Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems

  • C. Duarte-Galvan CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
  • I. Torres-Pacheco CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
  • R. G. Guevara-Gonzalez CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
  • R. J. Romero-Troncoso HSPdigital-CA Telematica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5+1.8, Palo Blanco, 36885 Salamanca, Gto., Mexico
  • L. M. Contreras-Medina CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
  • M. A. Rios-Alcaraz CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
  • J. R. Millan-Almaraz Facultad de Ciencias Fisico-Matematicas Universidad Autonoma de Sinaloa Av. de las Américas y Blvd. Universitarios, Cd. Universitaria, CP 80000, Culiacan, Sinaloa, México Tel/Fax: (+52)6677161154 Ext. 117
Keywords: controller, conventional control, optimal control, precision agriculture, protected agriculture

Abstract

Today agriculture is changing in response to the requirements of modern society, where ensuring food supply through practices such as water conservation, reduction of agrochemicals and the required planted surface, which guarantees high quality crops are in demand. Greenhouses have proven to be a reliable solution to achieve these goals; however, a greenhouse as a means for protected agriculture has the potential to lead to serious problems. The most of these are related to the inside greenhouse climate conditions where controlling the temperature and relative humidity (RH) are the main objectives of engineering. Achieving appropriate climate conditions to ensure high yield and quality crops reducing energy consumption have been the objective of investigations for some time. Different schemes in control theories have been applied in this field to solve the aforementioned problems. Therefore, the objective of this paper is to present a review of different control techniques applied in protected agriculture to manage greenhouse climate conditions, presenting advantages and disadvantages of developed control platforms in order to suggest a design methodology according to results obtained from different investigations.

Downloads

Download data is not yet available.

Author Biographies

C. Duarte-Galvan, CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
Ph. D. Student
I. Torres-Pacheco, CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
Professor
R. G. Guevara-Gonzalez, CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
Professor
R. J. Romero-Troncoso, HSPdigital-CA Telematica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5+1.8, Palo Blanco, 36885 Salamanca, Gto., Mexico
Professor
L. M. Contreras-Medina, CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
Ph. D. Student
M. A. Rios-Alcaraz, CA Ingenieria de Biosistemas, Division de Investigacion y Posgrado, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Cerro de las Campanas s/n, 76010, Queretaro, Qro., Mexico
M. Sc. Student
J. R. Millan-Almaraz, Facultad de Ciencias Fisico-Matematicas Universidad Autonoma de Sinaloa Av. de las Américas y Blvd. Universitarios, Cd. Universitaria, CP 80000, Culiacan, Sinaloa, México Tel/Fax: (+52)6677161154 Ext. 117
Professor

References

Albright LD, Arvanitis KG, Drysdale AE, 2002. Environmental control for plants on earth and in space. Control Systems Magazine IEEE 21(5): 28-47.
http://dx.doi.org/10.1109/37.954518 

Ali IA, Abdalla AM, 1993. A microcomputer-based system for all-year-round temperature control in greenhouses in dry arid lands. Comput Electron Agr 8(3): 195-210.
http://dx.doi.org/10.1016/0168-1699(93)90033-W 

Ang KH, Chong G, Li Y, 2005. PID control system analysis, design, and technology. IEEE T Contr Syst T 13(4): 559-576.
http://dx.doi.org/10.1109/TCST.2005.847331 

Arvanitis KG, Paraskevopoulos PN, Vernardos AA, 2000. Multirate adaptive temperature control of greenhouses. Comput Electron Agr 26(3): 303-320.
http://dx.doi.org/10.1016/S0168-1699(00)00082-X 

Baptista FJ, Bailey BJ, Meneses JF, Navas LM, 2010. Greenhouses climate modelling. Tests, adaptation and validation of a dynamic climate model. Span J Agric Res 8(2): 285-298. 

Bennis N, Duplaix J, Enéa G, Haloua M, Youlal H, 2008. Greenhouse climate modeling and robust control. Comput Electron Agr 61(2): 96-107.
http://dx.doi.org/10.1016/j.compag.2007.09.014 

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 Agr 55(1): 49-70.
http://dx.doi.org/10.1016/j.compag.2006.12.001 

Caponetto R, Fortuna L, Nunnari G, Occhipinti L, Xibilia MG, 2002. Soft computing for greenhouse climate control. IEEE T Fuzzy Syst 8(6): 753-760. 

