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Evaluating machine learning algorithms to predict lameness in dairy cattle.

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Machine learning models using accelerometer data can identify dairy cattle lameness. The ROCKET classifier showed high accuracy in detecting cows needing therapeutic claw trimming and classifying lameness severity.

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Area of Science:

  • Animal Science
  • Veterinary Medicine
  • Data Science

Background:

  • Dairy cattle lameness is a significant concern in commercial farming, impacting animal welfare and productivity.
  • Gait abnormalities are key indicators of lameness, prompting the use of precision technologies like accelerometers for monitoring.
  • Machine learning (ML) offers potential for automated and accurate lameness detection in dairy herds.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning algorithms in identifying lameness conditions in dairy cattle using accelerometer data.
  • To compare the performance of different ML models and feature sets for lameness classification.
  • To assess the potential for granular disease classification and severity grading.

Main Methods:

  • 310 Holstein dairy cows were fitted with leg-based accelerometers to collect data on lying time, daily steps, and daily movement.
  • Cows were categorized into corrective claw trimming (CCT), therapeutic claw trimming (TCT) for lameness, or healthy controls.
  • Data underwent median filtering, and three feature sets (conventional, slope, and all features) were used with Random Forest, Naive Bayes, Logistic Regression, and ROCKET ML algorithms.

Main Results:

  • The ROCKET classifier achieved high accuracy (>90%), ROC-AUC (>74%), and F1 score (>0.61) for classifying cows requiring CCT and TCT.
  • Incorporating slope features and using all features (conventional + slope) significantly improved algorithm performance.
  • The ROCKET classifier also demonstrated satisfactory accuracy (>0.85) in classifying locomotion scores into severely and moderately lame conditions.
  • Classification of infectious versus non-infectious lameness was not effective with current models.

Conclusions:

  • Machine learning models, particularly the ROCKET classifier with accelerometer data, are effective tools for identifying lameness in dairy cows.
  • The use of slope features derived from accelerometer data enhances the accuracy of lameness detection.
  • Further research is needed to improve the granularity and accuracy of ML models for more precise lameness classification and disease differentiation.