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Information technology for comprehensive monitoring and control of the microclimate in industrial greenhouses based on fuzzy logic

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Nowadays, applied computer-oriented and information digitalization technologies are developing very dynamically and are widely used in various industries. One of the highest priority sectors of the economy of Ukraine and other countries around the world, the needs of which require intensive implementation of high-performance information technologies, is agriculture. The purpose of the article is to synthesise scientific and practical provisions to improve the information technology of the comprehensive monitoring and control of microclimate in industrial greenhouses. The object of research is nonstationary processes of aggregation and transformation of measurement data on soil and climatic conditions of the greenhouse microclimate. The subject of research is methods and models of computer-oriented analysis of measurement data on the soil and climatic state of the greenhouse microclimate. The main scientific and practical effect of the article is the development of the theory of intelligent information technologies for monitoring and control of greenhouse microclimate through the development of methods and models of distributed aggregation and intellectualised transformation of measurement data based on fuzzy logic.
Słowa kluczowe
Rocznik
Strony
19--35
Opis fizyczny
Bibliogr. 28 poz., rys.
Twórcy
  • Department of Software of Computer Systems, Dnipro University of Technology, av. Dmytra Yavornytskoho, 19, Dnipro, UA49005, Ukraine
  • Department of Electronic Engineering, SHEI ’Donetsk National Technical University’ of the Ministry of Education and Science of Ukraine, Sofii Kovalevskoi st., 29, Lutsk, UA43024, Ukraine
  • Language Training Department, SHEI ’Donetsk National Technical University’ of the Ministry of Education and Science of Ukraine, Sofii Kovalevskoi st., 29, Lutsk, UA43024, Ukraine
Bibliografia
  • [1] FAOSTAT: Food and agriculture organization of the united nations. Available at: ttp://www.fao.org/faostat/en/#home [Accessed 25 May 2022].
  • [2] W. Baudoin, A. Nersisyan, A. Shamilov, A. Hodder, D. Gutierrez, Good Agricultural Practices for greenhouse vegetable production in the South East European countries, Food and Agriculture Organization of the United Nations, Rome 2017. URL: https://www.fao.org/documents/card/ru/c/22b737e1-488e-4993-86c9-13fd3fed122f/
  • [3] American Society of Agricultural and Biological Engineers: ANSI/ASAE EP406.4 JAN2003 (R2008) Heating, Ventilating and Cooling Greenhouses. Available at: http://materialstandard.com/wpcontent/uploads/2019/07/ANSI-ASABE-EP406-4-JAN2003-R2008.pdf [Accessed 15 May 2022].
  • [4] A. Kamilaris, A Review on the Application of Natural Computing in Environmental Informatics, In: 32nd EnviroInfo, Munchen, Germany, 2018, pp. 1–11. https://doi.org/10.48550/arXiv.1808.00260.
  • [5] M. Erazo-Rodas, M. Sandoval-Moreno, S. MunozRomero, M. Huerta, D. Rivas-Lalaleo, C. Naranjo, J. Rojo-Alvarez, Multiparametric Monitoring in Equatorian Tomato Greenhouses (I): Wireless Sensor Network Benchmarking, Sensors, 18 (8), 2018, pp. 1–22. https://doi.org/10.3390/s18082555.
  • [6] J. Miliauskaite, D. Kalibatiene, Complexity in Data-Driven Fuzzy Inference Systems: Survey, Classification and Perspective, Baltic J. Modern Computing, 8 (4), 2020, pp. 572–596. https://doi.org/10.22364/bjmc.2020.8.4.08.
  • [7] I. Laktionov, O. Vovna, A. Zori, Copncept of low cost computerized measuring system for microclimate parameters of greenhouses, Bulg. Journal of Agric. Sc., 23 (4), 2017, pp. 668–673. URL: https://agrojournal.org/23/04-24.pdf.
  • [8] O. Vovna, I. Laktionov, S. Sukach, M. Kabanets, E. Cherevko. Method of adaptive control of effective energy lighting of greenhouses in the visible optical range. Bulg. Journal of Agric. Sc., 24 (2), 2018, pp. 335–340. URL: https://agrojournal.org/24/02-23.pdf.
  • [9] I.S. Laktionov, O.V. Vovna, Y.O. Bashkov, A.A. Zori, A.A., V.A. Lebediev, Improved Computer-Oriented Method for Processing of Measurement Information on Greenhouse Microclimate, Int. J. Bioautomation, 23 (1), 2019, pp. 71–86. https://doi.org/10.7546/ijba.2019.23.1.71-86.
  • [10] I.S. Laktionov, O.V. Vovna, M.M. Kabanets, H.O. Sheina, I.A. Getman, Information model of the computer-integrated technology for wireless monitoring of the state of microclimate of industrial agricultural greenhouses, Instrumentation Mesure Metrologie, 20 (6), 2021, pp. 289 – 300. https://doi.org/10.18280/i2m.200601.
  • [11] J. Arshad, S. Saleem, M. Sana Ullah Badar, S. Khalid, Z. Mumtaz, S. Ullah, Z. Illyas, H. Ahmad Madni, An intelligent monitoring and controlling of greenhouse: Deployment of wireless sensor networks and internet-ofthings, Preprints MDPI, 2019, pp. 1–13.https://doi.org/10.20944/preprints201811.0215.v1.
  • [12] A. Touhami, B. Khelifa, L. Garcia, L. Parra, J. Lloret, B. Fateh, Sensor Netw ork Proposal for Greenhouse Automation placed at the South of Algeria, Network Protocols and Algorithms, 10 (4), 2018, pp. 53–69. https://doi.org/10.20944/10.5296/npa.v10i4.14155.