Castañeda-Miranda R, Ventura-Ramos E, Peniche-Vera RR, Herrera-Ruiz G, 2006. Fuzzy greenhouse climate control system based on a field programmable gate array. Biosyst Eng 94(2): 165-177.
http://dx.doi.org/10.1016/j.biosystemseng.2006.02.012 

Chalabi ZS, Bailey BJ, Wilkinson DJ, 1996. A real-time optimal control algorithm for greenhouse heating. Comput Electron Agr 15(1): 1-13.
http://dx.doi.org/10.1016/0168-1699(95)00053-4 

Coelho JP, De Moura Oliveira PB, Boaventura Cunha J, 2005. Greenhouse air temperature predictive control using the particle swarm optimisation algorithm. Comput Electron Agr 49(3): 330-344.
http://dx.doi.org/10.1016/j.compag.2005.08.003 

Contreras-Medina LM, Torres-Pacheco I, Guevara-Gonzalez RG, Romero-Troncoso RJ, Terol-Villalobos IR, Osornio-Rios RA, 2009. Mathematical modeling tendencies in plant pathology. Afr J Biotechnol 8(25): 7391-7400. 

Davis PF, Hooper AW, 2002. Improvement of greenhouse heating control. In: Control theory and applications, IEE Proceedings D 138, IET: 249-255.

 

De Baerdemaeker J, Munack A, Ramon H, Speckmann H, 2002. Mechatronic systems, communication, and control in precision agriculture. Control Systems Magazine, IEEE, 21(5): 48-70.
http://dx.doi.org/10.1109/37.954519 

De Koning ANM, 1990. Long-term temperature integration of tomato. Growth and development under alternating temperature regimes. Sci Hortic 45(1-2): 117-127.
http://dx.doi.org/10.1016/0304-4238(90)90074-O 

Dorf RC, Bishop RH, 2005. Sistemas de control moderno. Pearson Education, Madrid, 882 pp.
PMCid:2213137 

El Ghoumari MY, Tantau HJ, Serrano J, 2005. Non-linear constrained MPC: Real-time implementation of greenhouse air temperature control. Comput Electron Agr 49(3): 345-356.
http://dx.doi.org/10.1016/j.compag.2005.08.005 

Fan X, Zuo-Hua T, 2006. Application of a genetic simulated annealing algorithm in the greenhouse system. Computer Simulation 12: 045.

 

Fang L, Zhen-Xiao L, 2008. Research on the control mode of agriculture greenhouse control system in China. J Agr Mechaniz Res 10: 223-226. 

Fitz-Rodríguez E, Giacomelli GA, 2009. Yield prediction and growth mode characterization of greenhouse tomatoes with neural networks and fuzzy logic. T ASABE 52(6): 2115-2128. 

Fourati F, Chtourou M, 2007. A greenhouse control with feed-forward and recurrent neural networks. Simul Model Pract Th 15(8): 1016-1028.
http://dx.doi.org/10.1016/j.simpat.2007.06.001 

Gauthier L, 1992. A smalltalk-based platform for greenhouse environment control. Part I. Modeling and managing the physical system. T ASABE 35(6): 2003-2009. 

Gauthier L, Guay R, 1990. An object-oriented design for a greenhouse climate control system. T ASABE 33(3): 999-1004. 

Gauthier L, De Halleux D, Boisvert A, Trigui M, Zehrouni A, 1995. A control strategy for the operation of a reversible heat-pump in greenhouses. Appl Eng Agr 11(6): 873-879. 

Goggos V, King RE, 2000. Qualitative-evolutionary design of greenhouse environment control agents. Comput Electron Agr 26(3): 271-282.
http://dx.doi.org/10.1016/S0168-1699(00)00080-6 

Gutman PO, Lindberg PO, Ioslovich I, Seginer I, 1993. A non-linear optimal greenhouse control problem solved by linear programming. J Agr Eng Res 55(4): 335-351.
http://dx.doi.org/10.1006/jaer.1993.1054 

Hashimoto Y, Murase H, Morimoto T, Torii T, 2002. Intelligent systems for agriculture in Japan. IEEE Contr Syst Mag 21(5): 71-85.
http://dx.doi.org/10.1109/37.954520 

Hooper AW, 1988. Computer control of the environment in greenhouses. Comput Electron Agr 3(1): 11-27.
http://dx.doi.org/10.1016/0168-1699(88)90010-5 

Hooper AW, Davis PF, 1988. An algorithm for temperature compensation in a heated greenhouse. Comput Electron Agr 2(4): 251-262.
http://dx.doi.org/10.1016/0168-1699(88)90001-4 

Hurd RG, Graves CJ, 1983. The influence of different temperature patterns having the same integral on the earliness and yield of tomatoes. III Int Symp on Energy in Protected Cultivation (ISHS) 148: 547-554.

