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Article

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

by
Elham Bolandnazar
1,
Hassan Sadrnia
1,
Abbas Rohani
1,
Francesco Marinello
2 and
Morteza Taki
3,*
1
Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 91779-48978, Iran
2
Department of Land, Environment, Agriculture and Forestry, University of Padova, 36100 Vicenza, Italy
3
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
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(8), 1583; https://doi.org/10.3390/agriculture13081583
Submission received: 24 June 2023 / Revised: 22 July 2023 / Accepted: 4 August 2023 / Published: 9 August 2023
(This article belongs to the Special Issue Neural Networks and Image Analysis in Intelligent Agricultural Engineering)
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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 (Tout), relative humidity (Hout), 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 (R2) value. The RBF model produced RMSE, MAPE, and R2 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 greenhouse temperature; polyethylene cover; prediction; soft-computing; reliability

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MDPI and ACS Style

Bolandnazar, E.; Sadrnia, H.; Rohani, A.; Marinello, F.; Taki, M. Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses. Agriculture 2023, 13, 1583. https://doi.org/10.3390/agriculture13081583

AMA Style

Bolandnazar E, Sadrnia H, Rohani A, Marinello F, Taki M. Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses. Agriculture. 2023; 13(8):1583. https://doi.org/10.3390/agriculture13081583

Chicago/Turabian Style

Bolandnazar, Elham, Hassan Sadrnia, Abbas Rohani, Francesco Marinello, and Morteza Taki. 2023. "Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses" Agriculture 13, no. 8: 1583. https://doi.org/10.3390/agriculture13081583

APA Style

Bolandnazar, E., Sadrnia, H., Rohani, A., Marinello, F., & Taki, M. (2023). Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses. Agriculture, 13(8), 1583. https://doi.org/10.3390/agriculture13081583

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