Greenhouse microclimate environment adaptive control based on a wireless sensor network
Keywords:
greenhouse, microclimate control, wireless sensor network, fuzzy-PID controlAbstract
Environmental parameter measurements from various sensors spatially distributed in greenhouse crops can be used to create an accurate and detailed depiction of the climate in different regions of the greenhouse. Microclimate environments have an impact on crop yield, productivity, quantitative and qualitative characteristics of plants, and various plant diseases. In this study, an automatic monitoring system based on a wireless sensor network was designed to monitor the survival rate of greenhouse crops. The system design includes a sensor model selection and placement process, host computer monitoring software and communication module design. The Zigbee protocol was used for wireless communication between the nodes. The proposed fuzzy-PID controller was easy to design and highly adaptive to the measurement errors of the sensors. The test results show that the system valuable for monitoring the environment in the greenhouse, where it was successful in controlling and maintaining an optimal microclimate condition. Keywords: greenhouse, microclimate control, wireless sensor network, fuzzy-PID control DOI: 10.25165/j.ijabe.20201303.5027 Citation: Wang L N, Wang B R. Greenhouse microclimate environment adaptive control based on a wireless sensor network. Int J Agric & Biol Eng, 2020; 13(3): 64–69.References
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[2] Márquez Vera M A, Ramos-Fernández J C, Cerecero-Natale C F, Lafont F, Balmat J F, Esparza J I. Temperature control in a MISO greenhouse by inverting its fuzzy model. Computers and Electronics in Agriculture, 2016; 124: 168–174.
[3] Li Z G, Andrews J, Wang Y Q. Mathematical modelling of mechanical damage to tomato fruits. Postharvest Biology and Technology, 2017;
126: 50–56.
[4] Bai X Z, Liu L, Cao M Y, Panneerselvam J, Sun Q, Wang H X. Collaborative actuation of wireless sensor and actuator networks for the agriculture industry. IEEE Access, 2017; 5: 13286–13296.
[5] Lopes Toledo A, Lèbre La Rovere E. Urban mobility and greenhouse gas emissions: status, public policies, and scenarios in a developing economy city, Natal, Brazil. Sustainability. 2018; 10(11): 1–24.
[6] Yin J, Yang Y, Cao H, Zhang Z. Greenhouse environmental monitoring and closed-loop control with crop growth model based on wireless sensors network. Transactions of the Institute of Measurement & Control, 2015; 37(1): 50–62.
[7] Ding Y, Wang L, Li Y W, Li D L. Model predictive control and its application in agriculture: A review. Computers and Electronics in Agriculture, 2018; 151: 104–117.
[8] Wang L N, Zhang H H. An adaptive fuzzy hierarchical control for maintaining solar greenhouse temperature. Computers and Electronics in Agriculture, 2018; 155: 251–256.
[9] Ferentinos K P, Katsoulas N, Tzounis A, Bartzanas T, Kittas C. Wireless sensor networks for greenhouse climate and plant condition assessment. Biosystems Engineering, 2017; 153: 70–81.
[10] Gao P Q, Tian Z D, Gao X W. Network teleoperation robot system control based on fuzzy sliding mode. Journal of Advanced Computational Intelligence & Intelligent Informatics, 2016; 20(5): 828–835.
[11] Tang Y L, Lian H H, Li L X, Wang X J, Yan X X. A randomness detection method of ZigBee protocol in a wireless sensor network. Sensors, 2018; 18(11): 3962.
[12] Qi W H, Zong G D, Karimi H R. L∞ control for positive delay systems with semi-Markov process and application to a communication network model. IEEE Transactions on Industrial Electronics, 2019; 66(3): 2081–2091.
[13] Alfonso G B, Pablo C, Hyun Y, Juan C. GreenVMAS: Virtual organization based platform for heating greenhouses using waste energy from power plants. Sensors, 2018; 18(3): 861–881.
[14] Wang L, Zhang H. Sliding mode control with adaptive fuzzy compensation for uncertain nonlinear system. Mathematical Problems in Engineering, 2018; (PT.17): 2342391.1–2342391.6.
[15] Qi W H, Park J H, Zong G D, Cao J D, Cheng J. Anti-windup design for saturated semi-Markovian switching systems with stochastic disturbance. IEEE Transactions on Circuits and Systems II: Express Briefs, 2019; 66(7): 1187–1191.
