Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

  • Sina Ardabili
  • Amir MosaviEmail author
  • Asghar Mahmoudi
  • Tarahom Mesri Gundoshmian
  • Saeed Nosratabadi
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)


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.


Agricultural production Environmental parameters Mushroom growth prediction Machine learning Artificial neural networks (ANN) Food production Food security 



This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Institute of Automation, Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Department of Biosystem EngineeringUniversity of TabrizTabrizIran
  5. 5.Department of Biosystem EngineeringUniversity of Mohaghegh ArdabiliArdabilIran
  6. 6.Institute of Business Studies, Szent Istvan UniversityGodolloHungary
  7. 7.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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