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2025 Vol.7, Issue 3

Review Article

ICT-based status monitoring and anomaly detection techniques of smart greenhouse components: A review
Md Aminur Rahman1
,
Md Nasim Reza12
,
Emanuel Bicamumakuba1
,
Samsuzzaman Samsuzzaman1
,
Hongbin Jin2
,
Sun-Ok Chung12*†
,
Soon Jung Hong3*†
1Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
2Department of Smart Agricultural Systems, Chungnam National University, Daejeon 34134, Republic of Korea
3Korea National College of Agriculture and Fisheries, Jeonju 54874, Republic of Korea
*Corresponding Author
†These authors equally contributed to this study as corresponding author.
30 September 2025. pp. 170-188
PDF XML Citation
Abstract

The advancement of Information and Communication Technologies (ICT) has transformed greenhouse agriculture by improving crop productivity, resource efficiency, and system sustainability. The objective of this review was to provide an overview of recent advancements in ICT-enabled environmental status monitoring and anomaly detection technologies implemented in smart greenhouse components. Key technologies include Internet of Thing (IoT), Wireless Sensor Network (WSN), Machine Learning (ML)-based predictive maintenance, digital twin, and performance management. Anomaly detection approaches are analyzed across domains such as sensor fault, communication failure, and cybersecurity threat. A comparative evaluation highlights the evolution from conventional threshold-based methods to advanced ML techniques, including supervised, unsupervised, deep learning, and Edge Artificial Intelligence (AI). ML-based systems report detection accuracies up to 97%, yet challenges remain regarding data integrity, computational overhead, scalability, and adversarial robustness. Future prospects emphasize AI-based self-healing mechanism, blockchain-integrated data pipeline, 6G-enabled communication, and hybrid anomaly detection models. The study concludes that resilient ICT architectures are essential for maintaining intelligent greenhouse functionality and enhancing food security under climate variability.

Keywords
Precision agriculture
Sensor network
IoT
Machine learning
Greenhouse automation
References
1

Abbood, H.M., Nouri, N.M., Riahi, M., Alagheband, S.H. 2023. An intelligent monitoring model for greenhouse microclimate based on RBF Neural Network for optimal setpoint detection. Journal of Process Control 129: 103037. https://doi.org/10.1016/j.jprocont.2023.103037

10.1016/j.jprocont.2023.103037
2

Abdelmoneim, A.A., Al Kalaany, C.M., Dragonetti, G., Derardja, B., Khadra, R. 2025. Comparative analysis of soil moisture-and weather-based irrigation scheduling for drip-irrigated lettuce using low-cost internet of things capacitive sensors. Sensors 25(5): 1568. https://doi.org/10.3390/s25051568

10.3390/s2505156840096482PMC11902337
3

Abdo-Peralta, P., García-Pumagualle, C., Carrera-Silva, K., Frey, C., Rosero-Erazo, C.R., Ortega-Castro, J., Orozco, J.S.S., Toulkeridis, T. 2024. Implementation of an enhanced edge computing system for the optimization of strawberry crop in greenhouses: a smart agriculture approach. Agronomy 14(12): 3030. https://doi.org/10.3390/agronomy14123030

10.3390/agronomy14123030
4

Abid, A., Cheikhrouhou, O., Zaïbi, G., Kachouri, A. 2024. Machine learning based outlier detection in IoT greenhouse. In: Proceedings of the IEEE 27th International Symposium on Real-Time Distributed Computing (ISORC). pp. 1-9. https://doi.org/10.1109/ISORC61049.2024.10551361

10.1109/ISORC61049.2024.10551361
5

Agyemang, E.F. 2024. Anomaly detection using unsupervised machine learning algorithms: A simulation study. Scientific African 26: e02386. https://doi.org/10.1016/j.sciaf.2024.e02386

10.1016/j.sciaf.2024.e02386
6

Ahmed, M., Naser Mahmood, A., Hu, J. 2016. A survey of network anomaly detection techniques. Journal of Network and Computer Applications 60: 19-31. https://doi.org/10.1016/j.jnca.2015.11.016

10.1016/j.jnca.2015.11.016
7

Allioui, H., Mourdi, Y. 2023. Exploring the full potential of IoT for better financial growth and stability: A comprehensive survey. Sensors 23(19): 8015. https://doi.org/10.3390/s23198015

