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Automated system for calving time prediction and cattle classification utilizing trajectory data and movement

Wai Hnin Eaindrar Mg1, Thi Thi Zin2, Pyke Tin3

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Accurate cattle calving prediction is now possible with a new automated system. This technology classifies calving as normal or abnormal and predicts timing, improving livestock management and animal welfare.

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Area of Science:

  • Agricultural Science
  • Animal Science
  • Computer Vision

Background:

  • Accurate calving time prediction is crucial for livestock management and animal welfare.
  • Current methods may lack precision and automation.
  • Behavioral analysis offers potential for improved prediction.

Purpose of the Study:

  • To develop an automated system for cattle calving classification and time prediction.
  • To utilize 12-hour trajectory data for individual cow behavior analysis.
  • To enhance livestock management through precise calving event forecasting.

Main Methods:

  • A tailored YOLOv8 model was used for efficient cattle detection and noise filtering.
  • A Customized Tracking Algorithm (CTA) with Global IDs optimization ensured continuous, accurate cow tracking.
  • Three total movement features and three cumulative movement features were extracted for classification and prediction.

Main Results:

  • The automated system achieved 99% overall accuracy in detecting and tracking 20 cattle over 12 hours.
  • Classification accuracy for normal/abnormal calving reached 100%, 95%, and 85% based on different features.
  • Calving time prediction precision was achieved within 6, 9, and 8 hours, respectively.

Conclusions:

  • The developed system provides automated, accurate calving classification and time prediction.
  • This technology supports timely farmer intervention, enhancing cow and calf health.
  • The system optimizes resource allocation and farm efficiency, contributing to sustainable livestock farming.