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Neural Network Model for Greenhouse Microclimate Predictions

Author

Listed:
  • Theodoros Petrakis

    (Department of Agriculture, University of Patras, 26504 Patras, Greece)

  • Angeliki Kavga

    (Department of Agriculture, University of Patras, 26504 Patras, Greece)

  • Vasileios Thomopoulos

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Athanassios A. Argiriou

    (Laboratory of Atmospheric Physics, Department of Physics, University of Patras, 6500 Patras, Greece)

Abstract

Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As a result, to properly control these two essential aspects, the appropriate greenhouse environment should be maintained using a computational decision support system (DSS), which will be especially adaptable to changes in the characteristics of the external environment. A multilayer perceptron neural network (MLP-NN) was designed to model the internal temperature and relative humidity of an agricultural greenhouse. The specific NN uses Levenberg–Marquardt backpropagation as a training algorithm; the input variables are the external temperature and relative humidity, wind speed, and solar irradiance, as well as the internal temperature and relative humidity, up to three timesteps before the modeled timestep. The maximum errors of the modeled temperature and relative humidity are 0.877 K and 2.838%, respectively, whereas the coefficients of determination are 0.999 for both parameters. A model with a low maximum error in predictions will enable a DSS to provide the appropriate commands to the greenhouse actuators to maintain the internal conditions at the desired levels for cultivation with the minimum possible energy consumption.

Suggested Citation

  • Theodoros Petrakis & Angeliki Kavga & Vasileios Thomopoulos & Athanassios A. Argiriou, 2022. "Neural Network Model for Greenhouse Microclimate Predictions," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:780-:d:826878
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    References listed on IDEAS

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    1. Yongtao Shen & Ruihua Wei & Lihong Xu, 2018. "Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature," Energies, MDPI, vol. 11(1), pages 1-17, January.
    2. Angeliki Kavga & Vasileios Thomopoulos & Pantelis Barouchas & Nikolaos Stefanakis & Aglaia Liopa-Tsakalidi, 2021. "Research on Innovative Training on Smart Greenhouse Technologies for Economic and Environmental Sustainability," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    3. Barkat Rabbi & Zhong-Hua Chen & Subbu Sethuvenkatraman, 2019. "Protected Cropping in Warm Climates: A Review of Humidity Control and Cooling Methods," Energies, MDPI, vol. 12(14), pages 1-24, July.
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    Cited by:

    1. Piotr Boniecki & Agnieszka Sujak & Gniewko Niedbała & Hanna Piekarska-Boniecka & Agnieszka Wawrzyniak & Andrzej Przybylak, 2023. "Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
    2. Oladayo S. Ajani & Member Joy Usigbe & Esther Aboyeji & Daniel Dooyum Uyeh & Yushin Ha & Tusan Park & Rammohan Mallipeddi, 2023. "Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-14, July.

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