10.3390/s2319801537836845PMC10574902
8

Al-Qudah, R., Almuhajri, M., Suen, C.Y. 2025. Unveiling the potential of sustainable agriculture: A comprehensive survey on the advancement of AI and sensory data for smart greenhouses. Computers and Electronics in Agriculture 229: 109721. https://doi.org/10.1016/j.compag.2024.109721

10.1016/j.compag.2024.109721
9

Angelopoulos, A., Michailidis, E.T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., Zahariadis, T. 2019. Tackling faults in the industry 4.0 era survey of machine-learning solutions and key aspects. Sensors 20(1): 109. https://doi.org/10.3390/s20010109

10.3390/s2001010931878065PMC6983262
10

Apostolakis, A., Wagner, K., Daliakopoulos, I.N., Kourgialas, N.N., Tsanis, I.K. 2016. Greenhouse soil moisture deficit under saline irrigation and climate change. Procedia Engineering 162: 537-544. https://doi.org/10.1016/j.proeng.2016.11.098

10.1016/j.proeng.2016.11.098
11

Assimakopoulos, F., Vassilakis, C., Margaris, D., Kotis, K., Spiliotopoulos, D. 2025. AI and related technologies in the fields of smart agriculture: A review. Information 16(2): 100. https://doi.org/10.3390/info16020100

10.3390/info16020100
12

Astillo, P.V., Kim, J., Sharma, V., You, I. 2020. SGF-MD: Behavior rule specification-based distributed misbehavior detection of embedded IoT devices in a closed-loop smart greenhouse farming system. IEEE Access 8: 196235-196252. https://doi.org/10.1109/ACCESS.2020.3034096

10.1109/ACCESS.2020.3034096
13

Ayuningsih, E., Suryono, S., Gunawan, V. 2019. Fuzzy rule-based systems for controlling plant growth parameters in greenhouses using fog networks. In: Proceedings of the Fourth International Conference on Informatics and Computing (ICIC). pp. 1-6. https://doi.org/10.1109/ICIC47613.2019.8985857

10.1109/ICIC47613.2019.8985857
14

Barbosa, G., Gadelha, F., Kublik, N., Proctor, A., Reichelm, L., Weissinger, E., Wohlleb, G., Halden, R. 2015. Comparison of land, water, and energy requirements of lettuce grown using hydroponic vs. conventional agricultural methods. International Journal of Environmental Research and Public Health 12(6): 6879-6891. https://doi.org/10.3390/ijerph120606879

10.3390/ijerph12060687926086708PMC4483736
15

Barkhi, M., Pourhossein, J., Hosseini, S.A. 2024. Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty. Scientific Reports 14(1): 28466. https://doi.org/10.1038/s41598-024-77982-7

10.1038/s41598-024-77982-739557975PMC11574044
16

Barreca, F. 2024. Sustainability in food production: A high-efficiency offshore greenhouse. Agronomy 14(3): 518. https://doi.org/10.3390/agronomy14030518

10.3390/agronomy14030518
17

Barreto, L., Amaral, A. 2018. Smart farming: cybersecurity challenges. In: Proceedings of the International Conference on Intelligent Systems (IS). pp. 870-876. https://doi.org/10.1109/IS.2018.8710531

10.1109/IS.2018.8710531
18

Benameur, R., Dahane, A., Kechar, B., Benyamina, A.E.H. 2024. An innovative, smart, and sustainable low-cost irrigation system for anomaly detection using deep learning. Sensors 24(4): 1162 https://doi.org/10.3390/s24041162

10.3390/s2404116238400320PMC10892454
19

Bersani, C., Ruggiero, C., Sacile, R., Soussi, A., Zero, E. 2022. Internet of Things approaches for monitoring and control of smart greenhouses in Industry 4.0. Energies 15(10): 3834. https://doi.org/10.3390/en15103834

10.3390/en15103834
20

Bhaskaran, H.S., Gordon, M., Neethirajan, S. 2024. Development of a cloud-based IoT system for livestock health monitoring using AWS and Python. Smart Agricultural Technology 9: 100524. https://doi.org/10.1016/j.atech.2024.100524

