Revisión. Ventajas y desventajas de los sistemas de control climático aplicados en agricultura de precisión
Resumen
Hoy en día la agricultura está cambiando de acuerdo a las necesidades de la nueva sociedad. Las nuevas tendencias son asegurar la producción de alimentos a través de prácticas tales como ahorro de agua, reducción en el uso de agroquímicos y el espacio requerido para sembrar los cultivos mientras se garantiza la alta calidad de los cultivos. Los invernaderos han demostrado ser una solución viable para garantizar estos objetivos. Sin embargo, el uso de un invernadero conlleva serios problemas. Los más importantes están relacionados con las condiciones del microclima dentro del invernadero, donde el objetivo de la ingeniería es controlar la temperatura y humedad relativa (RH). Alcanzar las condiciones adecuadas del microclima para garantizar la alta productividad y calidad de los cultivos mientras se reducen los consumos de energía ha sido el objetivo de diversos investigadores a través del tiempo. Diversos esquemas de teoría de control han sido aplicados con el objetivo de resolver los problemas antes mencionados. Por lo tanto, el objetivo de este artículo es presentar una revisión de las diferentes técnicas de control aplicadas en agricultura de precisión para manejar las condiciones del microclima del invernadero, presentando las ventajas y desventajas de los sistemas desarrollados con la finalidad de proponer una metodología de diseño de acuerdo a los resultados obtenidos de las diferentes investigaciones.Descargas
Citas
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.
© CSIC. Los originales publicados en las ediciones impresa y electrónica de esta Revista son propiedad del Consejo Superior de Investigaciones Científicas, siendo necesario citar la procedencia en cualquier reproducción parcial o total.
Salvo indicación contraria, todos los contenidos de la edición electrónica se distribuyen bajo una licencia de uso y distribución “Creative Commons Reconocimiento 4.0 Internacional ” (CC BY 4.0). Consulte la versión informativa y el texto legal de la licencia. Esta circunstancia ha de hacerse constar expresamente de esta forma cuando sea necesario.
No se autoriza el depósito en repositorios, páginas web personales o similares de cualquier otra versión distinta a la publicada por el editor.
