Preprint Article Version 1 This version is not peer-reviewed

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Version 1 : Received: 15 August 2019 / Approved: 20 August 2019 / Online: 20 August 2019 (06:20:32 CEST)

How to cite: Ardabili, S.; Mosavi, A.; Mahmoudi, A.; Gundoshmian, T.M.; Nosratabadi, S.; Varkonyi-Koczy, A.R. Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks. Preprints 2019, 2019080201 (doi: 10.20944/preprints201908.0201.v1). Ardabili, S.; Mosavi, A.; Mahmoudi, A.; Gundoshmian, T.M.; Nosratabadi, S.; Varkonyi-Koczy, A.R. Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks. Preprints 2019, 2019080201 (doi: 10.20944/preprints201908.0201.v1).

Abstract

Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural networks with radial basis function was selected as the optimal predictor for the behavior of the system.

Subject Areas

agricultural production; environmental parameters; mushroom growth pre-diction; machine learning; artificial neural networks (ANN); food produc-tion; food security; multi-layered perceptron (MLP); radial basis function (RBF)

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