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Digital Twin-Based Smart Agriculture
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Digital Twin-Based Smart Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 March 2026) | Viewed by 1816

Special Issue Editors

Prof. Dr. Hyun Yoe

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
Interests: energy management; smart cattle shed; livestock disease; proximity UUID; big data
Dr. Meonghun Lee

E-Mail Website
Guest Editor
Department of Convergence Biosystems Mechanical Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
Interests: ICT convergence; wireless sensor network; computer networking; wireless communications; agricultural engineering; AI; cloud computing

Special Issue Information

Dear Colleagues,

The integration of digital twin (DT) technology into smart agriculture is revolutionizing how farming systems are designed, monitored, and optimized. By creating dynamic virtual replicas of physical agricultural assets and processes, digital twins enable real-time data analysis, predictive modeling, and decision-making support to enhance productivity, resource efficiency, and sustainability. This Special Issue invites original research articles, reviews, and case studies that explore the development, application, and future prospects of digital twin technologies in agriculture. Topics of interest include, but are not limited to, DT architectures for crop and livestock management, integration with IoT and AI systems, simulation of farming scenarios, precision agriculture applications, and challenges in data interoperability and system scalability. Contributions addressing the socio-economic and environmental impacts of digital twin adoption in agriculture are also highly encouraged. We aim to highlight cutting-edge research that advances the theoretical foundations, technological innovations, and practical applications of digital twin-based smart farming. We welcome submissions from academic researchers, industry practitioners, and policymakers alike.

Prof. Dr. Hyun Yoe
Dr. Meonghun Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • digital twin (DT)
  • IoT and AI systems
  • smart sensors
  • smart agriculture
  • precision agriculture

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Published Papers (1 paper)

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Research

19 pages, 1729 KB  
Article
Digital Twin-Based Virtual Sensor Data Prediction and Visualization Techniques for Smart Swine Barns
by Hyeon-O Choe and Meong-Hun Lee
Sensors 2025, 25(24), 7690; https://doi.org/10.3390/s25247690 - 18 Dec 2025
Cited by 1 | Viewed by 1230
Abstract
To address the limitations of sensor deployment and high maintenance costs in smart swine barns, this study proposes a digital twin (DT)-based virtual sensor prediction and visualization method. Spatial constraints and harsh barn environments often cause sensor blackout zones, hindering precise environmental monitoring. [...] Read more.
To address the limitations of sensor deployment and high maintenance costs in smart swine barns, this study proposes a digital twin (DT)-based virtual sensor prediction and visualization method. Spatial constraints and harsh barn environments often cause sensor blackout zones, hindering precise environmental monitoring. To overcome these challenges, a virtual sensor was defined at the central position between Zone 1 and Zone 2, and its data were generated using a hybrid model that combines inverse distance weighting (IDW)-based spatial interpolation with long short-term memory (LSTM)-based time-series prediction. The proposed method was evaluated using 34,992 datasets collected from January to August 2025. Performance analysis demonstrated that the hybrid model achieved high prediction accuracy, particularly for variables with strong spatial heterogeneity, such as carbon dioxide (CO2) and ammonia (NH3), with overall coefficients of determination (R2) exceeding 0.95. Furthermore, a Web-based graphics library (WebGL) digital twin visualization environment was developed to intuitively observe spatiotemporal changes in sensor data. The system integrates sensor placement, risk-level assessment, and time-series graphs, thereby supporting users in real-time environmental monitoring and decision-making. This approach improves the precision and reliability of smart barn management and contributes to the stabilization of farm income. Full article
(This article belongs to the Special Issue Digital Twin-Based Smart Agriculture)
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