Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis
نویسندگان
چکیده
Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions critical controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) lighting systems to optimize plant growth reduce financial losses. In this study, several machine models were employed predict indoor air an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), Gaussian Process Regression (GPR) applied using external parameters such as outside air, relative humidity, wind speed, solar radiation. The results showed that RBF model with the LM learning algorithm outperformed SVM GPR models. had high accuracy reliability RMSE of 0.82 °C, MAPE 1.21%, TSSE 474.07 EF 1.00. prediction can help farmers manage their crops resources efficiently energy inefficiencies lower yields. integration into greenhouse control lead significant savings cost reductions.
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ژورنال
عنوان ژورنال: Horticulturae
سال: 2023
ISSN: ['2311-7524']
DOI: https://doi.org/10.3390/horticulturae9080853