Abstract
The Vegetative propagation by cuttings in a greenhouse is a fundamental step in the olive tree production chain. This technique is the best response and most useful production method worldwide, including in the Mediterranean regions. This preliminary step requires some environmental conditions that require a demanding permanent control. In the present study, the ANNs model was developed to predict the parameters inside olive cuttings greenhouse. The prediction model consists of five input parameters with two hidden layers and one output layer (inside air temperature, soil temperature, or relative air humidity). The results show the linear relationships between the measured and predicted parameters with good performance. The correlation results show that some attributes have a high correlation while others are low. The prediction of the temperature and relative humidity of the greenhouse can bring substantial help to advanced climate control and plant productivity.
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Chakir, S., Bekraoui, A., Zemmouri, E.M., Majdoubi, H., Mouqallid, M. (2022). Prediction of Olive Cuttings Greenhouse Microclimate Under Mediterranean Climate Using Artificial Neural Networks. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_7
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DOI: https://doi.org/10.1007/978-3-031-02447-4_7
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