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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.

Sensors (Basel, Switzerland)

|December 31, 2025

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 twin hybrid model smart swine barns time-series prediction virtual 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.