Smart Greenhouse System Based on Internet of Things using Information Flow Diagram and MQTT Connectivity
Main Article Content
Abstract
Technological advancements have driven the rapidly changing digital era, making the Internet of Things (IoT) adaptable in several study fields. Efficiently designing and developing smart greenhouse systems may enhance precision agriculture by enabling automated and accurate operations. This study concentrates on the architectural design of a smart greenhouse monitoring system. It employs Message Queue Telemetry Transport (MQTT) as a protocol to facilitate data communication between devices within the IoT system over extensive distances. This protocol works in conjunction with applications that use the Information Flow Diagram (IFD) architecture for the user interface. Additionally, the protocol evaluates the efficiency of using the smart greenhouse system with a mobile application. By assessing farmers' acceptance of the proposed IoT system, 35 farmers explored the adoption of IoT technology for farming based on the Technology Acceptance Model (TAM) model. The evaluation of the results of the hypothesis test was analyzed using the critical ratio (CR) value, with a required limit of 1.96 at the 95% confidence level. We can conclude that all the hypotheses were acceptable.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All authors need to complete copyright transfer to Journal of Applied Informatics and Technology prior to publication. For more details click this link: https://ph01.tci-thaijo.org/index.php/jait/copyrightlicense
References
Ahmed, M., Rahaman, M. O., Rahman, M., & Kashem, M. A. (2019). Analyzing the quality of water and predicting the suitability for fish farming based on IoT in the context of Bangladesh. In International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1-5). IEEE. https://doi.org/10.1109/STI47673.2019.9068050
Alambaigi, A. & Ahangari, I. (2016). Technology acceptance model (TAM) as a predictor model for explaining agricultural experts behavior in acceptance of ICT. International Journal of Agricultural Management and Development (IJAMAD), 6(2), 235-247. https://doi.org/10.22004/ag.econ.262557
Bollen, K. A. (1989). Structural equations with latent variables. New York : John Wiley & Sons. https://doi.org/10.1002/9781118619179
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. http://www.jstor.org/stable/2632151
Davis, F. D. & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International journal of human-computer studies, 45(1), 19-45. https://doi.org/10.1006/ijhc.1996.0040
Diamantopoulos, A., Siguaw, J. A., & Siguaw, J. A. (2000). Introducing LISREL: A guide for the uninitiated. Cornell University, USA: Sage.
Dinculeană, D. & Cheng, X. (2019). Vulnerabilities and limitations of MQTT protocol used between IoT devices. Applied Sciences, 9(5), 848. https://doi.org/10.3390/app9050848
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of cleaner production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Imam, G. (2012). Aplikasi analisis multivariat dengan program IBM SPSS 20. Semarang : Badan Penerbit Universitas Diponegoro.
Jiang, G. & Yuan, K. H. (2017). Four new corrected statistics for SEM with small samples and nonnormally distributed data. Structural Equation Modeling, 24(4), 479–494. https://doi.org/10.1080/10705511.2016.1277726
Kaplan, D. (2009). Structural equation modeling: Foundations and extensions. Los Angeles: SAGE.
Khan, A. A., Faheem, M., Bashir, R. N., Wechtaisong, C., & Abbas, M. Z. (2022). Internet of things (IoT) assisted context aware fertilizer recommendation. IEEE. 10, 129505-129519. https://doi.org/10.1109/ACCESS.2022.3228160
Laghari, A. A., Wu, K., Laghari, R. A., Ali, M., & Khan, A. A. (2021). A review and state of art of internet of things (IoT). Archives of Computational Methods in Engineering, 1-19. https://doi.org/10.1007/s11831-021-09622-6
Madato, T., Petlamul, W., & Mahamad, K. (2022). Efficiency of smart farm system on enhancement of pepper production. Naresuan University Journal: Science and Technology (NUJST), 30(4), 53-65. https://doi.org/10.14456/nujst.2022.35
Mahmud, M. A., Buyamin, S., Mokji, M. M., & Abidin, M. Z. (2018). Internet of things based smart environmental monitoring for mushroom cultivation. Indonesian Journal of Electrical Engineering and Computer Science, 10(3), 847-852. http://doi.org/10.11591/ijeecs.v10.i3.pp847-852
Masriwilaga, A. A., Al-hadi, T. A. J. M., Subagja, A., & Septiana, S. (2019). Monitoring system for broiler chicken farms based on Internet of Things (IoT). Telekontran: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan, 7(1), 1-13.
