Deep learning-based temperature detection for group-raised pigs via thermal images
Body temperature detection of pigs is vital for efficient pig breeding. In this study, a novel approach for individual pig temperature detection in a group-raised environment is proposed. The methodology includes three approaches: (1) a Porcine-ESRGAN algorithm based on generative adversarial networ...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500437X |
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| Summary: | Body temperature detection of pigs is vital for efficient pig breeding. In this study, a novel approach for individual pig temperature detection in a group-raised environment is proposed. The methodology includes three approaches: (1) a Porcine-ESRGAN algorithm based on generative adversarial networks (GANs) was developed to reconstruct super-resolution of thermal infrared images, effectively addressing low-resolution limitations, (2) a novel temperature extraction method incorporating instantaneous recognition of postures was established to overcome challenges in measuring pigs in the lying position, and (3) an enhanced YOLOv7 architecture (termed ITG-YOLOv7) was implemented to improve the accuracy of thermal region detection. A performance evaluation demonstrated the following significant improvements achieved by the proposed methodology: Porcine-ESRGAN increased precision (P), recall (R), and mean average precision (mAP) for lying-position pigs by 3.1%, 11.5%, and 6.9%, respectively. Furthermore, the posture-based method attained a higher efficiency of 29.69% in the extraction of key-region temperature. ITG-YOLOv7 attained 93.2% P, 95.4% R, and 97.5% mAP when integrated with Porcine-ESRGAN. The mean absolute error (MAE) of the measured temperature was 0.09°C for upright pigs and 0.23°C for lying pigs. The results validate the framework’s efficacy for automated, highly precise monitoring of group-housed pigs’ body temperatures. |
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| ISSN: | 2772-3755 |