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Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses

Author

Listed:
  • Elham Bolandnazar

    (Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 91779-48978, Iran)

  • Hassan Sadrnia

    (Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 91779-48978, Iran)

  • Abbas Rohani

    (Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 91779-48978, Iran)

  • Francesco Marinello

    (Department of Land, Environment, Agriculture and Forestry, University of Padova, 36100 Vicenza, Italy)

  • Morteza Taki

    (Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 63417-73637, Iran)

Abstract

Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (T out ), relative humidity (H out ), wind speed (W), and solar radiation (S), were used as inputs for these models, and the output was the inside temperature. The results showed that the RBF model with the LM (Levenberg–Marquardt) learning algorithm outperformed the other models, achieving the lowest error and the highest coefficient of determination (R 2 ) value. The RBF model produced RMSE, MAPE, and R 2 values of 1.32 °C, 3.23%, and 0.931, respectively. These results demonstrate that the RBF model with the LM learning algorithm can reliably predict greenhouse air temperatures for the next two hours. The ANN model can be applied to optimize time management and reduce energy losses, improving the overall efficiency of greenhouse operations.

Suggested Citation

  • Elham Bolandnazar & Hassan Sadrnia & Abbas Rohani & Francesco Marinello & Morteza Taki, 2023. "Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses," Agriculture, MDPI, vol. 13(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1583-:d:1213284
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    References listed on IDEAS

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    1. Alexander Nauta & Jingjing Han & Syeda Humaira Tasnim & William David Lubitz, 2023. "Performance Evaluation of a Commercial Greenhouse in Canada Using Dehumidification Technologies and LED Lighting: A Modeling Study," Energies, MDPI, vol. 16(3), pages 1-23, January.
    2. Gauravkumar Gadhesaria & Chinmay Desai & Ravi Bhatt & Bashir Salah, 2020. "Thermal Analysis and Experimental Validation of Environmental Condition Inside Greenhouse in Tropical Wet and Dry Climate," Sustainability, MDPI, vol. 12(19), pages 1-14, October.
    3. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    4. Cossu, Marco & Tiloca, Maria Teresa & Cossu, Andrea & Deligios, Paola A. & Pala, Tore & Ledda, Luigi, 2023. "Increasing the agricultural sustainability of closed agrivoltaic systems with the integration of vertical farming: A case study on baby-leaf lettuce," Applied Energy, Elsevier, vol. 344(C).
    5. Van Beveren, P.J.M. & Bontsema, J. & Van Straten, G. & Van Henten, E.J., 2015. "Minimal heating and cooling in a modern rose greenhouse," Applied Energy, Elsevier, vol. 137(C), pages 97-109.
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