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Bayesian Networks for Greenhouse Temperature Control

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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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  • DOI: 10.1007/978-3-319-01854-6_17
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References

  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. Bot, G.P.A.: Physical modelling of greenhouse climate. In: Proc. of the IFAC/ISHS Workshop, pp. 7–12 (1991)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  4. Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–348 (1992)

    MATH  Google Scholar 

  5. Farkas, I.: Modelling and control in agricultural processes. Computers and Electronics in Agriculture 49, 315–316 (2005)

    CrossRef  MathSciNet  Google Scholar 

  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. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)

    CrossRef  MATH  Google Scholar 

  8. Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)

    MATH  Google Scholar 

  9. Jensen, F.V., Nielsen, T.D.: Bayesian networks and decision graphs, 2nd edn. Springer, New York (2007)

    CrossRef  MATH  Google Scholar 

  10. Jin, R., Breitbart, Y., Muoh, C.: Data discretization unification. Knowledge Information Systems 19, 1–29 (2009)

    CrossRef  Google Scholar 

  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. Lam, W., Bacchus, F.: Learning Bayesian belief networks. An approach based on the MDL principle. Computational Intelligence 10, 269–293 (1994)

    CrossRef  Google Scholar 

  13. Madsen, A., Jensen, F.V.: Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113, 203–245

    Google Scholar 

  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/s90100232

    CrossRef  Google Scholar 

  15. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)

    Google Scholar 

  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. 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. 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)

    CrossRef  MATH  Google Scholar 

  19. Shafer, G., Shenoy, P.: Probability propagation. Annals of Mathematics and Artificial Intelligence 2 (1990)

    Google Scholar 

  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:10.1109/37.954516

    CrossRef  Google Scholar 

  21. van Straten, G.: What can systems and control theory do for agriculture? Automatika 49(3-4), 105–107 (2008)

    Google Scholar 

  22. van Straten, G., van Willigenburg, G., van Henten, E., van Ooteghem, R.: Optimal control of greenhouse cultivation, p. 305. CRC Press, USA (2010)

    CrossRef  Google Scholar 

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Correspondence to José del Sagrado .

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del Sagrado, J., Rodríguez, F., Berenguel, M., Mena, R. (2014). Bayesian Networks for Greenhouse Temperature Control. In: , et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-01854-6_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01853-9

  • Online ISBN: 978-3-319-01854-6

  • eBook Packages: EngineeringEngineering (R0)