Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Greenhouse Technology and Climate Control · Leaf Properties and Growth Measurement · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Posgrado en Ingeniería y Tecnología Aplicada, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Zacatecas, Mexico
Research Laboratory on Optimal Design, Devices and Advanced Materials—OPTIMA, Department of Mathematics and Physics, ITESO, Tlaquepaque 45604, Jalisco, Mexico
Department of Technological and Industrial Processes, ITESO, Tlaquepaque 45604, Jalisco, Mexico
Tecnológico Nacional de México, Instituto Tecnológico Superior de Jerez, Jerez 99863, Zacatecas, Mexico
Subdirección de Investigación, Centro de Enseñanza Técnica Industrial, Guadalajara 44638, Jalisco, Mexico
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