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Correction: Gernhardt et al. Ex Vivo Computed Tomographic Morphometry and Motion of the Native and Fractured Equine Accessory Carpal Bone. <i>Animals</i> 2026, <i>16</i>, 1132.

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Pig-Posture Recognition Based on Computer Vision: Dataset and Exploration.

Hongmin Shao1,2, Jingyu Pu1,2, Jiong Mu1,2

  • 1College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.

Animals : an Open Access Journal From MDPI
|May 5, 2021
PubMed
Summary
This summary is machine-generated.

Early detection of pig diseases is possible through automated posture monitoring. This study developed a dataset and a deep learning model for accurate pig posture recognition, aiding in livestock health management.

Keywords:
agricultural automationautomated breedingcomputer visionpig postureposture recognition

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Area of Science:

  • Animal Science
  • Computer Science
  • Veterinary Medicine

Background:

  • Posture changes in growing pigs can indicate disease onset.
  • Manual monitoring of pig behavior is labor-intensive and time-consuming for large-scale farms.
  • Early detection of health issues is crucial for preventing disease outbreaks in swine populations.

Purpose of the Study:

  • To develop an automated system for monitoring pig postures to detect early signs of disease.
  • To create the first human-annotated dataset for pig posture identification.
  • To evaluate the efficacy of a deep separable convolutional network for pig posture classification.

Main Methods:

  • Established a novel dataset with 800 images per posture (standing, lying stomach, lying side, exploring).
  • Utilized a deep separable convolutional network for pig posture classification.
  • Validated the model's performance in a simulated piggery environment.

Main Results:

  • Achieved 92.45% accuracy in classifying pig postures using the deep separable convolutional network.
  • Demonstrated the feasibility of automated pig posture recognition.
  • The developed dataset and model show potential for real-world livestock farm applications.

Conclusions:

  • Automated pig posture recognition is effective for early disease detection in swine.
  • The established dataset and deep learning approach offer a scalable solution for livestock health monitoring.
  • This technology can significantly improve the efficiency and effectiveness of disease prevention in large-scale piggeries.