 

Ibrahim IA, Sørensen CG, 2010. A more energy efficient controller for the greenhouses climate control system. Appl Eng Agr 25(3): 491-498. 

Ioslovich I, Gutman P, Linker R, 2009. Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop. Automatica 45(5): 1227-1231.
http://dx.doi.org/10.1016/j.automatica.2008.12.024 

Ioslovich I, Gutman P, Seginer I, 1996: A non-linear optimal greenhouse control problem with heating and ventilation. Optim Contr Appl Met 17(3): 157-169.
http://dx.doi.org/10.1002/(SICI)1099-1514(199607/09)17:3<157::AID-OCA570>3.0.CO;2-X 

Jacobson BK, Jones PH, Jones JW, Paramore JA, 1989. Real-time greenhouse monitoring and control with an expert system. Comput Electron Agr 3(4): 273-285.
http://dx.doi.org/10.1016/0168-1699(89)90018-5 

Jewett TJ, Short TH, 1992. Computer control of a five-stage greenhouse shading system. T ASABE 35(2): 651-658. 

Jones P, Jones JW, Hwang Y, 1990. Simulation for determining greenhouse temperature setpoints. T ASABE 33(5): 1722-1728. 

Körner O, Challa H, 2003a. Design for an improved temperature integration concept in greenhouse cultivation. Comput Electron Agr 39(1): 39-59.
http://dx.doi.org/10.1016/S0168-1699(03)00006-1 

Körner O, Challa H, 2003b. Process-based humidity control regime for greenhouse crops. Comput Electron Agr 39(3): 173-192.
http://dx.doi.org/10.1016/S0168-1699(03)00079-6 

Körner O, Van Straten G, 2008. Decision support for dynamic greenhouse climate control strategies. Comput Electron Agr 60(1): 18-30.
http://dx.doi.org/10.1016/j.compag.2007.05.005 

Kurata K, Eguchi N, 1990. Machine learning of fuzzy rules for crop management in protected cultivation. T ASAE 33(4): 1360-1368. 

Langhans RW, Wolfe M, Albright LD, 1980. Use of average night temperatures for plant growth for potential energy savings. Symposium on More Profitable Use of Energy in Protected Cultivation (ISHS) 115: 31-38.

 

Linker R, Seginer I, Gutman P, 1998. Optimal CO2 control in a greenhouse modeled with neural networks. Comput Electron Agr 19(3): 289-310.
http://dx.doi.org/10.1016/S0168-1699(98)00008-8 

Linker R, Gutman PO, Seginer I, 2000. Robust model-based failure detection and identification in greenhouses. Comput Electron Agr 26(3): 255-270.
http://dx.doi.org/10.1016/S0168-1699(00)00079-X 

Millan-Almaraz J, Guevara-Gonzalez R, Romero-Troncoso R, Osornio-Rios R, Torres-Pacheco I, 2009. Advantages and disadvantages on photosynthesis measurement techniques: A review. Afr J Biotechnol 8(25): 8316-8331. 

Millan-Almaraz J, Romero-Troncoso R, Guevara-Gonzalez R, Contreras-Medina L, Carrillo-Serrano R, Osornio-Rios R, Duarte-Galvan C, Rios-Alcaraz M, Torres-Pacheco I, 2010. FPGA-based fused smart sensor for real-time plant-transpiration dynamic estimation. Sensors 10(9): 7340-7349.
http://dx.doi.org/10.3390/s100908316
PMid:22163656 PMCid:3231202 

Miller WB, Albright LD, Langhans RW, 1985. Plant growth under averaged day/night temperatures. Symposium Greenhouse Climate and its Control (ISHS) 174: 313-320.