Mukherji, S. V., Sinha, R., Basak, S., & Kar, S. P. (2019). Smart agriculture using internet of things and MQTT protocol. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 14-16). IEEE.
https://doi.org/10.1109/COMITCon.2019.8862233
Padalalu, P., Mahajan, S., Dabir, K., Mitkar, S., & Javale, D. (2017). Smart water dripping system for agriculture/farming. In 2017 International Conference for Convergence of Technology (I2CT) (pp. 659-662). IEEE. https://doi.org/10.1109/I2CT.2017.8226212
Patil, V. B.& Shah, A. B. (2019). Automated watering and irrigation system using Arduino UNO. International Journal of Innovative Science and Research Technology, 4(12), 928-932.
Promput, S., Maithomklang, S., & Panya-isara, C. (2023). Design and analysis performance of IoT-based water quality monitoring system using LoRa technology. TEM Journal, 12(1), 29-35. https://doi.org/10.18421/TEM121-04
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.
https://doi.org/10.1016/j.compedu.2019.103732
Rahimi, B., Nadri, H., Afshar, H. L., & Timpka, T. (2018). A systematic review of the technology acceptance model in health informatics. Applied clinical informatics, 9(03), 604-634. https://doi.org/10.1055/s-0038-1668091
Rhodes, D. L. (2012). The systems development life cycle (SDLC) as a standard: Beyond the documentation. In SAS Global Forum, (184), 1-5.
Rukhiran, M., Chomngern, T., & Netinant, P. (2023). Insights from a dataset on behavioral intentions in learning information flow diagram capability for software design. Data in Brief, 49, 109307. https://doi.org/10.1016/j.dib.2023.109307
Rukhiran, M., & Netinant, P. (2020a). Mobile application development of hydroponic smart farm using information flow diagram. In 2020-5th International Conference on Information Technology (InCIT) (pp. 150-155). IEEE. https://doi.org/10.1109/InCIT50588.2020.9310780
Rukhiran, M., & Netinant, P. (2020b). IoT architecture based on information flow diagram for vermiculture smart farming Kit. TEM Journal, 9(4), 1131-1137. https://doi.org/10.18421/TEM94‐03
Sofwan, A., Sumardi, S., Ahmada, A. I., Ibrahim, I., Budiraharjo, K., & Karno, K. (2020). Smart greetthings: Smart greenhouse based on internet of things for environmental engineering. In 2020 International Conference on Smart Technology and Applications (ICoSTA) (pp. 1-5). IEEE. https://doi.org/10.1109/ICoSTA48221.2020.1570614124
Terence, S. & Purushothaman, G. (2020). Systematic review of internet of things in smart farming. Transactions on Emerging Telecommunications Technologies, 31(6). https://doi.org/10.1002/ett.3958
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://ssrn.com/abstract=4062393
Yoon, H. W., Kim, D. J., Lee, M., Weon, C., & Smith, A. (2020, August). L & M farm: A smart farm based on LoRa & MQTT. In 2020 International Conference on Omni-layer Intelligent Systems (COINS) (pp. 1-6). IEEE.
https://doi.org/10.1109/COINS49042.2020.9191387
Zaguia, A. (2023). Smart greenhouse management system with cloud-based platform and IoT sensors. Spatial Information Research, 31(5), 559-571. https://doi.org/10.1007/s41324-023-00523-3