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Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

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Listed:
  • Sina Ardabili
  • Amir Mosavi
  • Asghar Mahmoudi
  • Tarahom Mesri Gundoshmian
  • Saeed Nosratabadi
  • Annamaria R. Varkonyi-Koczy

Abstract

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on 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 was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. 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 network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.

Suggested Citation

  • Sina Ardabili & Amir Mosavi & Asghar Mahmoudi & Tarahom Mesri Gundoshmian & Saeed Nosratabadi & Annamaria R. Varkonyi-Koczy, 2020. "Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks," Papers 2010.02673, arXiv.org.
    • Handle: RePEc:arx:papers:2010.02673
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    References listed on IDEAS

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    1. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    2. Farshid Aram & Ebrahim Solgi & Ester Higueras García & Danial Mohammadzadeh S. & Amir Mosavi & Shahaboddin Shamshirband, 2019. "Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    3. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
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    Cited by:

    1. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    2. Rimantas Barauskas & Andrius Kriščiūnas & Dalia Čalnerytė & Paulius Pilipavičius & Tautvydas Fyleris & Vytautas Daniulaitis & Robertas Mikalauskis, 2022. "Approach of AI-Based Automatic Climate Control in White Button Mushroom Growing Hall," Agriculture, MDPI, vol. 12(11), pages 1-25, November.

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