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Approach of AI-Based Automatic Climate Control in White Button Mushroom Growing Hall

Author

Listed:
  • Rimantas Barauskas

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, Studentu St. 50-407, LT-51368 Kaunas, Lithuania)

  • Andrius Kriščiūnas

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, Studentu St. 50-407, LT-51368 Kaunas, Lithuania)

  • Dalia Čalnerytė

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, Studentu St. 50-407, LT-51368 Kaunas, Lithuania)

  • Paulius Pilipavičius

    (UAB Baltic Champs, Poviliškiai, LT-81411 Šiauliai, Lithuania)

  • Tautvydas Fyleris

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, Studentu St. 50-407, LT-51368 Kaunas, Lithuania)

  • Vytautas Daniulaitis

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, Studentu St. 50-407, LT-51368 Kaunas, Lithuania)

  • Robertas Mikalauskis

    (UAB Baltic Champs, Poviliškiai, LT-81411 Šiauliai, Lithuania)

Abstract

Automatic climate management enables us to reduce repetitive work and share knowledge of different experts. An artificial intelligence-based layer to manage climate in white button mushroom growing hall was presented in this article. It combines visual data, climate data collected by sensors, and technologists’ actions taken to manage climate in the mushroom growing hall. The layer employs visual data analysis methods (morphological analysis, Fourier analysis, convolutional neural networks) to extract indicators, such as the percentage of mycelium coverage and number of pins of different size per area unit. These indicators are used to generate time series that represent the dynamics of the mushroom growing process. The incorporation of time synchronized indicators obtained from visual data with monitored climate indicators and technologists’ actions allows for the application of a supervised learning decision making model to automatically define necessary climate changes. Whereas managed climate parameters and visual indicators depend on the mushroom production stage, three different models were created to correspond the incubation, shock, and fruiting stage of the mushroom production process (using decision trees, K-nearest neighbors’ method). An analysis of the results showed that trends of the selected visual indicators remain similar during different cultivations. Thus, the created decision-making models allow for the definition of the majority of the cases in which the climate change or transition between the growing stages is needed.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1921-:d:973401
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    References listed on IDEAS

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    1. Adel Mellit & Mohamed Benghanem & Omar Herrak & Abdelaziz Messalaoui, 2021. "Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks," Energies, MDPI, vol. 14(16), pages 1-16, August.
    2. Shuzhen Yang & Bocai Jia & Tao Yu & Jin Yuan, 2022. "Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot," Agriculture, MDPI, vol. 12(7), pages 1-24, July.
    3. 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.
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    Cited by:

    1. Shekaina Justin & Wafaa Saleh & Maha M. A. Lashin & Hind Mohammed Albalawi, 2023. "Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System," Sustainability, MDPI, vol. 15(18), pages 1-18, September.

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