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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

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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 identification Computer vision YOLOv10

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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.