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Volume 13
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Article

Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions

by
Fabián García-Vázquez
1,
Jesús R. Ponce-González
2,
Héctor A. Guerrero-Osuna
1,*,
Rocío Carrasco-Navarro
2,
Luis F. Luque-Vega
3,4,
Marcela E. Mata-Romero
5,
Ma. del Rosario Martínez-Blanco
1,
Celina Lizeth Castañeda-Miranda
1 and
Germán Díaz-Flórez
1
1
Posgrado en Ingeniería y Tecnología Aplicada, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Zacatecas, Mexico
2
Research Laboratory on Optimal Design, Devices and Advanced Materials—OPTIMA, Department of Mathematics and Physics, ITESO, Tlaquepaque 45604, Jalisco, Mexico
3
Department of Technological and Industrial Processes, ITESO, Tlaquepaque 45604, Jalisco, Mexico
4
Tecnológico Nacional de México, Instituto Tecnológico Superior de Jerez, Jerez 99863, Zacatecas, Mexico
5
Subdirección de Investigación, Centro de Enseñanza Técnica Industrial, Guadalajara 44638, Jalisco, Mexico
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(14), 8531; https://doi.org/10.3390/app13148531
Submission received: 20 June 2023 / Revised: 20 July 2023 / Accepted: 21 July 2023 / Published: 24 July 2023
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)
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Abstract

Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) and Support Vector Regression (SVR) to predict the internal temperature of a greenhouse. A meteorological station is installed in the greenhouse to collect internal data (temperature, humidity, and dew point) and external data (temperature, humidity, and solar radiation). The data comprises a one year, and is divided into seasons for better analysis and modeling of the internal temperature. The study involves sixteen experiments corresponding to the four models and the four seasons and evaluating the models’ performance using R2, RMSE, MAE, and MAPE metrics, considering an acceptability interval of ±2 °C. The results show that LR models had difficulty maintaining the acceptability interval, while the SVR models adapted to temperature outliers, presenting the highest forecast accuracy among the proposed algorithms.
Keywords: smart agriculture; data science; supervised learning smart agriculture; data science; supervised learning

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MDPI and ACS Style

García-Vázquez, F.; Ponce-González, J.R.; Guerrero-Osuna, H.A.; Carrasco-Navarro, R.; Luque-Vega, L.F.; Mata-Romero, M.E.; Martínez-Blanco, M.d.R.; Castañeda-Miranda, C.L.; Díaz-Flórez, G. Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions. Appl. Sci. 2023, 13, 8531. https://doi.org/10.3390/app13148531

AMA Style

García-Vázquez F, Ponce-González JR, Guerrero-Osuna HA, Carrasco-Navarro R, Luque-Vega LF, Mata-Romero ME, Martínez-Blanco MdR, Castañeda-Miranda CL, Díaz-Flórez G. Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions. Applied Sciences. 2023; 13(14):8531. https://doi.org/10.3390/app13148531

Chicago/Turabian Style

García-Vázquez, Fabián, Jesús R. Ponce-González, Héctor A. Guerrero-Osuna, Rocío Carrasco-Navarro, Luis F. Luque-Vega, Marcela E. Mata-Romero, Ma. del Rosario Martínez-Blanco, Celina Lizeth Castañeda-Miranda, and Germán Díaz-Flórez. 2023. "Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions" Applied Sciences 13, no. 14: 8531. https://doi.org/10.3390/app13148531

APA Style

García-Vázquez, F., Ponce-González, J. R., Guerrero-Osuna, H. A., Carrasco-Navarro, R., Luque-Vega, L. F., Mata-Romero, M. E., Martínez-Blanco, M. d. R., Castañeda-Miranda, C. L., & Díaz-Flórez, G. (2023). Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions. Applied Sciences, 13(14), 8531. https://doi.org/10.3390/app13148531

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