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Monitoring Mushroom Growth with Machine Learning

Author

Listed:
  • Vasileios Moysiadis

    (Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece)

  • Georgios Kokkonis

    (Department of Business Administration, University of Western Macedonia, 51100 Grevena, Greece)

  • Stamatia Bibi

    (Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece)

  • Ioannis Moscholios

    (Department of Informatics and Telecommunications, University of Peloponnese, 22100 Tripolis, Greece)

  • Nikolaos Maropoulos

    (Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece)

  • Panagiotis Sarigiannidis

    (Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece)

Abstract

Mushrooms contain valuable nutrients, proteins, minerals, and vitamins, and it is suggested to include them in our diet. Many farmers grow mushrooms in restricted environments with specific atmospheric parameters in greenhouses. In addition, recent technologies of the Internet of things intend to give solutions in the agriculture area. In this paper, we evaluate the effectiveness of machine learning for mushroom growth monitoring for the genus Pleurotus . We use YOLOv5 to detect mushrooms’ growing stage and indicate those ready to harvest. The results show that it can detect mushrooms in the greenhouse with an F1-score of up to 76.5%. The classification in the final stage of mushroom growth gives an accuracy of up to 70%, which is acceptable considering the complexity of the photos used. In addition, we propose a method for mushroom growth monitoring based on Detectron2. Our method shows that the average growth period of the mushrooms is 5.22 days. Moreover, our method is also adequate to indicate the harvesting day. The evaluation results show that it could improve the time to harvest for 14.04% of the mushrooms.

Suggested Citation

  • Vasileios Moysiadis & Georgios Kokkonis & Stamatia Bibi & Ioannis Moscholios & Nikolaos Maropoulos & Panagiotis Sarigiannidis, 2023. "Monitoring Mushroom Growth with Machine Learning," Agriculture, MDPI, vol. 13(1), pages 1-17, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:223-:d:1037072
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    References listed on IDEAS

    as
    1. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
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    Cited by:

    1. Anant Sujatanagarjuna & Shohreh Kia & Dominique Fabio Briechle & Benjamin Leiding, 2023. "MushR: A Smart, Automated, and Scalable Indoor Harvesting System for Gourmet Mushrooms," Agriculture, MDPI, vol. 13(8), pages 1-16, August.

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