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Estimation of the Temperatures in an Experimental Infrared Heated Greenhouse Using Neural Network Models

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  • Angeliki Kavga

    (Department of Greenhouse Cultivations and Floriculture, Technological Educational Institute of Messolonghi, Messolonghi, Greece)

  • Vassilis Kappatos

    (Department of Mechanical and Water Resources Engineering, Technological Educational Institute of Messolonghi, Messolonghi, Greece)

Abstract

A high quality greenhouse control demands continuous measurements of indoor and outdoor greenhouse conditions. In this work, neural network models were used for reducing the cost and the time of temperature measurements, estimating two of the most important parameters in the operation of the greenhouse, namely the inside air temperature and the cover temperature of greenhouse. An extensive experimental investigation was carried out in an infrared heated greenhouse, using the experimental data to train and validate the neural network models. The modelling results show that the estimated temperatures have been in good agreement with the experimental data with accuracy ranging from 95.64% to 97.67%.

Suggested Citation

  • Angeliki Kavga & Vassilis Kappatos, 2013. "Estimation of the Temperatures in an Experimental Infrared Heated Greenhouse Using Neural Network Models," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 4(2), pages 14-22, July.
    • Handle: RePEc:igg:jaeis0:v:4:y:2013:i:2:p:14-22
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    Cited by:

    1. Lihan Chen & Lihong Xu & Ruihua Wei, 2023. "Energy-Saving Control Algorithm of Venlo Greenhouse Skylight and Wet Curtain Fan Based on Reinforcement Learning with Soft Action Mask," Agriculture, MDPI, vol. 13(1), pages 1-16, January.

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