 

Nielsen OF, 1995. Climate computer algorithms for peak shaving of greenhouse heating demand. Comput Electron Agr 13(4): 315-335.
http://dx.doi.org/10.1016/0168-1699(95)00031-3 

Ogata K, 2003. Ingenieria de control moderna. Pearson Education. Madrid, 894 pp. 

Pasgianos GD, Arvanitis KG, Polycarpou P, Sigrimis N, 2003. A nonlinear feedback technique for greenhouse environmental control. Comput Electron Agr 40(1-3): 153-177.
http://dx.doi.org/10.1016/S0168-1699(03)00018-8 

Pinon S, Camacho EF, Kuchen B, Peña M, 2005. Constrained predictive control of a greenhouse. Comput Electron Agr 49(3): 317-329.
http://dx.doi.org/10.1016/j.compag.2005.08.007 

Seginer I, 1997. Some artificial neural network applications to greenhouse environmental control. Comput Electron Agr 18(2-3): 167-186.
http://dx.doi.org/10.1016/S0168-1699(97)00028-8 

Seginer I, McClendon RW, 1992. Methods for optimal control of the greenhouse environment. T ASAE 35(4): 1299-1307. 

Seginer I, Hwang Y, Boulard T, Jones JW, 1996. Mimicking and expert greenhouse grower with a neural-net policy. T ASAE 39(1): 299-306. 

Setiawan A, Albright LD, Phelan RM, 2000. Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control. Comput Electron Agr 26(3): 283-302.
http://dx.doi.org/10.1016/S0168-1699(00)00081-8 

Sigrimis N, Rerras N, 1996. A linear model for greenhouse control. T ASABE 39(1): 253-261. 

Sigrimis N, Anastasiou A, Rerras N, 2000. Energy saving in greenhouses using temperature integration: A simulation survey. Comput Electron Agr 26(3): 321-341.
http://dx.doi.org/10.1016/S0168-1699(00)00083-1 

Sigrimis N, Antsaklis P, Groumpos PP, 2002. Advances in control of agriculture and the environment. IEEE Contr Syst Mag 21(5): 8-12.
http://dx.doi.org/10.1109/37.954516 

Sigrimis N, King RE, 2000. Advances in greenhouse environment control. Comput Electron Agr 26(3): 217-219.
http://dx.doi.org/10.1016/S0168-1699(00)00076-4 

Soto-Zarazua GM, Rico-Garcia E, Ocampo R, Guevara-Gonzalez RG, Herrera-Ruiz G, 2010. Fuzzy-logic-based feeder system for intensive tilapia production (Oreochromis niloticus). Aquacult Int 18(3): 379-391.
http://dx.doi.org/10.1007/s10499-009-9251-9 

Takakura T, Manning TO, Giacomelli GA, Roberts WJ, 1994. Feedforward control for a floor heat greenhouse. T ASABE 37(3): 939-945. 

Trabelsi A, Lafont F, Kamoun M, Enea G, 2007. Fuzzy identification of a greenhouse. Appl Soft Comput 7(3): 1092-1101.
http://dx.doi.org/10.1016/j.asoc.2006.06.009 

Van Straten G, Challa H, Buwalda F, 2000. Towards user accepted optimal control of greenhouse climate. Comput Electron Agr 26(3): 221-238.
http://dx.doi.org/10.1016/S0168-1699(00)00077-6 

Van Straten G, Van Willigenburg LG, Van Henten EJ, Van Ooteghem RJC, 2011. Optimal control of greenhouse cultivation. CRC Press, NY, USA. 

Vazquez-Cruz M, Torres-Pacheco I, Miranda-Lopez R, Cornejo-Perez O, Osornio-Ríos AR, Guevara-Gonzalez R, 2010. Potential of mathematical modeling in fruit quality. Afr J Biotechnol 9(3): 260-267.

Published
2012-10-26
How to Cite
Duarte-Galvan, C., Torres-Pacheco, I., Guevara-Gonzalez, R. G., Romero-Troncoso, R. J., Contreras-Medina, L. M., Rios-Alcaraz, M. A., & Millan-Almaraz, J. R. (2012). Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems. Spanish Journal of Agricultural Research, 10(4), 926-938. https://doi.org/10.5424/sjar/2012104-487-11
Section
Agricultural engineering