Green House Effect and Internet of Things: A Review


  • Qammar Shabbir Rana National Defense University Islamabad
  • Ali Iqtadar Mirza Govt College University Lahore


Internet of Things, greenhouses, Information technology, farming systems


This article summarises the current state of knowledge concerning Internet of Things (IoT) systems for ideal greenhouse conditions. Descriptive and statistical methods were applied to the data in order to draw conclusions regarding the connections between the IoT, new technologies, precision farming, Agriculture 4.0, and productive commercial agriculture. This is discussed within the broader context of the Internet of Things (IoT) and its role in reducing the negative impacts of climate change and global warming on agriculture through the optimization of key parameters like temperature and humidity, intelligent data acquisition, rule-based control, and removing obstacles to the widespread use of IoT in this sector of the economy. Low agricultural yields and losses have been exacerbated by recent unexpected and severe weather events; this is a challenge that can be overcome with technology-mediated precision agriculture. Over time, technological advancements have led to the creation of sensors that can detect and warn of impending frost, monitor crops remotely, protect against fire hazards, precisely regulate nutrient levels in soilless greenhouse cultivation, eliminate the need for grid power by relying solely on solar power, and control feeding, shading, and lighting systems intelligently to maximize crop output while minimizing overhead expenses. The limited adoption of smart technologies in commercial agriculture, the price, and the accuracy of the sensors are just some of the specific challenges. Future R&D initiatives and commercial applications can be aided by considering the obstacles and challenges.


K. Wang et al., “How does the Internet of Things (IoT) help in microalgae biorefinery?,” Biotechnol. Adv., vol. 54, p. 107819, Jan. 2022, doi: 10.1016/J.BIOTECHADV.2021.107819.

Y. Zhang, P. Geng, C. B. Sivaparthipan, and B. A. Muthu, “Big data and artificial intelligence based early risk warning system of fire hazard for smart cities,” Sustain. Energy Technol. Assessments, vol. 45, p. 100986, Jun. 2021, doi: 10.1016/J.SETA.2020.100986.

Z. Allam and Z. A. Dhunny, “On big data, artificial intelligence and smart cities,” Cities, vol. 89, pp. 80–91, Jun. 2019, doi: 10.1016/J.CITIES.2019.01.032.

H. Agrawal, J. Prieto, C. Ramos, and J. M. Corchado, “Smart feeding in farming through IoT in silos,” Adv. Intell. Syst. Comput., vol. 530, pp. 355–366, 2016, doi: 10.1007/978-3-319-47952-1_28/COVER.

B. Dawson and M. Spannagle, “AGRICULTURE AND FOOD SUPPLY IMPACTS,” Complet. Guid. to Clim. Chang., pp. 21–25, Nov. 2008, doi: 10.4324/9780203888469-6.

A. A. R. Madushanki, M. N. Halgamuge, W. A. H. S. Wirasagoda, and A. Syed, “Adoption of the Internet of Things (IoT) in agriculture and smart farming towards urban greening: A review,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 4, pp. 11–28, 2019, doi: 10.14569/ijacsa.2019.0100402.

A. Villa-Henriksen, G. T. C. Edwards, L. A. Pesonen, O. Green, and C. A. G. Sørensen, “Internet of Things in arable farming: Implementation, applications, challenges and potential,” Biosyst. Eng., vol. 191, pp. 60–84, Mar. 2020, doi: 10.1016/J.BIOSYSTEMSENG.2019.12.013.

M. N. Akhtar, A. J. Shaikh, A. Khan, H. Awais, E. A. Bakar, and A. R. Othman, “Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review,” Agric. 2021, Vol. 11, Page 475, vol. 11, no. 6, p. 475, May 2021, doi: 10.3390/AGRICULTURE11060475.

Y. Bo and H. Wang, “The application of cloud computing and the internet of things in agriculture and forestry,” Proc. - 2011 Int. Jt. Conf. Serv. Sci. IJCSS 2011, pp. 168–172, 2011, doi: 10.1109/IJCSS.2011.40.

