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

Elham Bolandnazar, Hassan Sadrnia, Abbas Rohani, Francesco Marinello and Morteza Taki ()
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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

Agriculture, 2023, vol. 13, issue 8, 1-16

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.

Keywords: greenhouse temperature; polyethylene cover; prediction; soft-computing; reliability (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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