10.1016/j.atech.2024.100524
21

Bhujel, A., Basak, J.K., Khan, F., Arulmozhi, E., Jaihuni, M., Sihalath, T., Lee, D., Park, J., Kim, H.T. 2020. Sensor systems for greenhouse microclimate monitoring and control: A review. Journal of Biosystems Engineering 45(4): 341-361. https://doi.org/10.1007/s42853-020-00075-6

10.1007/s42853-020-00075-6
22

Bicamumakuba, E., Habineza, E., Reza, M.N. and Chung, S.O., 2025. IoT-enabled LoRaWAN gateway for monitoring and predicting spatial environmental parameters in smart greenhouses: A review. Precision Agriculture Science and Technology 7(1): 28-46.

10.22765/PASTJ.20250003
23

Cafuta, D., Dodig, I., Cesar, I., Kramberger, T. 2021. Developing a modern greenhouse scientific research facility. A case study. Sensors 21(8): 2575. https://doi.org/10.3390/s21082575

10.3390/s2108257533916901PMC8067565
24

Canatan, M., Muñoz-Carpena, R., Boz, Z. 2025. Continuous surface temperature monitoring of refrigerated fresh produce through visible and thermal infrared sensor fusion. Postharvest Biology and Technology 222: 113354. https://doi.org/10.1016/j.postharvbio.2024.113354

10.1016/j.postharvbio.2024.113354
25

Cao, X., Yao, Y., Li, L., Zhang, W., An, Z., Zhang, Z., Xiao, L., Guo, S., Cao, X., Wu, M., Luo, D. 2022. igrow: A smart agriculture solution to autonomous greenhouse control. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 11837-11845. https://doi.org/10.1609/aaai.v36i11.21440

10.1609/aaai.v36i11.21440
26

Cheng, W., Ma, T., Wang, X., Wang, G. 2022. Anomaly detection for internet of things time series data using generative adversarial networks with attention mechanism in smart agriculture. Frontiers in Plant Science 13: 890563. https://doi.org/10.3389/fpls.2022.890563

10.3389/fpls.2022.89056335734254PMC9207449
27

Chicaiza, K., Paredes, R.X., Sarzosa, I.M., Yoo, S.G., Zang, N. 2024. Smart farming technologies: A methodological overview and analysis. IEEE Access 12: 164922-164941. https://doi.org/10.1109/ACCESS.2024.3487497

10.1109/ACCESS.2024.3487497
28

Devarajan, Y. 2025. Investigation of Emerging Technologies in Agriculture: An In-depth Look at Smart Farming, Nano-agriculture, AI, and Big Data. J. Biosyst. Eng. 50, 170-192. https://doi.org/10.1007/s42853-025-00258-z

10.1007/s42853-025-00258-z
29

Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., Kaliaperumal, R. 2022. Smart farming: Internet of things (IoT)-based sustainable agriculture. Agriculture 12(10): 1745. https://doi.org/10.3390/agriculture12101745

10.3390/agriculture12101745
30

Dhawas, P., Dhore, A., Bhagat, D., Pawar, R.D., Kukade, A., Kalbande, K. 2024. Big data preprocessing, techniques, integration, transformation, normalisation, cleaning, discretization, and binning. In Big Data Analytics Techniques for Market Intelligence. pp. 159-182. IGI Global Scientific Publishing, New York. https://doi.org/10.4018/979-8-3693-0413-6.ch006

10.4018/979-8-3693-0413-6.ch006
31

Domínguez-Niño, J.M., Oliver-Manera, J., Arbat, G., Girona, J., Casadesús, J. 2020. Analysis of the variability in soil moisture measurements by capacitance sensors in a drip-irrigated orchard. Sensors 20(18): 5100. https://doi.org/10.3390/s20185100

10.3390/s2018510032906820PMC7570759
32

Elshenawy, L.M., Halawa, M.A., Mahmoud, T.A., Awad, H.A., Abdo, M.I. 2021. Unsupervised machine learning techniques for fault detection and diagnosis in nuclear power plants. Progress in Nuclear Energy 142: 103990. https://doi.org/10.1016/j.pnucene.2021.103990

