Non-Contact Evaluation of a Pig′s Body Temperature Incorporating Environmental Factors
Autor: | Junyu Meng, Hequn Tan, Yao-Ze Feng, Gui-Feng Jia, Wei Li |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Fever
Mean squared error Swine 040301 veterinary sciences lcsh:Chemical technology Biochemistry Article Body Temperature Analytical Chemistry 0403 veterinary science Heat exchanger Statistics Animals temperature prediction model lcsh:TP1-1185 Electrical and Electronic Engineering support vector regression Instrumentation Mathematics infrared imaging Artificial neural network Temperature 0402 animal and dairy science Humidity pigs 04 agricultural and veterinary sciences Gold standard (test) 040201 dairy & animal science Atomic and Molecular Physics and Optics Random forest Support vector machine Female Predictive modelling |
Zdroj: | Sensors Volume 20 Issue 15 Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 4282, p 4282 (2020) |
ISSN: | 1424-8220 |
DOI: | 10.3390/s20154282 |
Popis: | 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&prime internal body temperature. |
Databáze: | OpenAIRE |
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