International Journal of Biometeorology

, Volume 63, Issue 10, pp 1405–1415 | Cite as

Establishment of validated models for non-invasive prediction of rectal temperature of sows using infrared thermography and chemometrics

  • Yao-Ze FengEmail author
  • Hai-Tao Zhao
  • Gui-Feng Jia
  • Chijioke Ojukwu
  • He-Qun Tan
Original Paper


Rectal temperature is an important physiological indicator used to characterize the reproductive and health status of sows. Infrared thermography, a surface temperature measurement technology, was investigated in this study to explore its feasibility in non-invasive detection of rectal temperature in sows. A total of 124 records of rectal temperature and surface temperature in various body regions of 99 Landrace × Yorkshire crossbred sows were collected. These surface temperatures together with ambient temperature, ambient humidity, and wind speed in pig pens were correlated with the real rectal temperature of sows to establish rectal temperature prediction models by introducing chemometrics algorithms. Two types of models, i.e., full feature models and selected feature models, were established by applying the partial least squares regression (PLSR) method. The optimal model was attained when 7 important features were selected by LARS-Lasso, where correlation coefficients and root mean squared errors of calibration were 0.80 and 0.30 °C, respectively. Particularly, the validity and stability of established simplified models were further evaluated by applying the model to an independent prediction set, where correlation coefficients and root mean squared errors for prediction were 0.80 and 0.35 °C, respectively. The validation of established models is scarce in previous similar studies. Above all, this study demonstrated that introduction of chemometrics methodologies would lead to more reliable and accurate model for predicting sow rectal temperature, thus the potential for ensuring animal welfare in a broader view if extended to more applications.


Sows Rectal temperature Surface temperature Infrared thermography Partial least squares regression Chemometrics 


Funding information

The authors would like to thank the financial supports from the National Key R&D Program of China (2018YFD0500700) and the Natural Science Foundation of Hubei Province (2018CFB099).


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Copyright information

© ISB 2019

Authors and Affiliations

  1. 1.College of EngineeringHuazhong Agricultural UniversityWuhanChina
  2. 2.Key Laboratory of Agricultural Equipment in Mid-lower Yangtze RiverMinistry of Agriculture and Rural AffairsWuhanChina

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