10.1016/j.pnucene.2021.103990
33

Farooq, M.S., Riaz, S., Helou, M.A., Khan, F.S., Abid, A., Alvi, A. 2022. Internet of things in greenhouse agriculture: A survey on enabling technologies, applications, and protocols. IEEE Access 10: 53374-53397. https://doi.org/10.1109/ACCESS.2022.3166634

10.1109/ACCESS.2022.3166634
34

Folorunso, T.A., Oshiga, O., Bala, J.A., Ojogunwa, D.A. 2024. Development of an internet of things based smart greenhouse. International Journal of Computer Trends and Technology 72(5): 96-101. https://doi.org/10.14445/22312803/IJCTT-V72I5P112

10.14445/22312803/IJCTT-V72I5P112
35

Gao, X., Li, S., He, Y., Yang, Y., Tian, Y. 2024. Spectrum imaging for phenotypic detection of greenhouse vegetables: A review. Computers and Electronics in Agriculture 225: 109346. https://doi.org/10.1016/j.compag.2024.109346

10.1016/j.compag.2024.109346
36

Geng, X., Zhang, Q., Wei, Q., Zhang, T., Cai, Y., Liang, Y., Sun, X. 2019. A mobile greenhouse environment monitoring system based on the internet of things. IEEE access 7: 135832-135844. https://doi.org/10.1109/ACCESS.2019.2941521

10.1109/ACCESS.2019.2941521
37

Guan, S., Fang, Q., Guan, T. 2021. Application of a novel PNN evaluation algorithm to a greenhouse monitoring system. IEEE Transactions on Instrumentation and Measurement 70: 1-12. https://doi.org/10.1109/TIM.2021.3079558

10.1109/TIM.2021.3079558
38

Guesbaya, M., García-Mañas, F., Rodríguez, F., Megherbi, H. 2023. A soft sensor to estimate the opening of greenhouse vents based on an LSTM-RNN neural network. Sensors 23(3): 1250. https://doi.org/10.3390/s23031250

10.3390/s2303125036772289PMC9921858
39

Guo, J., Zhang, B., Lin, L., Xu, Y., Zhou, P., Luo, S., Zhuo, Y., Ji, J., Luo, Z. Hassan, S.G. 2024. Multi-model fusion method for predicting CO2 concentration in greenhouse tomatoes. Computers and Electronics in Agriculture 227: 109623. https://doi.org/10.1016/j.compag.2024.109623

10.1016/j.compag.2024.109623
40

Guo, Z., Feng, L. 2024. Multi-step prediction of greenhouse temperature and humidity based on temporal position attention LSTM. Stochastic Environmental Research and Risk Assessment 38(12): 4907-4934. https://doi.org/10.1007/s00477-024-02840-x

10.1007/s00477-024-02840-x
41

Hamed, S., Ibba, P., Petrelli, M., Ciocca, M., Lugli, P., Petti, L. 2021. Transistor-based plant sensors for agriculture 4.0 measurements. In: Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). pp. 69-74. https://doi.org/10.1109/MetroAgriFor52389.2021.9628560

10.1109/MetroAgriFor52389.2021.9628560
42

Helmy, H.S., Abuarab, M.E., Abdeldaym, E.A., Abdelaziz, S.M., Abdelbaset, M.M., Dewedar, O.M., Molina-Martinez, J.M., El-Shafie, A.F. and Mokhtar, A. 2024. Field-grown lettuce production optimized through precision irrigation water management using soil moisture-based capacitance sensors and biodegradable soil mulching. Irrigation Science 2024: 1-26. https://doi.org/10.1007/s00271-024-00969-9

10.1007/s00271-024-00969-9
43

Henderson, S., Gholami, D., Zheng, Y. 2018. Soil moisture sensor-based systems are suitable for monitoring and controlling irrigation of greenhouse crops. HortScience 53(4): 552-559. https://doi.org/10.21273/HORTSCI12676-17

10.21273/HORTSCI12676-17
44

Hojabri, M., Kellerhals, S., Upadhyay, G., Bowler, B. 2022. IoT-based PV array fault detection and classification using embedded supervised learning methods. Energies 15(6): 2097. https://doi.org/10.3390/en15062097

10.3390/en15062097
45

Hoque, M.J., Ahmed, M.R., Hannan, S. 2020. Automated greenhouse monitoring and controlling system using sensors and solar power. European Journal of Engineering and Technology Research 5(4): 510-515. https://doi.org/10.24018/ejeng.2020.5.4.1887

