Тип публикации: статья из журнала

Год издания: 2025

Идентификатор DOI: 10.3390/agriengineering7090285

Аннотация: <jats:p>Integration of IoT and predictive modeling is critical for optimizing microclimate management in urban-agglomeration vertical farming. In this study, we present a hybrid digital twin approach that combines a physical microclimate model with a distributed IoT monitoring system to simulate and control the phytotron environmenПоказать полностьюt. A set of heat- and mass-balance equations governing the dynamics of temperature, humidity, and transpiration was implemented and parameterized using a genetic algorithm (GA)—an evolutionary optimization method—with real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO2) and Wi-Fi-connected power meters managed by Home Assistant. The optimized model achieved mean temperature deviations ≤ 0.1 °C, relative humidity errors ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach. These results demonstrate that the proposed framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization in vertical farming, while also laying the groundwork for scalable digital twins in controlled-environment agriculture.</jats:p>

Журнал: AgriEngineering

Выпуск журнала: Т. 7, № 9

Номера страниц: 285

ISSN журнала: 26247402

Издатель: MDPI

• Bukhtoyarov Vladimir V. (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Nekrasov Ivan S. (Department of Technological Machines and Equipment of Oil and Gas Complex, School of Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Timofeenko Ivan A. (Interdisciplinary Laboratory of City Farming, Institute of Gastronomy, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Gorodov Alexey A. (Department of Technological Machines and Equipment of Oil and Gas Complex, School of Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Kartushinskii Stanislav A. (School of Space and Information Technology, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Trofimov Yury V. (Center LED and Optoelectronics Technologies of National Academy Sciences of Belarus, 220090 Minsk, Belarus)
• Lishik Sergey I. (Center LED and Optoelectronics Technologies of National Academy Sciences of Belarus, 220090 Minsk, Belarus)

• Ядро РИНЦ (eLIBRARY.RU)