Machine Learning Trends in Mushroom Agriculture: A Systematic Review Methodology

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.