  • [13] S. Salvi, S.A. Pramod Jain, H.A. Sanjay, T.K. Harshita, M. Farhana, J. Naveen, M.V. Suhas, Cloud Based Data Analysis and Monitoring of Smart Multi-level Irrigation System Using IoT, In: 2017 International Conference on I-SMAC IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2017, pp. 752–757. https://doi.org/10.1109/I-SMAC.2017.8058279.
  • [14] F. Ouyang, H. Cheng, Y. Lan, Y. Zhang, X. Yin, J. Hu, X. Peng, G. Wang, S. Chen, Automatic delivery and recovery system of Wireless Sensor Networks (WSN) nodes based on UAV for agricultural applications, Computers and Electronics in Agriculture, 162, 2019, pp. 31–43. https://doi.org/10.1016/j.compag.2019.03.025.
  • [15] J.R. Llera, E.D. Goodman, E.S. Runkle, L. Xu, Improving greenhouse environmental control using crop-model-driven multiobjective optimization, In: Genetic and Evolutionary Computation Conference Companion (GECCO’ 18), Kyoto, Japan, 2018, pp. 292 –293. https://doi.org/10.1145/3205651.3205724.
  • [16] C.H. Guzman, J.L. Carrera, H.A. Duran, J. Berumen, A.A. Ortiz, O.A. Guirette, A. Arroyo, J.A. Brizuela, F. Gomez, A. Blanco, H.R. Azcaray, M. Hernandez, Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control, Sensors, 19 (1), 2018, pp. 1–13. https://doi.org/10.3390/s19010060.
  • [17] H. Wang, J.A. Sanchez-Molina, M. Li, F.R. Diaz, Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning, Water, 11 (1), 2019, pp. 1–19. https://doi.org/10.3390/w11010158.
  • [18] J. Agajo, J.G. Kolo, G. Jonas, A.R. Opeyemi, N.O. Chikeze, O.B. Chukwujekwu, A modified web-based agro-climatic remote monitoring system via wireless sensor network, In: 2017 IEEE 3rd Int. Conf. on ElectroTechnology for National Development (NIGERCON), Owerri, Nigeria, 2018, pp. 258–270. https://doi.org/10.1109/NIGERCON.2017.8281898.
  • [19] M. Azaza, K. Echaieb, E. Fabrizio, A. Iqbal, A. Mami, An intelligent system for the climate control and energy savings in agricultural greenhouses, Energy Efficiency, 9 (6), 2016, pp. 1241–1255. https://doi.org/10.1007/s12053-015-9421-8.
  • [20] Zh. Xu, J. Chen, Switching Control Strategy for Greenhouse Temperature-Humidity System Based on Prediction Modeling: A Simulation Study, Journal of Engineering and Technological Sciences, 49 (5), 2017, pp. 689–703.https://doi.org/10.20944/preprints201611.0044.v1.
  • [21] M. Taki, Y. Ajabshirchi, S. Faramarz Ranjbar, M. Matloobi, Application of neural networks and multiple regression models in greenhouse climate estimation, AgricEngInt: CIGR Journal, 18 (3), 2016, pp. 29–43. URL:https://cigrjournal.org/index.php/Ejounral/article/view/3672/2414
  • [22] Y. Kaneda, H. Ibayashi, N. Oishi, H. Mineno, Greenhouse Environmental Control System Based on SW-SVR, Procedia Computer Science, 60 (1), 2015, pp. 860–869.https://doi.org/10.1016/j.procs.2015.08.249.
  • [23] T.A. Izzuddin, M.A. Johari, M.Z.A. Rashid, M.H. Jali, Smart irrigation using fuzzy logic method, ARPN Journal of Engineering and Applied Sciences, 13 (2), 2018, pp. 517–522. URL: http://www.arpnjournals.org/jeas/research papers/rp 2018/jeas 0118 6698.pdf
  • [24] C. Algarin, J. Cabarcas, A. Llanos, LowCost Fuzzy Logic Control for Greenhouse Environments with Web Monitoring, Electronics, 6 (4), 2017, pp. 1–12. https://doi.org/10.3390/electronics6040071.
  • [25] R. Ben Ali, E. Aridhi, M. Abbes, A. Mami, Fuzzy logic controller of temperature and humidity inside an agricultural greenhouse, In: 7th International Renewable Energy Congress (IREC), Hammamet, Tunis, 2016, pp. 1–6. https://doi.org/10.1109/IREC.2016.7478929.
  • [26] O. Alpay, E. Erdem, The Control of Greenhouses Based on Fuzzy Logic Using Wireless Sensor Networks, Int. J. of Computational Intelligence Systems, 12 (1), 2019, pp. 190–203. https://doi.org/10.2991/ijcis.2018.125905641.
  • [27] A.J. Both, L. Benjamin, J. Franklin, G. Holroyd, L.D. Incoll, M.G. Lefsrud, G. Pitkin, Guidelines for measuring and reporting environmental parameters for experiments in greenhouses, Plant Methods, 11 (43), 2015, pp. 1–18. https://doi.org/10.1186/s13007-015-0083-5.
  • [28] W. Baudoin, Good agricultural practices for greenhouse vegetable crops: Principles for mediterranean climate areas, FAO of the United Nations, Rome 2013. URL: https://agris.fao.org/agrissearch/search.do?recordID=XF2013001549
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f0416051-3337-4d8a-b2f5-eeac6870a77a
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