Intellectual Scenario-synergetic Control of the Humidity and Temperature Regime of the Greenhouse Facilities

Authors

  • Dmytro Polishchyk
  • Vitaliy Lysenko
  • Serhii Osadchiy
  • Natalia Zaiets

DOI:

https://doi.org/10.47839/ijc.21.3.2686

Keywords:

intelligent control system, biotechnological object, greenhouse, mathematical filter, neural network, synergetic control

Abstract

The article substantiates the management of the humidity and temperature regime of greenhouse complexes on the basis of a scenario-synergetic approach. The scenarios for controlling the temperature and humidity conditions in the greenhouse using the approach of fuzzy neural networks are formed. The structure of an automated control system for technological processes is developed, which provides automated collection and processing of information for the implementation of control actions in order to improve the efficiency of the greenhouse complex on the basis of a scenario-synergetic approach. The corresponding fuzzy neural networks are synthesized for a synergistic assessment of the interaction of technological parameters. Estimation of the root-mean-square error in the synthesis of fuzzy neural networks confirms the possibility of their use for the synergistic formation of scenarios for controlling the temperature and humidity regime in greenhouses to reveal the presence of a synergistic effect. Production rules for scenario management of temperature and humidity conditions are formed. It is shown that the use of fuzzy neural networks for the formation of scenarios for controlling the humidity and temperature regime provides the possibility of obtaining the appropriate scenarios for making managerial decisions and their prompt correction.

References

T. Boulard, S. Wang, “Greenhouse crop transpiration simulation from external climate condition,” Agricultural and Forest Meteorology, vol. 100, issue 1, pp. 25–34, 2000. https://doi.org/10.1016/S0168-1923(99)00082-9.

H. Challa, “Integration of explanatory and empirical crop models for greenhouse management support,” Proceedings of Models. Plant Growth, Acta Hort., vol. 507, pp. 107-115, 2007. https://doi.org/10.17660/ActaHortic.1999.507.12.

A. Kichah, P. E. Bournet, C. Migeon, T. Boulard, “Measurement and CFD simulation of microclimate characteristics and transpiration of an impatiens pot plant crop in a greenhouse,” Biosystems Engineering, vol. 112, issue 1, pp. 22–34, 2012. https://doi.org/10.1016/j.biosystemseng.2012.01.012.

D. Koshkin, “The dynamic model of the greenhouse environment control system,” Motrol, Lublin, tom 13A, pp. 189–195, 2011.

A. Chochowski, I. Bolbot, V. Lysenko, V. Reshetiuk, “The optimization of energy efficiency of mobile robots,” Annals of Warsaw University of Life Sciences – SGGW. Agricultural and Forest Engineering, no 70, pp. 79–88, 2017. https://doi.org/10.22630/AAFE.2017.70.20.

S. S. Mehta, T. F. Burks, W. E. Dixon, “Vision-based localization of a wheeled mobile robot for greenhouse applications: A daisy-chaining approach,” Computers and Electronics in Agriculture, vol. 63, issue 1, pp. 28-37, 2008. https://doi.org/10.1016/j.compag.2008.01.013.

A. O. Dudnyk, N. A. Zaiets, T. I. Landel, M. A. Hachkovska, I. Yu. Yakymenko, Development of Resource-efficient Modes of Growing Vegetable Products in Greenhouse Complexes: monograph, Kyiv: Princeeko, 2020, 277 p. (in Ukrainian)

S. G. Oparin, “Sinergy in integrated systems of risk management sinergy in integrated systems of risk management and its accounting in the digital economy and its accounting in the digital economy,” Issues of Risk Analysis, vol. 17, issue 6, pp. 50-61, 2020. (in Russian). https://doi.org/10.32686/1812-5220-2020-17-6-50-61.

A. V. Kovaleva, “Development models and strategic management of the company,” Audit and Financial Analysis, no. 1, pp. 1-7, 2008.

D. A. Novikov, A. A. Ivashchenko, Models and Methods of Organizational Management of Innovative Development of the Firm, Moscow: Combook, 2006, 336 p.

Systems Theory and System Analysis in the Management of Organizations: Tutorial, edited by V. N. Volkova, A. A. Emelyanova, Moscow: Finance and Statistics, 2006, 848 p. (in Russian)

V. Lysenko, B. Golovinskyi, V. Reshetiuk, V. Shcherbatyuk, V. Shtepa, “Energy-efficient modes for management of biotechnical objects based on natural disturbances prediction,” Annals of Warsaw University of Life Sciences – SGGW Agriculture, no. 65, pp. 111-118, 2015.

