AI Based Digital Twin Model for Cattle Caring

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.