{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T16:26:27Z","timestamp":1791044787739,"version":"4.1.0"},"reference-count":41,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003668","name":"Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries","doi-asserted-by":"publisher","award":["RS-2024-00406426"],"award-info":[{"award-number":["RS-2024-00406426"]}],"id":[{"id":"10.13039\/501100003668","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s25247690","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T16:42:05Z","timestamp":1766076125000},"page":"7690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Digital Twin-Based Virtual Sensor Data Prediction and Visualization Techniques for Smart Swine Barns"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8694-4237","authenticated-orcid":false,"given":"Hyeon-O","family":"Choe","sequence":"first","affiliation":[{"name":"Low-Carbon Agriculture-Based Smart Distribution Research Center, Sunchon National University, Suncheon 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8863-7580","authenticated-orcid":false,"given":"Meong-Hun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Convergence Biosystems Mechanical Engineering, Sunchon National University, Suncheon 57922, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"ref_1","first-page":"38","article-title":"Research and development trends of livestock smart farms using ICT convergence technology","volume":"59","author":"Kwon","year":"2017","journal-title":"Rural. 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