Y. Syafarinda, F. Akhadin, Z. E. Fitri, Yogiswara, B. Widiawanl, and E. Rosdiana, “The Precision Agriculture Based on Wireless Sensor Network with MQTT Protocol,” IOP Conf. Ser. Earth Environ. Sci., vol. 207, no. 1, p. 012059, Nov. 2018, doi: 10.1088/1755-1315/207/1/012059.

G. Chiesa, D. Di Vita, A. Ghadirzadeh, A. H. Muñoz Herrera, and J. C. Leon Rodriguez, “A fuzzy-logic IoT lighting and shading control system for smart buildings,” Autom. Constr., vol. 120, p. 103397, Dec. 2020, doi: 10.1016/J.AUTCON.2020.103397.

R. K. Singh, R. Berkvens, and M. Weyn, “Energy Efficient Wireless Communication for IoT Enabled Greenhouses,” 2020 Int. Conf. Commun. Syst. NETworkS, COMSNETS 2020, pp. 885–887, Jan. 2020, doi: 10.1109/COMSNETS48256.2020.9027392.

N. Sahraei, S. Watson, S. Sofia, A. Pennes, T. Buonassisi, and I. M. Peters, “Persistent and adaptive power system for solar powered sensors of Internet of Things (IoT),” Energy Procedia, vol. 143, pp. 739–741, Dec. 2017, doi: 10.1016/J.EGYPRO.2017.12.755.

H. Gai, J. Beath, J. Fang, and H. H. Lou, “Alternative emission monitoring technologies and industrial internet of things–based process monitoring technologies for achieving operational excellence,” Curr. Opin. Green Sustain. Chem., vol. 23, pp. 31–37, Jun. 2020, doi: 10.1016/J.COGSC.2020.04.009.

Z. Ullah, F. Al-Turjman, L. Mostarda, and R. Gagliardi, “Applications of Artificial Intelligence and Machine learning in smart cities,” Comput. Commun., vol. 154, pp. 313–323, Mar. 2020, doi: 10.1016/J.COMCOM.2020.02.069.

A. Sagheer, M. Mohammed, K. Riad, and M. Alhajhoj, “A Cloud-Based IoT Platform for Precision Control of Soilless Greenhouse Cultivation,” Sensors 2021, Vol. 21, Page 223, vol. 21, no. 1, p. 223, Dec. 2020, doi: 10.3390/S21010223.

M. Raj et al., “A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0,” J. Netw. Comput. Appl., vol. 187, p. 103107, Aug. 2021, doi: 10.1016/J.JNCA.2021.103107.

A. Castañeda-Miranda and V. M. Castaño-Meneses, “Internet of things for smart farming and frost intelligent control in greenhouses,” Comput. Electron. Agric., vol. 176, p. 105614, Sep. 2020, doi: 10.1016/J.COMPAG.2020.105614.

K. Lova Raju and V. Vijayaraghavan, “IoT Technologies in Agricultural Environment: A Survey,” Wirel. Pers. Commun., vol. 113, no. 4, pp. 2415–2446, Aug. 2020, doi: 10.1007/S11277-020-07334-X/METRICS.

M. P. Caro, M. S. Ali, M. Vecchio, and R. Giaffreda, “Blockchain-based traceability in Agri-Food supply chain management: A practical implementation,” 2018 IoT Vert. Top. Summit Agric. - Tuscany, IOT Tuscany 2018, pp. 1–4, Jun. 2018, doi: 10.1109/IOT-TUSCANY.2018.8373021.

V. Kharchenko, V. Panchenko, P. V. Tikhonov, and P. Vasant, “Cogenerative PV thermal modules of different design for autonomous heat and electricity supply,” Handb. Res. Renew. Energy Electr. Resour. Sustain. Rural Dev., pp. 86–119, Jan. 2018, doi: 10.4018/978-1-5225-3867-7.CH004.

