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Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine

Jiefei Liu1, Derek W Bailey2, Huiping Cao1

  • 1Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA.

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This study introduces an unsupervised machine learning framework to automatically identify cattle behaviors using Global Positioning System (GPS) tracking data, reducing the need for manual observation.

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Area of Science:

  • Animal Science
  • Machine Learning
  • Geographic Information Systems

Background:

  • Global Positioning Systems (GPS) enable remote livestock monitoring for well-being and pasture use.
  • Supervised machine learning for behavior identification is labor-intensive due to required animal observations.

Purpose of the Study:

  • To develop an automated method for identifying cattle behaviors using unsupervised learning techniques.
  • To eliminate the need for human observations in analyzing livestock behavior from GPS data.

Main Methods:

  • A two-step framework was designed: time series segmentation of GPS data followed by cluster analysis and labeling.
  • Unsupervised learning techniques were applied to GPS tracking data from five cows in a rangeland pasture.
  • Cattle movement pathways were clustered based on velocity and distance from water, then classified into walking, grazing, and resting behaviors.

Main Results:

  • Six distinct behavior clusters were identified from cow movement data.
  • The framework successfully classified behaviors into walking (mean velocity 44 m/min), grazing (13 m/min), and resting (2 m/min).
  • Predicted diurnal patterns revealed typical grazing bouts in the early morning and evening.

Conclusions:

  • The proposed unsupervised framework effectively predicts cattle behavior from unlabeled GPS tracking data.
  • This approach offers a labor-saving alternative to traditional supervised methods for livestock behavior analysis.
  • The findings demonstrate the applicability of advanced machine learning for ecological and agricultural monitoring.