A. Gao, H. Chen, A. Hou, K. Xie, “Efficient antimicrobial silk composites using synergistic effects of violacein and silver nanoparticles,” Materials Science and Engineering: C, vol. 103, 109821, 2019. https://doi.org/10.1016/j.msec.2019.109821.

H. Vögeling, N. Plenagl, B. S. Seitz, L. Duse, S. R. Pinnapireddy, E. Dayyoub, et. al., “Synergistic effects of ultrasound and photodynamic therapy leading to biofilm eradication on polyurethane catheter surfaces modified with hypericin nanoformulations,” Materials Science and Engineering: C, vol. 103, 109749, 2019. https://doi.org/10.1016/j.msec.2019.109749.

G. Hossine, K. Katia, “Improvement of vector control of dual star induction drive using synergetic approach,” Proceedings of the 2017 14th IEEE International Multi-Conference on Systems, Signals & Devices (SSD), 2017, pp. 643-648. https://doi.org/10.1109/SSD.2017.8167006.

S. Prophet, J. Atman, G. F. Trommer, “A synergetic approach to indoor navigation and mapping for aerial reconnaissance and surveillance,” Proceedings of the 2017 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017, pp. 1-8. https://doi.org/10.1109/IPIN.2017.8115919.

K. Li, X. Qi, B. Wei, H. Huang, J. Wang, J. Zhang, “Prediction of transformer top oil temperature based on kernel extreme learning machine error prediction and correction,” Gaodianya Jishu/High Voltage Engineering, vol. 43, no. 12, pp. 4045–4053, 2017. http://doi.org/10.13336/j.1003-6520.hve.20171127032.

S. Prophet, J. Atman, G. F. Trommer, “A synergetic approach to indoor navigation and mapping for aerial reconnaissance and surveillance,” Proceedings of the 2017 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017, pp. 1-8. https://doi.org/10.1109/IPIN.2017.8115919.

Q. Gao, M. Zribi, M. Escorihuela, N. Baghdadi, “Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture map-ping at 100 m resolution,” Sensors, vol. 17, issue 9, 1966, 2017. https://doi.org/10.3390/s17091966.

X. Zhang, L. Che, M. Shahidehpour, A. S. Alabdulwahab, A. Abusorrah, “Reliability-based optimal planning of electricity and natural gas interconnections for multiple energy hubs,” IEEE Transactions on Smart Grid, vol. 8, issue 4, pp. 1658–1667, 2017. https://doi.org/10.1109/TSG.2015.2498166.

T. Takakura, “Research exploring greenhouse environment control over the last 50 years,” Mathematics and Computers in Simulation, vol. 167, pp. 19–31, 2020. https://doi.org/10.25165/j.ijabe.20191205.5179.

T. Blevins, W. K. Wojsznis, M. Nixon, Advanced Control Foundation: Tools, Techniques and Applications, International Society of Automation (ISA), Research Triangle Park, NC, 2013.

H. Liu, S. Fang, X. Guo, “Research and design of intelligent greenhouse control system based on AIoT fusion technology,” IOP Conf. Ser.: Earth Environ. Sci., vol. 474, 032036, 2020. https://doi.org/10.1088/1755-1315/474/3/032036.

J.-T. Ding, H.-Y. Yao, Z.-L. Zang, M. Huang, S.-J. Zhou, “Precise control and prediction of the greenhouse growth environment of Dendrobium candidum,” Comput. Electron. Agric., vol. 151, pp. 453–459, 2018. https://doi.org/10.1016/j.compag.2018.06.037.

A. Sachenko, V. Kochan, V. Turchenko, V. Tymchyshyn and N. Vasylkiv, “Intelligent nodes for distributed sensor network, Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (IMTC/99), 1999, pp. 1479-1484 vol. 3. https://doi.org/10.1109/IMTC.1999.776072.

O. Vdovichenko, A. Perepelitsyn, “Technologies for building systems of remote lining of communication lines: a practical example of implementation,” Radioelectronic and Computer Systems, no. 2, pp. 31-38, 2021. https://doi.org/10.32620/reks.2021.2.03.

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Published

2022-09-30

How to Cite

Polishchyk, D., Lysenko, V., Osadchiy, S., & Zaiets, N. (2022). Intellectual Scenario-synergetic Control of the Humidity and Temperature Regime of the Greenhouse Facilities. International Journal of Computing, 21(3), 311-317. https://doi.org/10.47839/ijc.21.3.2686

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