An Automatic Ear Temperature Monitoring Method for Group-Housed Pigs Based on Infrared Thermography

The goal of this study was to develop an automated monitoring system based on Infrared thermography (IRT) for the detection of group-housed pig ears temperature. The aim in the first part of the study was to recognize pigs’ ears by using neural network analysis (SwinStar-YOLO). In the second part of the study, the goal was to automatically extract the maximum and average values of the temperature in the ear region using morphological image processing and a temperature matrix. Our dataset (3600 pictures, 10812 pig ears) was processed using 5-fold cross-validation before training the ear detection model. The model recognized pig’s ear with a precision of 93.74% related to threshold intersection over union (IoU). Correlation analysis of ear temperature extracted by artificial and the method proposed in this research, and the coefficients of determination maximum and average values of the temperature in the ear region were 0. 97 and 0. 88, respectively, for 400 pig ear samples. The method proposed in this research is feasible and reliable for automatic monitoring of ear temperature of pigs, and will be a powerful tool for early warning of pig health.