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2025 Vol.7, Issue 2 Preview Page

Review Article

Remote management and variability assessment of environmental conditions for smart vertical farms: A review
Ezatullah Zakir1
,
Md Nasim Reza12
,
Md Rejaul Karim1
,
Gusti Ayu Putri Mei Ulainti1
,
Md Razob Ali1
,
Hongbin Jin2
,
Sun-Ok Chung12*†
,
Md Shaha Nur Kabir3*†
1Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
2Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
3Department of Agricultural and Industrial Engineering, Faculty of Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh
*Corresponding Author
†The authors equally contributed as the corresponding authors.
30 June 2025. pp. 114-133
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Abstract

Vertical farming (VF) has emerged as a transformative approach to urban agriculture, enabling year-round crop production in compact, multilayered indoor environments. By employing soilless cultivation systems and climate-controlled conditions, VF decouples food production from environmental constraints such as limited arable land and changing climate. However, the vertical stratification of crops introduces significant microclimatic variability in temperature, humidity, light intensity, and CO2 concentration, which can adversely affect crop uniformity and resource efficiency. This review aimed to explore remote management technologies and variability assessment of environmental conditions for smart vertical farms. The integration of wireless sensor network (WSNs), Internet of Thing (IoT) technologies, and low-power wide-area network (LPWAN) protocols like long range wide area network (LoRaWAN) for environmental data acquisition and control were evaluated. Key sensing modules for monitoring critical variables such as pH, electrical conductivity (EC), temperature, and CO2 are discussed, alongside recent developments in real-time communication, edge computing, and machine learning (ML)-driven control systems. A particular focus is given to microclimatic variability assessment using geostatistical and ML-based methods for spatial-temporal mapping and predictive decision-making. The review also analyzes power supply strategies, including energy harvesting, node placement optimization, and signal preprocessing techniques for noise reduction and multi-sensor fusion. Findings reveal that wireless systems offer considerable advantages over wired setups in terms of flexibility, scalability, and operational efficiency in VF environments. The successful deployment of smart VF systems depends on the precise alignment of sensor placement, communication protocols, data processing, and visualization interfaces. Despite advancements, challenges remain, including signal attenuation in enclosed layers, energy limitations, and sensor drift under harsh microclimates.

Keywords
Smart farming
Remote monitoring
IoT
Environmental sensing
Spatial variability
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Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 7
  • No :2
  • Pages :114-133
  • Received Date : 2025-06-20
  • Revised Date : 2025-06-27
  • Accepted Date : 2025-06-27
  • DOI :https://doi.org/10.22765/pastj.20250010
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