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Updated: Sep 12, 2025

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An algorithm for detecting cow lameness based on ensemble learning of keypoint motion features.

Yuhao Shen1, Baoshan Li1, Yueming Wang2

  • 1Inner Mongolia University of Science and Technology, School of Digital and Intelligence Industry, Baotou, 014010, China; Grassland Animal Husbandry Artificial Intelligence Inner Mongolia Autonomous Region Engineering Research Center, Baotou, 014010, China.

Journal of Dairy Science
|August 8, 2025
PubMed
Summary

This study improves dairy cow lameness detection by integrating keypoint-derived motion features. An enhanced YOLOv8-Pose model and ensemble learning achieve over 97% accuracy for intelligent lameness monitoring.

Keywords:
computer visioncow lamenessdeep learningensemble learningkeypoint detection

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Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Lameness significantly impacts dairy cow health, welfare, and productivity.
  • Farm environmental challenges like poor lighting and occlusion hinder accurate keypoint detection and motion analysis.
  • Individual motion features are often insufficient for comprehensive lameness assessment.

Purpose of the Study:

  • To develop an integrated lameness detection method for dairy cows using keypoint-derived motion features.
  • To enhance the accuracy of cow keypoint detection in complex farm environments.
  • To improve the robustness and accuracy of lameness classification by fusing multiple motion features.

Main Methods:

  • Improved YOLOv8-Pose for accurate cow keypoint detection under challenging conditions.
  • Extraction of three temporal motion features: hoof displacement, hoof speed, and head-neck motion.
  • Lameness classification using Conv2D-LSTM and ensemble learning (stacking) for feature fusion.

Main Results:

  • The enhanced YOLOv8-Pose model achieved high detection accuracy (e.g., 99.4% precision, 97.8% mAP@0.5).
  • Individual motion features exceeded 85% classification accuracy.
  • The integrated keypoint motion feature method reached an overall accuracy of 97.2%.

Conclusions:

  • The proposed method offers a feasible approach for intelligent lameness monitoring in dairy cows.
  • Keypoint detection enhancement and feature integration significantly improve lameness detection accuracy.
  • The algorithm demonstrates strong accuracy and generalization capabilities through cross-validation.