Non-Contact Evaluation of Pigs' Body Temperature Incorporating Environmental Factors

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-07, Vol.20 (15), p.4282
• Main Authors: Jia, Guifeng, Li, Wei, Meng, Junyu, Tan, Hequn, Feng, Yaoze
• Format: Article
• Language: English
• Subjects: Accuracy; African swine fever; Air temperature; Animals; Automation; Back propagation; Body Temperature; Epidemics; Errors; Experiments; Female; Fever; Heat; Hogs; Humidity; Infectious diseases; Infrared imaging; Internet of Things; pigs; Prediction models; Skin; Skin temperature; Support vector machines; support vector regression; Swine; Temperature; temperature prediction model
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20154282

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.