[16] Tian Z D, Li S J, Wang Y H, Sha Y. A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos Solitons & Fractals, 2017; 98: 158–172.
[17] Kamilaris A, Prenafeta-Boldú F X. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 2018; 147: 70–90.
[18] Anna C, Salah S, Brett W. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 2018; 151: 61–69.
[19] Manonmani A, Thyagarajan T, Elango M, Sutha S. Modelling and control of greenhouse system using neural networks. Transactions of the Institute of Measurement and Control, 2016; 40(3): 918–929.
[20] Nachidi M, Rodriguez F, Tadeo F, Guzman J L. Takagi-Sugeno control of nocturnal temperature in greenhouses using air heating. ISA
Transactions, 2011; 50(2): 315–320.
[21] Zheng X L, Wang X Y, Yin Y F, Hu L L. Stability analysis and constrained fuzzy tracking control of positive nonlinear systems. Nonlinear Dynamics, 2015; 83(4): 22509–22522.
[22] Ramírez-Arias A, Rodríguez F, Guzmán J L, Berenguel M. Multiobjective hierarchical control architecture for greenhouse crop growth. Automatica, 2012; 48(3): 490–498.
[23] Yau H T, Chen C L. Fuzzy sliding mode controller design for maximum power point tracking control of a solar energy system. Transactions Institute of Measurement & Control, 2012; 34(5): 557–565.
[24] Li X H, Cheng X, Yan K, Gong P. A monitoring system for vegetable greenhouses based on a wireless sensor network. Sensors, 2010; 10(10): 8963–8980.
[25] Tang Y, Ma X, Li M, Wang Y. The effect of temperature and light on strawberry production in a solar greenhouse. Sol. Energy, 2020; 195: 318–328.
[26] Ioslovich I, Gutman P O, Linker R. Hamilton–Jacobi–Bellman formalism for optimal climate control of greenhouse crop. Automatica, 2009; 45(5): 1227–1231.
[27] Ahmed H A, Tong Y X, Yang Q C, Al-Faraj A A, Abdel-Ghany A M. Spatial distribution of air temperature and relative humidity in the greenhouse as affected by external shading in arid climates. Journal of Integrative Agriculture, 2019; 18(12): 2869–2882.
[28] Zhu X N, Ding B J, Li W J, Gu L Z, Yang Y X. On development of security monitoring system via wireless sensing network. J Wireless Com Network, 2018: 221.
[29] Bahloul M, Chrifi-Alaoui L, Drid S, Souissi M, Chaabane M. Robust sensorless vector control of an induction machine using multiobjective adaptive fuzzy luenberger observer. ISA Transactions, 2018; 74: 144–154.
[30] Su Y, Xu L, Li D. Adaptive fuzzy control of a class of MIMO nonlinear system with actuator saturation for greenhouse climate control problem, IEEE T. Autom. Science Eng. 2016; 13(2): 772–788.
[31] Kudinov Y I, Kolesnikov V A, Pashchenko F F, Pashchenko A F, Papic L. Optimization of fuzzy PID controller’s parameters. Procedia Computer Science, 2017; 103: 618–622.
[32] Wang Y Z, Jin Q B, Zhang R D. Improved fuzzy PID controller design using predictive functional control structure. ISA Transactions, 2017; 71(Part 2): 354–363.
[33] Precup R E, Hellendoorn H. A survey on industrial applications of fuzzy control. Computers in Industry, 2011; 62(3): 213–226.
[34] Akram U, Khalid M, Shafiq S. An improved optimal sizing methodology for future autonomous residential smart power systems. IEEE Access, 2018; 6: 5986–6000.
[35] Ma X P, Dong H H, Tang J Q, Jia L M, Qin Y, Cheng R J. Two-Layer hierarchy optimization model for communication protocol in railway wireless monitoring networks. Wireless Communications & Mobile Computing, 2018; 2018: 9516342.
[36] Liu F C, Liu Y L, Jin D H, Jia X Y, Wang T T. Research on workshop-based positioning technology based on internet of things in big data background. Complexity, 2018; 6: 1–11.
[37] Meka S, Fonseca, Jr. B. Improving route selections in ZigBee wireless sensor networks. Sensors, 2020; 20(1): 164.
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Published
2020-06-08
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Wang, L., & Wang, B. (2020). Greenhouse microclimate environment adaptive control based on a wireless sensor network. International Journal of Agricultural and Biological Engineering, 13(3), 64–69. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5027
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Animal, Plant and Facility Systems
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