ReviewJournal of fungi (Basel, Switzerland)2025
Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.
Review in Journal of fungi (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Phenotypic Analysis of Edible Mushroom Fruiting Bodies in Monocular RGB Images: A Problem-Oriented Critical Review.Journal of fungi (Basel, Switzerland) · 2026Review
- Design and Testing of an Integrated Robot for Harvesting, Stipe-Cutting, and Grading ofSensors (Basel, Switzerland) · 2026Article
- Dual-Platform Mushroom Cultivation for STEM Education: AI-Assisted Environmental Monitoring and Student Perceptions.Education sciences · 2026Article
- Molecular Advances and Sustainable Strategies in Mushroom Production for Food Security: A Review.Journal of fungi (Basel, Switzerland) · 2026Review
- Microbial contamination and microbiome composition of fresh edible mushrooms: a critical review.Frontiers in microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.