine industry. The early symptoms of these diseases might appear to be a body temperature rise of up to 41–43 ◦ C [5]. Monitoring the body temperature of swine individuals enables the early detection of and quick response to epidemics
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