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Cattle lameness detection using depth image and deep learning.

San Chain Tun1, Pyke Tin2, Masaru Aikawa3

  • 1Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki, 889-2192, Japan.

Scientific Reports
|March 14, 2026
PubMed
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This study introduces a deep learning framework for cattle lameness detection using depth images. The system achieves high accuracy in identifying and tracking lame animals, improving welfare monitoring.

Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Lameness in cattle presents significant animal welfare and economic challenges.
  • Current monitoring methods are often manual and subjective.
  • Automated systems are needed for continuous and objective assessment.

Purpose of the Study:

  • To develop and evaluate an end-to-end deep learning framework for 24/7 cattle lameness monitoring.
  • To compare different instance segmentation models and tracking algorithms.
  • To optimize a spatio-temporal model for accurate lameness classification.

Main Methods:

  • The framework utilizes instance segmentation (YOLOv11m-seg), a custom tracking algorithm (PTAV3), and a spatio-temporal classification model (EfficientNet-B7 + LSTM).

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Last Updated: Mar 15, 2026

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Published on: November 28, 2025

253
  • Top-down depth images of cattle were used for detection, tracking, and classification.
  • Multiple model configurations and pre-processing techniques were evaluated.

    • Main Results:

    • YOLOv11m-seg achieved high detection accuracy (Mask AP@50: 99.26%) at 75.49 FPS.
    • The PTAV3 tracking algorithm reached 99.94% overall accuracy.
    • The best classification model (EfficientNet-B7 + LSTM) achieved 95.95% accuracy and 96.06% F1-score.

    Conclusions:

    • The developed deep learning framework offers a robust, automated, and objective solution for cattle lameness scoring.
    • This system demonstrates significant potential for real-time animal welfare monitoring in agricultural settings.
    • The integrated approach enhances efficiency and accuracy in managing cattle health.