10.24018/ejeng.2020.5.4.1887
46

Hosny, K.M., El-Hady, W.M., Samy, F.M. 2025. Technologies, protocols, and applications of internet of things in greenhouse farming: A survey of recent advances. Information Processing in Agriculture 12(1): 91-111. https://doi.org/10.1016/j.inpa.2024.04.002

10.1016/j.inpa.2024.04.002
47

Huynh, H.X., Tran, L.N., Duong-Trung, N. 2023. Smart greenhouse construction and irrigation control system for optimal Brassica Juncea development. PLOS ONE 18(10): e0292971. https://doi.org/10.1371/journal.pone.0292971

10.1371/journal.pone.029297137883345PMC10602328
48

Ibrahim, H., Mostafa, N., Halawa, H., Elsalamouny, M., Daoud, R., Amer, H., Adel, Y., Shaarawi, A., Khattab, A., ElSayed, H. 2019. A layered IoT architecture for greenhouse monitoring and remote control. SN Applied Sciences 1(3): 223. https://doi.org/10.1007/s42452-019-0227-8

10.1007/s42452-019-0227-8
49

Islam, S., Reza, M.N., Ahmed, S., Samsuzzaman, Lee, K.H., Cho, Y.J., Noh, D.H. Chung, S.O. 2024. Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8): 1390. https://doi.org/10.3390/agriculture14081390

10.3390/agriculture14081390
50

Joaquim, M.M., Kamble, A.W., Misra, S., Badejo, J., Agrawal, A. 2022. IoT and machine learning based anomaly detection in WSN for a smart greenhouse. In: Proceedings of the Data, Engineering and Applications: Select Proceedings of IDEA. pp. 421-431. Singapore. https://doi.org/10.1007/978-981-19-4687-5_32

10.1007/978-981-19-4687-5_32
51

Jouini, O., Sethom, K., Namoun, A., Aljohani, N., Alanazi, M.H., Alanazi, M.N. 2024. A survey of machine learning in edge computing: Techniques, frameworks, applications, issues, and research directions. Technologies 12(6): 81. https://doi.org/10.3390/technologies12060081

10.3390/technologies12060081
52

Juneidi, S.J. 2022. Smart greenhouses using internet of things: Case study on tomatoes. International Journal on Smart Sensing and Intelligent Systems 15(1): 1-15. https://doi.org/10.2478/ijssis-2022-0019

10.2478/ijssis-2022-0019
53

Kabir, M.S.N., Reza, M.N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M.R., Lee, K.Y. Chung, S.O. 2023. Technological trends and engineering issues on vertical farms: a review. Horticulturae, 9(11): 1229. https://doi.org/10.3390/horticulturae9111229

10.3390/horticulturae9111229
54

Khan, S.Z., Le Moullec, Y., Alam, M.M. 2021. An NB-IoT-based edge-of-things framework for energy-efficient image transfer. Sensors 21(17): 5929. https://doi.org/10.3390/s21175929

10.3390/s2117592934502818PMC8434658
55

Kim, T.H., Lee, K.Y., Ali, M.R., Reza, M.N., Chung, S.O. Kang, N.R. 2023. PID Control for Greenhouse Climate Regulation: A Review. Precision Agriculture 5(2): 94. https://doi.org/10.12972/pastj.20230008

10.12972/pastj.20230008
56

Kondaveeti, H.K., Sai, G.B., Athar, S.A., Vatsavayi, V.K., Mitra, A., Ananthachari, P. 2024. Federated learning for smart agriculture: Challenges and opportunities. In: Proceedings of the 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). pp. 1-7. https://doi.org/10.1109/ICDCECE60827.2024.10548604

10.1109/ICDCECE60827.2024.10548604
57

Lalhriatpuii, R., Wasson, V. 2024. Comprehensive exploration of IoT communication protocol: CoAP, MQTT, HTTP, LoRaWAN and AMQP. In: Proceedings of the International Conference on Machine Learning Algorithms. pp. 261-274. https://doi.org/10.1007/978-3-031-75861-4_23