V. Panchenko, A. Izmailov, V. Kharchenko, and Y. Lobachevskiy, “Photovoltaic Solar Modules of Different Types and Designs for Energy Supply,”, vol. 9, no. 2, pp. 74–94, Jan. 1AD, doi: 10.4018/IJEOE.2020040106.

A. and Kate, “Global refining catalysts market (2021 to 2026) – Growth, trends, COVID-19 impact and forecasts,” Focus Catal., vol. 2021, no. 9, p. 2, Sep. 2021, doi: 10.1016/J.FOCAT.2021.08.004.

A. Sinha, G. Shrivastava, and P. Kumar, “Architecting user-centric internet of things for smart agriculture,” Sustain. Comput. Informatics Syst., vol. 23, pp. 88–102, Sep. 2019, doi: 10.1016/J.SUSCOM.2019.07.001.

J. Ruan et al., “Agriculture IoT: Emerging Trends, Cooperation Networks, and Outlook,” IEEE Wirel. Commun., vol. 26, no. 6, pp. 56–63, Dec. 2019, doi: 10.1109/MWC.001.1900096.

Y. L. Zhong, Z. Tian, G. P. Simon, and D. Li, “Scalable production of graphene via wet chemistry: progress and challenges,” Mater. Today, vol. 18, no. 2, pp. 73–78, Mar. 2015, doi: 10.1016/J.MATTOD.2014.08.019.

R. R. Shamshiri et al., “Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence,” Next-Generation Greenhouses Food Secur., May 2021, doi: 10.5772/INTECHOPEN.97714.

X. Wan, F. Zhang, Y. Liu, and J. Leng, “CNT-based electro-responsive shape memory functionalized 3D printed nanocomposites for liquid sensors,” Carbon N. Y., vol. 155, pp. 77–87, Dec. 2019, doi: 10.1016/J.CARBON.2019.08.047.

A. I. Mtz-Enriquez et al., “Tailoring the detection sensitivity of graphene based flexible smoke sensors by decorating with ceramic microparticles,” Sensors Actuators B Chem., vol. 305, p. 127466, Feb. 2020, doi: 10.1016/J.SNB.2019.127466.

J. Ruan et al., “An IoT-based E-business model of intelligent vegetable greenhouses and its key operations management issues,” Neural Comput. Appl., vol. 32, no. 19, pp. 15341–15356, Oct. 2020, doi: 10.1007/S00521-019-04123-X/METRICS.

J. Bontsema, E. J. Van Henten, T. H. Gieling, and G. L. A. M. Swinkels, “The effect of sensor errors on production and energy consumption in greenhouse horticulture,” Comput. Electron. Agric., vol. 79, no. 1, pp. 63–66, Oct. 2011, doi: 10.1016/J.COMPAG.2011.08.008.

M. Molinara, A. Bria, S. De Vito, and C. Marrocco, “Artificial intelligence for distributed smart systems,” Pattern Recognit. Lett., vol. 142, pp. 48–50, Feb. 2021, doi: 10.1016/J.PATREC.2020.12.006.

L. Bosmans et al., “Habitat-specific variation in gut microbial communities and pathogen prevalence in bumblebee queens (Bombus terrestris),” PLoS One, vol. 13, no. 10, p. e0204612, Oct. 2018, doi: 10.1371/JOURNAL.PONE.0204612.

G. L. Baron, V. A. A. Jansen, M. J. F. Brown, and N. E. Raine, “Pesticide reduces bumblebee colony initiation and increases probability of population extinction,” Nat. Ecol. Evol. 2017 19, vol. 1, no. 9, pp. 1308–1316, Aug. 2017, doi: 10.1038/s41559-017-0260-1.

H. Connelly, K. Poveda, and G. Loeb, “Landscape simplification decreases wild bee pollination services to strawberry,” Agric. Ecosyst. Environ., vol. 211, pp. 51–56, Dec. 2015, doi: 10.1016/J.AGEE.2015.05.004.

