2024 INTERNATIONAL JOURNAL OF SUSTAINABLE AGRICULTURAL MANAGEMENT AND INFORMATICS 2024 10(卷), 3(期), (null页)
In this study, the air temperature inside a semi-arid greenhouse was investigated. The model database was built using greenhouse climatic data from a prototype greenhouse in Algeria's Ghardaia region. Over January month, external and internal climatic data were collected in order to develop and validate models for simulating environmental conditions inside the greenhouse, such as relative humidity (RHext), total radiation (GH), air pressure (P), and external temperature (Text). The main objective of this study is to compare three deep neural network models feed forward networks (FFN), Nonlinear auto-regressive network with exogenous inputs (NARX), and recurrent neural networks (RNN-LSTM) to see which one predicted temperature changes in the environment the best. The results showed that the NARX-predicted results agreed closely with the measurements; additionally, RNN provided satisfactory results, while FFN was the weakest of the three models.