Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review

Edible mushrooms have gained global popularity due to their nutritional value, medicinal properties, bioactive compounds and industrial applications. Despite their long-standing roles in ecology, nutrition, and traditional medicine, their additional functions in cultivation, breeding, and classification processes are still in their infancy due to technological constraints. The advent of Artificial Intelligence (AI) technologies has transformed the cultivation process of mushrooms, genetic breeding, and classification methods. However, the analysis of the application of AI in the mushroom production cycle is currently scattered and unorganized. This comprehensive review explores the application of AI technologies in mushroom cultivation, breeding, and classification. Four databases (Scopus, IEEE Xplore, Web of Science, and PubMed) and one search engine (Google Scholar) were used to perform a thorough review of the literature on the utility of AI in various aspects of the mushroom production cycle, including intelligent environmental control, disease detection, yield prediction, germplasm characterization, genotype-phenotype integration, genome editing, gene mining, multi-omics, automatic species identification and grading. In order to fully realize the potential of these edge-cutting AI technologies in transforming mushroom breeding, classification, and cultivation, this review addresses challenges and future perspectives while calling for interdisciplinary approaches and multimodal fusion.