J. Bryden, R. J. Gill, R. A. A. Mitton, N. E. Raine, and V. A. A. Jansen, “Chronic sublethal stress causes bee colony failure,” Ecol. Lett., vol. 16, no. 12, pp. 1463–1469, Dec. 2013, doi: 10.1111/ELE.12188.

P. Dai et al., “The Herbicide Glyphosate Negatively Affects Midgut Bacterial Communities and Survival of Honey Bee during Larvae Reared in Vitro,” J. Agric. Food Chem., vol. 66, no. 29, pp. 7786–7793, Jul. 2018, doi: 10.1021/ACS.JAFC.8B02212/SUPPL_FILE/JF8B02212_SI_001.PDF.

F. Li et al., “Effects of phoxim exposure on gut microbial composition in the silkworm, Bombyx mori,” Ecotoxicol. Environ. Saf., vol. 189, p. 110011, Feb. 2020, doi: 10.1016/J.ECOENV.2019.110011.

V. J. McCracken, J. M. Simpson, R. I. Mackie, and H. R. Gaskins, “Molecular Ecological Analysis of Dietary and Antibiotic-Induced Alterations of the Mouse Intestinal Microbiota,” J. Nutr., vol. 131, no. 6, pp. 1862–1870, Jun. 2001, doi: 10.1093/JN/131.6.1862.

G. Kairo et al., “Drone exposure to the systemic insecticide Fipronil indirectly impairs queen reproductive potential,” Sci. Reports 2016 61, vol. 6, no. 1, pp. 1–12, Aug. 2016, doi: 10.1038/srep31904.

Y. S. Wang, Y. J. Huang, W. C. Chen, and J. H. Yen, “Effect of carbendazim and pencycuron on soil bacterial community,” J. Hazard. Mater., vol. 172, no. 1, pp. 84–91, Dec. 2009, doi: 10.1016/J.JHAZMAT.2009.06.142.

T. Lu et al., “Understanding the influence of glyphosate on the structure and function of freshwater microbial community in a microcosm,” Environ. Pollut., vol. 260, p. 114012, May 2020, doi: 10.1016/J.ENVPOL.2020.114012.

W. Skeff, C. Neumann, and D. E. Schulz-Bull, “Glyphosate and AMPA in the estuaries of the Baltic Sea method optimization and field study,” Mar. Pollut. Bull., vol. 100, no. 1, pp. 577–585, Nov. 2015, doi: 10.1016/J.MARPOLBUL.2015.08.015.

J. Praet, A. Parmentier, R. Schmid-Hempel, I. Meeus, G. Smagghe, and P. Vandamme, “Large-scale cultivation of the bumblebee gut microbiota reveals an underestimated bacterial species diversity capable of pathogen inhibition,” Environ. Microbiol., vol. 20, no. 1, pp. 214–227, Jan. 2018, doi: 10.1111/1462-2920.13973.

M. T. Bartling, A. Vilcinskas, and K. Z. Lee, “Sub-Lethal Doses of Clothianidin Inhibit the Conditioning and Biosensory Abilities of the Western Honeybee Apis mellifera,” Insects 2019, Vol. 10, Page 340, vol. 10, no. 10, p. 340, Oct. 2019, doi: 10.3390/INSECTS10100340.

D. Shin and C. T. Smartt, “Assessment of esterase gene expression as a risk marker for insecticide resistance in Florida Culex nigripalpus (Diptera: Culicidae),” J. Vector Ecol., vol. 41, no. 1, pp. 63–71, Jun. 2016, doi: 10.1111/JVEC.12195.




How to Cite

Qammar Shabbir Rana, & Ali Iqtadar Mirza. (2023). Green House Effect and Internet of Things: A Review. International Journal of Agriculture and Sustainable Development, 5(1), 23–31. Retrieved from