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Machine Learning Trends in Mushroom Agriculture: A Systematic Review Methodology

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Advances in Visual Informatics (IVIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14322))

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Abstract

The optimization of sustainable growth and management of mushrooms requires the utilization of machine learning models and appropriate evaluation techniques. Prior to implementing machine learning model in agricultural settings, preliminary trials are often conducted to mitigate potential risks. During the experimental phase, sample data sets are obtained from various agriculture sources or existing data repositories. In this paper a systematic review methodology is employed to analyze the machine learning models used in mushroom farming. The review encompasses 71 articles analyzed from 2014 to 2023, derived from published sources such as PubMed, Willey Online Library, IEEE, and Google Scholar. The purpose is to address several research questions, including the identification of trends in the use of machine learning models for mushroom farming, comprehension of the evaluation techniques utilized, selection of data sources, and knowledge of current methodologies and learning strategies in machine learning as they pertain to agriculture. Overall, this review provides valuable insight into the everyday practices of machine learning in the context of mushroom farming. Researchers and practitioners can utilize the findings to develop effective models, evaluation techniques, and learning strategies in this field.

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Authors and Affiliations

  1. School of Science and Technology, Asia e University, 47500, Subang Jaya, Malaysia

    Bayu Priyatna & Zainab Abu Bakar

  2. College of Computer Studies, De La Salle University, 1004, Manila, Philippines

    Norshuhani Zamin

  3. Faculty Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia

    Yazrina Yahya

Authors
  1. Bayu Priyatna
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  2. Zainab Abu Bakar
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  3. Norshuhani Zamin
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  4. Yazrina Yahya
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Correspondence to Bayu Priyatna .

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Editors and Affiliations

  1. Universiti Tenaga Nasional, Kajang, Selangor, Malaysia

    Halimah Badioze Zaman

  2. University of Cambridge, Cambridge, UK

    Peter Robinson

  3. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  4. MIT Sloan School of Management, Asia School of Business, Cambridge, MA, USA

    Renato Lima De Oliveira

  5. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  6. National Central University, Jhongli, Taiwan

    Timothy K. Shih

  7. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Rabiah Abdul Kadir

  8. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Ummul Hanan Mohamad

  9. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Mohammad Nazir Ahmad

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Priyatna, B., Bakar, Z.A., Zamin, N., Yahya, Y. (2024). Machine Learning Trends in Mushroom Agriculture: A Systematic Review Methodology. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2023. Lecture Notes in Computer Science, vol 14322. Springer, Singapore. https://doi.org/10.1007/978-981-99-7339-2_47

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  • DOI: https://doi.org/10.1007/978-981-99-7339-2_47

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