10.1007/978-3-031-75861-4_23
58

Lee, T.Y., Reza, M.N., Chung, S.O., Kim, D.U., Lee, S.Y. Choi, D.H. 2023. Application of fuzzy logics for smart agriculture: A review. Precis. Agric 5(1): 1. https://doi.org/10.12972/pastj.20230001

10.12972/pastj.20230001
59

Li, X., Zhang, L., Wang, X., Liang, B. 2024. Forecasting greenhouse air and soil temperatures: A multi-step time series approach employing attention-based LSTM network. Computers and Electronics in Agriculture 217: 108602. https://doi.org/10.1016/j.compag.2023.108602

10.1016/j.compag.2023.108602
60

Lim, J.W., Reza, M.N., Chung, S.O., Lee, K.Y., Lee, S.Y., Lee, K.N., Lee, B. 2023. Application of artificial neural network in smart protected horticulture: A review. Precision Agriculture 5(1): 29-41. https://doi.org/10.12972/pastj.20230003

10.12972/pastj.20230003
61

Lu, Y., Liu, M., Li, C., Liu, X., Cao, C., Li, X., Kan, Z. 2022. Precision fertilization and irrigation: Progress and applications. AgriEngineering 4(3): 626-655. https://doi.org/10.3390/agriengineering4030041

10.3390/agriengineering4030041
62

Maraveas, C. 2022. Incorporating artificial intelligence technology in smart greenhouses: Current state of the Art. Applied Sciences 13(1): 14. https://doi.org/10.3390/app13010014

10.3390/app13010014
63

Maraveas, C., Bartzanas, T. 2021. Application of internet of things (IoT) for optimized greenhouse environments. AgriEngineering 3(4): 954-970. https://doi.org/10.3390/agriengineering3040060

10.3390/agriengineering3040060
64

Marino, G., Scalisi, A., Guzmán-Delgado, P., Caruso, T., Marra, F.P., Lo Bianco, R. 2021. Detecting mild water stress in olive with multiple plant-based continuous sensors. Plants 10(1): 131. https://doi.org/10.3390/plants10010131

10.3390/plants1001013133440632PMC7827840
65

Maroli, A., Narwane, V.S., Gardas, B.B. 2021. Applications of IoT for achieving sustainability in agricultural sector: A comprehensive review. Journal of Environmental Management 298: 113488. https://doi.org/10.1016/j.jenvman.2021.113488

10.1016/j.jenvman.2021.113488
66

Millán, S., Montesinos, C, Campillo, C. 2024. Evaluation of different commercial sensors for the development of their automatic irrigation system. Sensors 24(23): 7468. https://doi.org/10.3390/s24237468

10.3390/s2423746839686006PMC11644087
67

Misra, N.N., Dixit, Y., Al-Mallahi, A., Bhullar, M.S., Upadhyay, R., Martynenko, A. 2022. IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal 9(9): 6305-6324. https://doi.org/10.1109/JIOT.2020.2998584

10.1109/JIOT.2020.2998584
68

Moore, A.D., Holzworth, D.P., Herrmann, N.I., Brown, H.E., de Voil, P.G., Snow, V.O., Zurcher, E.J. Huth, N.I. 2014. Modelling the manager: Representing rule-based management in farming systems simulation models. Environmental Modelling & Software 62: 399-410. https://doi.org/10.1016/j.envsoft.2014.09.001

10.1016/j.envsoft.2014.09.001
69

Morales-García, J., Padilla-Quimbiulco, D., Cantabella, M., Ayuso, B., Muñoz, A., Cecilia, J.M. 2024. GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management. Journal of Ambient Intelligence and Smart Environments 16(3): 389-405. https://doi.org/10.3233/AIS-230359

10.3233/AIS-230359
70

Oguntosin, V., Okeke, C., Adetiba, E., Abdulkareem, A., Olowoleni, J. 2023. IoT-based greenhouse monitoring and control system. International Journal of Computing and Digital Systems 14(1): 469-483. https://doi.org/10.12785/ijcds/140137

10.12785/ijcds/140137
71

Oh, H., Chae, K. 2008. Real-time intrusion detection system based on self-organized maps and feature correlations. In: Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology. pp. 1154-1158. https://doi.org/10.1109/ICCIT.2008.362

