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Multistage pig identification using a sequential ear tag detection pipeline.

Martin Wutke1, Damiano Debiasi2, Shobhana Tomar2

  • 1Institute of Animal Breeding and Husbandry, Faculty of Agricultural and Nutritional Sciences, Christian-Albrechts-University Kiel, Kiel, 24118, Germany. mwutke@tierzucht.uni-kiel.de.

Scientific Reports
|June 20, 2025
PubMed
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This summary is machine-generated.

This study presents a robust, lighting-invariant method for identifying individual pigs using ear tags. The computer vision approach enhances livestock management through reliable, automated animal identification.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Animal Science

Background:

  • Accurate livestock identification is crucial for welfare and behavior monitoring.
  • Biometric identification is limited for species like pigs; ear tags are common but challenging for computer vision.
  • Existing computer vision methods struggle with poor lighting and complex ear tag coding.

Purpose of the Study:

  • To develop a robust, lighting-invariant method for individual pig identification using ear tags.
  • To overcome limitations of current computer vision techniques in livestock identification.
  • To support automated animal identification for precision livestock farming.

Main Methods:

  • A sequential object detection pipeline using four models: pig detection, ear tag localization, rotation correction (pin detection), and digit recognition.
Keywords:
Animal identificationComputer visionYOLOv10

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  • Leveraging commercially available ear tags for identification.
  • Evaluation in familiar (top-down) and unfamiliar (close-up) camera environments.

    • Main Results:

    • High independent model performance: mAP0.95 values of 0.970 (pig detection), 0.979 (ear tag detection), 0.974 (pin detection), and 0.979 (ID classification).
    • Excellent precision (0.996) in a familiar camera setup.
    • Strong generalization with 0.913 precision and 0.903 recall in an unfamiliar setup.
    • Public release of three custom datasets for ear tag, pin, and digit detection.

    Conclusions:

    • The proposed method provides effective, lighting-invariant individual pig identification.
    • The approach demonstrates strong performance and generalization capabilities.
    • The study contributes to automated animal identification and precision livestock farming, with potential integration into multi-object tracking systems.