A hydroponic greenhouse fuzzy control system: design, development and optimization using the genetic algorithm
Abstract
Aim of study: The design and development of a hydroponic greenhouse fuzzy control system.
Area of study: The evaluation was performed using experimental data obtained from the literature. The construction and evaluation of the fuzzy control hydroponic greenhouse system was carried out in a greenhouse in Tehran, Iran.
Material and methods: The greenhouse environmental conditions, including temperature, humidity, and carbon dioxide, were controlled. The design of a fuzzy controller begun with the selection of linguistic variables, process status, and input and output variables. The fuzzy control system consisted of three modules: 1) fuzzy module, 2) cost function, and 3) genetic algorithm for the adjustment of the greenhouse environmental conditions.The next step was to select a set of linguistic rules and the type of fuzzy inference process. The rules were set once, and the fuzzy set and output value needed to be specified after the inference, along with the development of a non-fuzzy strategy.
Main results: The mean temperatures provided by the fuzzy control system during the day and night were 34.25°C and 23.22°C, respectively, which were improved to 31.17°C and 21.96°C after optimization. The mean humidity was 39.4% and 56.5% during the day and the night, respectively, which turned 60.22% and 74.59% after optimization. The control system also achieved desirable conditions in terms of CO2 amount.
Research highlights: The results showed that the measured values of temperature and relative humidity of the greenhouse were improved after optimization with genetic algorithm.
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References
Aaslyng JM, Lund JB, Ehler N, Rosenqvist E, 2003. IntelliGrow: A greenhouse component-based climate control system. Environ Model Softw 18: 657-666. https://doi.org/10.1016/S1364-8152(03)00052-5
Ali RB, Bouadila S, Mami A, 2018. Development of a Fuzzy Logic Controller applied to an agricultural greenhouse experimentally validated. Appl Therm Eng 141: 798-810. https://doi.org/10.1016/j.applthermaleng.2018.06.014
Alomran AM, Luki II, 2012. Effects of deficit irrigation on yield and water use of grown cucumbers in Saudi Arabia. Trans Ecol Environ 168: 353-358. https://doi.org/10.2495/SI120301
Alscher G, Krug H, Liebig HP, 2001. Optimisation of CO2 and temperature control in greenhouse crops by means of growth models at different abstraction levels III. Simulation and optimisation with the combined model. Gartenbauwissenschaft 66: 213-218.
Arbel A, Barak M, Shklyar A, 2003. Combination of forced ventilation and fogging systems for cooling greenhouses. Biosyst Eng 84: 45-55. https://doi.org/10.1016/S1537-5110(02)00216-7
Azaza M, Echaieb K, Mami A, Iqbal A, 2014. Optimized micro-climate controller of a greenhouse powered by photovoltaic generator. 5th Int Congr on Renewable Energy (IREC). Hammamet, Tunisia. https://doi.org/10.1109/IREC.2014.6826937
Banakar A, Azeem MF, 2011. Parameter identification of TSK neuro-fuzzy models. Fuzzy Sets Syst 179: 62-82. https://doi.org/10.1016/j.fss.2011.05.003
Banakar A, Karimi R, 2012. Genetic algorithm optimizing approach in rosa petals hot air dryer. Int J Agric Food Sci 2: 60-65.
Blasco X, Martínez M, Herrero JM, Ramos C, Sanchis J, 2007. Model-based predictive control of greenhouse climate for reducing energy and water consumption. Comput Electron Agric 55: 49-70. https://doi.org/10.1016/j.compag.2006.12.001
Bruant M, Guarracino G, Michel P, 2001. Design and tuning of a fuzzy controller for indoor air quality and thermal comfort management. Int J Sol Energ 21: 81-109. https://doi.org/10.1080/01425910108914366
Chen F, Tang YN, Shen MY, 2011. Coordination control of greenhouse environmental factors. Int J Autom Comput 8: 147-153. https://doi.org/10.1007/s11633-011-0567-3
Faouzi D, Bibi-Triki N, Draoui B, Abène A, 2017. Greenhouse environmental control using optimized, modeled and simulated fuzzy logic controller technique in MATLAB SIMULINK. I.J. Math Sci Comput 3: 12-27. https://doi.org/10.5815/ijmsc.2017.03.02
Ganguly A, Ghosh S, 2007. Modeling and analysis of a fan-pad ventilated floricultural greenhouse. Energ Build 39: 1092-1097. https://doi.org/10.1016/j.enbuild.2006.12.003
Guerbaoui M, Ed-Dahhak A, ElAfou Y, Lachhab A, Belkoura A, Bouchikhi B, 2013. Implementation of direct fuzzy controller in greenhouse based on labview. Int J Electr Electron Eng Stud 1(1): 1-13.