10.1109/ICCIT.2008.362
72

Okasha, A.M., Ibrahim, H.G., Elmetwalli, A.H., Khedher, K.M., Yaseen, Z.M., Elsayed, S. 2021. Designing low-cost capacitive-based soil moisture sensor and smart monitoring unit operated by solar cells for greenhouse irrigation management. Sensors 21(16): 5387. https://doi.org/10.3390/s21165387

10.3390/s2116538734450826PMC8399650
73

Panaligan, N.A.P., Aringo, M.Q., Ella, V.B. 2022. Assessment of potential for adoption of wireless sensor network technology for irrigation water management of high value crops in the Philippines. IOP Conference Series: Earth and Environmental Science 1038(1): 012027. https://doi.org/10.1088/1755-1315/1038/1/012027

10.1088/1755-1315/1038/1/012027
74

Placidi, P., Morbidelli, R., Fortunati, D., Papini, N., Gobbi, F., Scorzoni, A. 2021. Monitoring soil and ambient parameters in the IoT precision agriculture scenario: An original modeling approach dedicated to low-cost soil water content sensors. Sensors 21(15): 5110. https://doi.org/10.3390/s21155110

10.3390/s2115511034372355PMC8348011
75

Puder, A., Zink, M., Seidel, L., Sax, E. 2024. Hybrid anomaly detection in time series by combining kalman filters and machine learning models. Sensors 24(9): 2895. https://doi.org/10.3390/s24092895

10.3390/s2409289538733000PMC11086117
76

Quan, V.M., Gupta, G.S., Mukhopadhyay, S. 2011. Review of sensors for greenhouse climate monitoring. In: Proceedings of the 2011 IEEE Sensors Applications Symposium. pp. 112-118. https://doi.org/10.1109/SAS.2011.5739816

10.1109/SAS.2011.5739816
77

Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Randazzo, G., Muzirafuti, A. 2022. IoT-enabled smart agriculture: architecture, applications, and challenges. Applied Sciences 12(7): 3396. https://doi.org/10.3390/app12073396

10.3390/app12073396
78

Raes, L., Celikkol, B., Schreer, O., De Winter, J., Baltazar, J., McAleer, S.R., Bertocci, D. 2025. Connected services: IoT data to fuel your local digital twin. In: Proceedings of the Decide Better: Open and Interoperable Local Digital Twins. pp. 169-202. https://doi.org/10.1007/978-3-031-81451-8_7

10.1007/978-3-031-81451-8_7
79

Rajeswari, S., Suthendran, K., Rajakumar, K. 2017. A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics. In: Proceedings of the 2017 International Conference on Intelligent Computing and Control (I2C2) pp. 1-5. https://doi.org/10.1109/I2C2.2017.8321902

10.1109/I2C2.2017.8321902
80

Rayhana, R., Xiao, G., Liu, Z. 2020. Internet of things empowered smart greenhouse farming. IEEE journal of radio frequency identification 4(3): 195-211. https://doi.org/10.1109/JRFID.2020.2984391

10.1109/JRFID.2020.2984391
81

Reka, S.S., Chezian, B.K., Chandra, S.S. 2019. A novel approach of IoT-based smart greenhouse farming system. In: Proceedings of the 2018 Green Buildings and Sustainable Engineering (GBSE). pp. 227-235. https://doi.org/10.1007/978-981-13-1202-1_20

10.1007/978-981-13-1202-1_20
82

Reza, M.N., Chowdhury, M., Islam, S., Kabir, M.S.N., Park, S.U., Lee, G.J., Cho, J., Chung, S.O. 2023. Leaf area prediction of pennywort plants grown in a plant factory using image processing and an artificial neural network. Horticulturae, 9(12): 1346. https://doi.org/10.3390/horticulturae9121346

10.3390/horticulturae9121346
83

Rodríguez, M., Tobón, D.P., Múnera, D. 2023. Anomaly classification in industrial Internet of things: A review. Intelligent Systems with Applications 18: 200232. https://doi.org/10.1016/j.iswa.2023.200232

10.1016/j.iswa.2023.200232
84

Rosero-Montalvo, P.D., István, Z., Tözün, P., Hernandez, W. 2023. Hybrid anomaly detection model on trusted IoT devices. IEEE Internet of Things Journal 10(12): 10959-10969. https://doi.org/10.1109/JIOT.2023.3243037

