Design of a Soft Real-Time Web System for Monitoring Greenhouse Climate Using Arduino Platform and ESP32

Document Type : Original Research

Authors

Department of Agrotechnology, Faculty of Aburaihan, University of Tehran, Tehran, Iran

10.22103/bbr.2026.27194.1150

Abstract

Greenhouse crops are highly sensitive to fluctuations in environmental parameters; their survival and productivity depend heavily on maintaining optimal conditions. With the global expansion of greenhouse cultivation, including in Iran, intelligent climate monitoring systems have become essential. This project designs a smart system for monitoring greenhouse climates using Internet of Things (IoT) technology. Three key parameters―temperature, relative air humidity, and light intensity―are measured in real time and displayed on a web-based user interface. The microcontroller connects to the internet via Wi-Fi and transmits data collected by the DHT11 and GY-30 sensors to a web server using the HTTP protocol, enabling remote monitoring. The system utilizes cost-effective hardware, specifically the ESP32 board, and software tools like the Arduino IDE for data processing and communication. Key features include dynamic color changes of graphical gauges based on parameter status, automatic data updates every minute, a soft real-time display of the last update time, and visual alert mechanisms for critical conditions. Comparisons with related studies show that this system delivers reliable performance and is suitable for various greenhouse applications while maintaining simplicity and low cost.

Keywords


Alaviyan, Y., Aghaseyedabdollah, M., Sadafi, M.,
& Yazdizade, A. (2020). Design and manufacture
of a smart greenhouse with supervisory control of
environmental parameters using fuzzy inference
controller. 2020 6th Iranian Conference on Signal
Processing and Intelligent Systems (ICSPIS), (pp.
1-6). IEEE. (In Persian)
https://doi.org/10.1109/ICSPIS51611.2020.9349619
Arathi, P., Krishnan, A., Krishna, D. Y., Gagana,
Y., & Patel, G. (2023). Greenhouse monitoring
and controlling using arduino. International
Journal of Engineering and Management
Research, 13(3), 138–139.
Asane, S., Salve, S., Birajdar, S., Borkar, S., &
Gawali, P. (2025). IoT-Driven Smart Greenhouse
System for Real-Time Environmental Monitoring.
European Journal of Scientific Research and
Reviews, 2(4), 220–220.
https://doi.org/10.5455/EJSRR.20250430014602
Gaikwad, A., Ghatge, A., Kumar, H., & Mudliar,
K. (2016). Monitoring of smart greenhouse.
International Research Journal of Engineering
and Technology (IRJET), 3, 573–575.
Hammami, A. (2019). Smart environment data
monitoring. 2019 International Conference on
Computer and Information Sciences (ICCIS) (pp.
1-6). IEEE.
https://doi.org/10.1109/ICCISci.2019.8716469
Hong, G.-Z., & Hsieh, C.-L. (2016). Application of
integrated control strategy and bluetooth for
irrigating romaine lettuce in greenhouse. IFACPapersOnLine, 49(16), 381–386.
https://doi.org/10.1016/j.ifacol.2016.10.070
Kumar, A., Singh, V., Kumar, S., Jaiswal, S. P., &
Bhadoria, V. S. (2022). IoT enabled system to
monitor and control greenhouse. Materials Today:
Proceedings, 49, 3137–3141.
https://doi.org/10.1016/j.matpr.2020.11.040
Massah, J., Asefpour, K., Sami, S., & Jazayeri, I.
(2026). Design, development, and performance
evaluation of a GPS and machine vision-based
navigation system for an intelligent field guard
robot. Iran Agricultural Research, 45(1), 113–126.
Massah, J., Tahmasebi, H., Roozban, M. R., &
Azadegan, B. (2023). The effects of an automated
system for climate control on the yield of
greenhouse crops (case study on rose). Journal of
Crops Improvement, 25(1), 269–278.
https://doi.org/10.22059/jci.2022.339739.2687
Osman, S. O., Mohamed, M. Z., Suliman, A. M., &
Mohammed, A. A. (2018). Design and
implementation of a low-cost real-time in-situ
drinking water quality monitoring system using
arduino. 2018 International Conference on
Computer, Control, Electrical, and Electronics
Engineering (ICCCEEE) (pp. 1-7). IEEE
https://doi.org/10.1109/ICCCEEE.2018.8515886
Pradeep, M., Rinku, D. R., Swapna, P., Jyothi, V.,
Athiraja, A., & Prasannakumar, G. (2024). An
IoT Based Greenhouse Remote Monitoring
System for Automation of Supervision for Optimal
Plant Growth. 2024 10th International Conference
on Advanced Computing and Communication
Systems (ICACCS) (Vol. 1, pp. 797-802). IEEE.
https://doi.org/10.1109/ICACCS60874.2024.10716941

Risheh, A., Jalili, A., & Nazerfard, E. (2020). Smart
Irrigation IoT solution using transfer learning for
neural networks. 2020 10th International
Conference on Computer and Knowledge
Engineering (ICCKE) (pp. 342-349). IEEE.
https://doi.org/10.1109/ICCKE50421.2020.9303612
Sami, S., Hajian, M., & Nazghelichi, T. (2026). A
Time-Driven Approach Leveraging Universally
Accessible Features for Photovoltaic Power
Forecasting. Iranica Journal of Energy &
Environment, 17(2), 382–396.
https://doi.org/10.5829/ijee.2026.17.02.13
Taru, Y. K., & Karwankar, A. (2017). Water
monitoring system using arduino with labview.
2017 International Conference on Computing
Methodologies and Communication (ICCMC) (pp.
416-419). IEEE.
https://doi.org/10.1109/ICCMC.2017.8282722
Tembhurne, V., Bhatkar, M., & Ikhe, Y. (2022).
Automatic greenhouse environment monitoring
and controlling system. International Journal of
Research in Engineering, Science and
Management, 5(12), 30–33.
Tjahjono, B., Fauzia, Y., & Kurnia, R. (2023).
Prototyping of precission farming hydroponic
garden using arduino using design thinking
method at puriponic greenhouse depok.
International Journal of Science, Technology &
Management, 4(2), 305–310.
https://doi.org/10.46729/ijstm.v4i2.774