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Digital Twin-Based Virtual Sensor Data Prediction and Visualization Techniques for Smart Swine Barns.

Hyeon-O Choe1, Meong-Hun Lee2

  • 1Low-Carbon Agriculture-Based Smart Distribution Research Center, Sunchon National University, Suncheon 57922, Republic of Korea.

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|December 31, 2025
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Summary
This summary is machine-generated.

This study introduces a digital twin (DT) approach using a hybrid virtual sensor model to overcome sensor limitations in smart swine barns. The method enhances environmental monitoring accuracy and supports real-time decision-making for improved farm management.

Keywords:
digital twinhybrid modelsmart swine barnstime-series predictionvirtual sensor

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Area of Science:

  • Agricultural Engineering
  • Environmental Monitoring
  • Digital Twins

Background:

  • Smart swine barns face challenges with sensor deployment, leading to blind spots and high maintenance costs.
  • Precise environmental monitoring is crucial for optimal animal welfare and farm productivity.

Purpose of the Study:

  • To develop a digital twin (DT)-based virtual sensor prediction and visualization method for smart swine barns.
  • To overcome limitations of physical sensor deployment and harsh environmental conditions.

Main Methods:

  • A hybrid model combining inverse distance weighting (IDW) for spatial interpolation and long short-term memory (LSTM) for time-series prediction was used to generate virtual sensor data.
  • A Web-based graphics library (WebGL) was employed to create an intuitive digital twin visualization environment.

Main Results:

  • The hybrid model achieved high prediction accuracy (R² > 0.95) for key variables like carbon dioxide (CO2) and ammonia (NH3), especially those with strong spatial heterogeneity.
  • The digital twin visualization system effectively integrated sensor data, risk assessment, and time-series analysis for real-time monitoring.

Conclusions:

  • The proposed virtual sensor and digital twin approach significantly improves the precision and reliability of environmental monitoring in smart swine barns.
  • This technology supports enhanced farm management decisions, contributing to stable farm income and operational efficiency.