10.1109/JIOT.2023.3243037
85

Shekarian, S.M., Aminian, M., Mohammad Fallah, A., Akbary Moghaddam, V. 2024. AI-powered sensor fault detection for cost-effective smart greenhouses. Computers and Electronics in Agriculture 224: 109198. https://doi.org/10.1016/j.compag.2024.109198

10.1016/j.compag.2024.109198
86

Sinha, P., Sahu, D., Prakash, S., Yang, T., Rathore, R.S., Pandey, V.K. 2025. A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning. Scientific Reports 15(1): 9684. https://doi.org/10.1038/s41598-025-94500-5

10.1038/s41598-025-94500-540114016PMC11926101
87

Syarif, I., Prugel-Bennett, A., Wills, G. 2012. Unsupervised clustering approach for network anomaly detection. In: Proceedings of the 4th International Conference in Networked Digital Technologies. pp. 135-145. https://doi.org/10.1007/978-3-642-30507-8_13

10.1007/978-3-642-30507-8_13
88

Tien, J.M. 2017. Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science 4(2): 149-178. https://doi.org/10.1007/s40745-017-0112-5

10.1007/s40745-017-0112-5
89

Tomkiewicz, D., Piskier, T. 2012. A plant based sensing method for nutrition stress monitoring. Precision agriculture 13: 370-383. https://doi.org/10.1007/s11119-011-9252-3

10.1007/s11119-011-9252-3
90

Ullah, I., Fayaz, M., Aman, M., Kim, D. 2022. An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption. Computing 104(2): 433-457. https://doi.org/10.1007/s00607-021-00963-5

10.1007/s00607-021-00963-5
91

Vatistas, C., Avgoustaki, D.D., Bartzanas, T. 2022. A systematic literature review on controlled-environment agriculture: How vertical farms and greenhouses can influence the sustainability and footprint of urban microclimate with local food production. Atmosphere 13(8): 1258. https://doi.org/10.3390/atmos13081258

10.3390/atmos13081258
92

Yang, Y., Ding, S., Liu, Y., Meng, S., Chi, X., Ma, R., Yan, C. 2022. Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse. Digital Communications and Networks 8(4): 498-507. https://doi.org/10.1016/j.dcan.2021.11.004

10.1016/j.dcan.2021.11.004
93

Yin, H., Cao, Y., Marelli, B., Zeng, X., Mason, A.J., Cao, C. 2021. Soil sensors and plant wearables for smart and precision agriculture. Advanced Materials 33(20): 2007764. https://doi.org/10.1002/adma.202007764

10.1002/adma.202007764
94

Zhang, C., Kong, J., Wu, D., Guan, Z., Ding, B., Chen, F. 2023. Wearable sensor: An emerging data collection tool for plant phenotyping. Plant Phenomics 5: 0051. https://doi.org/10.34133/plantphenomics.0051

10.34133/plantphenomics.005137408737PMC10318905
95

Zhang, M., Yan, T., Wang, W., Jia, X., Wang, J., Klemeš, J.J. 2022. Energy-saving design and control strategy towards modern sustainable greenhouse: A review. Renewable and Sustainable Energy Reviews 164: 112602. https://doi.org/10.1016/j.rser.2022.112602

10.1016/j.rser.2022.112602
96

Zhou, Y.P., Fang, J.A. 2009. Intrusion detection model based on hierarchical fuzzy inference system. In: Proceedings of the 2009 Second International Conference on Information and Computing Science. pp. 144-147. https://doi.org/10.1109/ICIC.2009.145

10.1109/ICIC.2009.145
97

Zou, X., Liu, W., Huo, Z., Wang, S., Chen, Z., Xin, C., Bai, Y., Liang, Z., Gong, Y., Qian, Y., Shu, L. 2023. Current status and prospects of research on sensor fault diagnosis of agricultural internet of things. Sensors 23(5): 2528. https://doi.org/10.3390/s23052528

10.3390/s2305252836904732PMC10007498
Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 7
  • No :3
  • Pages :170-188
  • Received Date : 2025-06-20
  • Revised Date : 2025-07-28
  • Accepted Date : 2025-07-30
  • DOI :https://doi.org/10.22765/pastj.20250013
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