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AI Based Digital Twin Model for Cattle Caring.

Xue Han1, Zihuai Lin1, Cameron Clark2

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Summary
This summary is machine-generated.

This study introduces artificial intelligence (AI)-powered digital twins for cattle, using farm IoT data to monitor and predict their physiological and behavioral states with high accuracy.

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AILSTM modeldeep learningdigital twin

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Area of Science:

  • Agricultural Technology
  • Artificial Intelligence
  • Animal Science

Background:

  • Effective cattle management relies on real-time monitoring of physiological and behavioral states.
  • Existing farm IoT systems collect vast amounts of data but often lack advanced analytical capabilities.
  • Predicting future cattle states is crucial for optimizing herd health and productivity.

Purpose of the Study:

  • To develop and validate an AI-powered digital twin model for cattle status monitoring and prediction.
  • To leverage farm IoT sensor data for real-time cattle state assessment.
  • To forecast cattle physiological cycles and future time budgets.

Main Methods:

  • Utilized a farm Internet of Things (IoT) system for remote cattle monitoring and data acquisition.
  • Developed a digital twin model for cattle using Deep Learning (DL) algorithms.
  • Trained and optimized the DL model using extensive sensor data for high accuracy.

Main Results:

  • Achieved high accuracy in predicting cattle behavioral states.
  • Minimized training loss error to approximately 0.580.
  • Reduced prediction error for the next behavior state to approximately 5.197 after optimization.
  • Demonstrated real-time monitoring of cattle physiological cycles.

Conclusions:

  • The developed AI-powered digital twin offers a novel approach to cattle status monitoring and prediction.
  • The model accurately forecasts cattle behavior and future time budgets.
  • This technology has the potential to significantly enhance farm management and animal welfare through data-driven insights.