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Bayesian Networks for Greenhouse Temperature Control

  • José del Sagrado
  • Francisco Rodríguez
  • Manuel Berenguel
  • Rafael Mena
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)

Abstract

Greenhouse production processes are heavily influenced by greenhouse climate conditions, as crop growth performance is directly influenced by these conditions. A solution to the problem of controlling the temperature in greenhouses using an open–loop control system based on Bayesian networks is presented in this paper. The system is built and tested using data gathered from a real greenhouse. The results show the performance and applicability of this type of systems.

Keywords

Bayesian networks Greenhouse climate control Decision support 

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References

  1. 1.
    Bot, G.P.A.: Greenhouse climate from physical processes to a dynamic model. PhD thesis, Agricultural University of Wageningen: The Netherlands (1983)Google Scholar
  2. 2.
    Bot, G.P.A.: Physical modelling of greenhouse climate. In: Proc. of the IFAC/ISHS Workshop, pp. 7–12 (1991)Google Scholar
  3. 3.
    Boulard, T., Baille, A.: A simple greenhouse climate control model incorporating effects on ventilation and evaporative cooling. Agricultural and Forest Meteorology 65, 145–157 (1993)CrossRefGoogle Scholar
  4. 4.
    Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–348 (1992)zbMATHGoogle Scholar
  5. 5.
    Farkas, I.: Modelling and control in agricultural processes. Computers and Electronics in Agriculture 49, 315–316 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fayyad, U.M., Irani, K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proc. IJCAI 1993, Chambéry, France, pp. 1022–1029 (1993)Google Scholar
  7. 7.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)zbMATHCrossRefGoogle Scholar
  8. 8.
    Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)zbMATHGoogle Scholar
  9. 9.
    Jensen, F.V., Nielsen, T.D.: Bayesian networks and decision graphs, 2nd edn. Springer, New York (2007)zbMATHCrossRefGoogle Scholar
  10. 10.
    Jin, R., Breitbart, Y., Muoh, C.: Data discretization unification. Knowledge Information Systems 19, 1–29 (2009)CrossRefGoogle Scholar
  11. 11.
    Kamp, P.G.H., Timmerman, G.J.: Computerized environmental control in greenhouses. A step by step approach. IPC Plant, The Netherlands (1996)Google Scholar
  12. 12.
    Lam, W., Bacchus, F.: Learning Bayesian belief networks. An approach based on the MDL principle. Computational Intelligence 10, 269–293 (1994)CrossRefGoogle Scholar
  13. 13.
    Madsen, A., Jensen, F.V.: Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113, 203–245Google Scholar
  14. 14.
    Pawlowski, A., Guzman, J.L., Rodríguez, F., Berenguel, M., Sánchez, J., Dormido, S.: Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control. Sensors 9, 232–252 (2009), doi:10.3390/s90100232CrossRefGoogle Scholar
  15. 15.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  16. 16.
    Rodríguez, F., Berenguel, M., Arahal, M.R.: Feedforward controllers for greenhouse climate control based on physical models. In: Proc. ECC 2001, Oporto, Portugal (2001)Google Scholar
  17. 17.
    Rodríguez, F.: Modeling and hierarchical control of greenhouse crop production. PhD thesis, University of Almería, Spain (2002) (in Spanish), http://aer.ual.es/TesisPaco/TesisCompleta.pdf
  18. 18.
    Rodríguez, F., Guzmán, J.L., Berenguel, M., Arahal, M.R.: Adaptive hierarchical control of greenhouse crop production. Int. J. Adap. Cont. Signal Process. 22, 180–197 (2008)zbMATHCrossRefGoogle Scholar
  19. 19.
    Shafer, G., Shenoy, P.: Probability propagation. Annals of Mathematics and Artificial Intelligence 2 (1990)Google Scholar
  20. 20.
    Sigrimis, N., Antsaklis, P., Groumpos, P.P.: Advances in control of agriculture and the environment. IEEE Control Systems 21(5), 8–12 (2001), doi:10.1109/37.954516CrossRefGoogle Scholar
  21. 21.
    van Straten, G.: What can systems and control theory do for agriculture? Automatika 49(3-4), 105–107 (2008)Google Scholar
  22. 22.
    van Straten, G., van Willigenburg, G., van Henten, E., van Ooteghem, R.: Optimal control of greenhouse cultivation, p. 305. CRC Press, USA (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • José del Sagrado
    • 1
  • Francisco Rodríguez
    • 1
  • Manuel Berenguel
    • 1
  • Rafael Mena
    • 1
  1. 1.Departamento de InformáticaUniversidad de AlmeríaAlmeríaSpain

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