An Automatic Ear Temperature Monitoring Method for Group-Housed Pigs Adopting 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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
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| Series: | Animals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-2615/15/15/2279 |
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| Summary: | 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, 10,812 pig ears) was processed using 5-fold cross-validation before training the ear detection model. The model recognized pigs’ ears with a precision of 93.74% related to threshold intersection over union (IoU). Correlation analysis between manually extracted and algorithm-derived ear temperatures from 400 pig ear samples showed coefficients of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>) of 0.97 for maximum and 0.88 for average values. This demonstrates that our proposed method is feasible and reliable for automatic pig ear temperature monitoring, serving as a powerful tool for early health warning. |
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| ISSN: | 2076-2615 |