He H, Xue H, 2012. The design of greenhouse environment control system based on variable universe fuzzy control algorithm. In: Computer and Computing Technologies in Agriculture V. CCTA 2011; Li D, Chen Y (eds). IFIP Advances in Information and Communication Technology, vol 368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27281-3_10
Hellendoorn H, Thomas C, 1993. Defuzzification in fuzzy controllers. J Intell Fuzzy Syst 1: 109-123. https://doi.org/10.3233/IFS-1993-1202
Janoudi AK, Widders IE, Flore JA, 1993. Water deficits and environmental factors affect photosynthesis in leaves of cucumber (Cucumis sativus). J Am Soc Hortic Sci 118: 366-370. https://doi.org/10.21273/JASHS.118.3.366
Kayacan E, Khanesar MA, 2016. Chapter 2: Fundamentals of type-1 fuzzy logic theory. fuzzy neural netw. Real Time Control. Amsterdam: Elsevier, 13-24. https://doi.org/10.1016/B978-0-12-802687-8.00002-5
Khafajeh H, Banakar A, Minaei S, Delavar M, 2020. Evaluation of AquaCrop model of cucumber under greenhouse cultivation. J Agric Sci 158: 845-854. https://doi.org/10.1017/S0021859621000472
Khudoyberdiev A, Ahmad S, Ullah I, Kim D, 2020. An optimization scheme based on fuzzy logic control for efficient energy consumption in hydroponics environment. Energies 13(2): 1-27. https://doi.org/10.3390/en13020289
Kläring HP, Hauschild C, Heißner A, Bar-Yosef B, 2007. Model-based control of CO2 concentration in greenhouses at ambient levels increases cucumber yield. Agric For Meteorol 143: 208-216. https://doi.org/10.1016/j.agrformet.2006.12.002
Lafont F, Balmat JF, 2002. Optimized fuzzy control of a greenhouse. Fuzzy Sets Syst 128: 47-59. https://doi.org/10.1016/S0165-0114(01)00182-8
Lafont F, Balmat JF, 2004. Fuzzy logic to the identification and the command of the multidimensional systems. Int J Comput Cogn 2: 21-47.
Lammari K, Bounaama F, Ouradj B, Draoui B, 2020. Constrained Ga Pi sliding mode control of indoor climate coupled Mimo Greenhouse model. J Therm Eng 6: 313-326. https://doi.org/10.18186/thermal.711554
Mao X, Liu M, Wang X, Liu C, Hou Z, Shi J, 2003. Effects of deficit irrigation on yield and water use of greenhouse grown cucumber in the North China Plain. Agric Water Manag 61: 219-228. https://doi.org/10.1016/S0378-3774(03)00022-2
Márquez-Vera MA, Ramos-Fernández JC, Cerecero-Natale LF, Lafont F, Balmat JF, Esparza-Villanueva JI, 2016. Temperature control in a MISO greenhouse by inverting its fuzzy model. Comput Electron Agric 124: 168-174. https://doi.org/10.1016/j.compag.2016.04.005
Mohamed S, Hameed IA, 2018. A GA-based adaptive neuro-fuzzy controller for greenhouse climate control system. Alexandria Eng J 57: 773-779. https://doi.org/10.1016/j.aej.2014.04.009
Mohammadi A, Omid M, 2010. Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Appl Energ 87: 191-196. https://doi.org/10.1016/j.apenergy.2009.07.021
Moreton OR, Rowley PN, 2012. The feasibility of biomass CHP as an energy and CO2 source for commercial glasshouses. Appl Energ 96: 339-346. https://doi.org/10.1016/j.apenergy.2012.02.023
Ogata K, Yang Y, 2010. Modern control engineering. Pearson Upper Saddle River, NJ, USA.
Pilkington LJ, Messelink G, van Lenteren JC, Le Mottee K, 2010. Protected biological control-Biological pest management in the greenhouse industry. Biol Control 52: 216-220. https://doi.org/10.1016/j.biocontrol.2009.05.022
Robles C, Callejas J, Polo Llanos A, 2017. Low-cost fuzzy logic control for greenhouse environments with web monitoring. Electronics 6(4): 71. https://doi.org/10.3390/electronics6040071
Rogge E, Nevens F, Gulinck H, 2008. Reducing the visual impact of 'greenhouse parks' in rural landscapes. Landsc Urban Plan 87(1): 76-83. https://doi.org/10.1016/j.landurbplan.2008.04.008
Sieminski A, 2014. International energy outlook. Energy Inf Adm 18.
Sriraman A, Mayorga RV, 2007. Climate control inside a greenhouse: An intelligence system approach using fuzzy logic programming. J Env Inf 10: 68-74. https://doi.org/10.3808/jei.200700101
Trabelsi A, Lafont F, Kamoun M, Enea, G, 2007. Fuzzy identification of a greenhouse. Appl Soft Comput 7: 1092-1101. https://doi.org/10.1016/j.asoc.2006.06.009
Tripathi P, Rabara RC, Shulaev V, Shen QJ, Rushton PJ, 2015. Understanding water-stress responses in soybean using hydroponics system-A systems biology perspective. Front Plant Sci 6: 1145. https://doi.org/10.3389/fpls.2015.01145
Ursem RK, Krink T, Jensen MT, Michalewicz Z, 2002. Analysis and modeling of control tasks in dynamic systems. IEEE Trans Evol Comput 6: 378-389. https://doi.org/10.1109/TEVC.2002.802871
Wang L, Zhang H, 2018. An adaptive fuzzy hierarchical control for maintaining solar greenhouse temperature. Comput Electron Agric 155: 251-256. https://doi.org/10.1016/j.compag.2018.10.023
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