ArticleScientific data2026
Phenotypic image dataset of naturally grown shiitake mushrooms across multiple varieties and growth stages.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Phenotypic image dataset of naturally grown shiitake mushrooms across multiple varieties and growth stages.Scientific data · 2026Article
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9 authors.
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Abstract
As a valuable edible fungus with both culinary and medicinal value, shiitake mushrooms hold significant economic and social importance in promoting agricultural restructuring, increasing farmers' income, and advancing the intelligent development of the edible fungus industry. However, in natural growing environments, the classification, grading, and inspection of shiitake mushrooms still rely primarily on manual labor. This approach is inefficient, highly subjective, and lacks standardized data support. Currently, publicly available datasets for edible fungi are limited in quantity, making it challenging to meet the research demands for intelligent detection and automated harvesting models. To address this issue, this study constructed a multi-variety shiitake mushroom image dataset under natural growth conditions, selecting three mainstream market varieties: 9608, Chunsheng No. 1, and Qihe No. 9. The dataset contains 1,782 high-resolution original images, which were expanded to 6,500 images through systematic data augmentation, containing a total of 43,752 precisely annotated mushroom instances. The dataset covers three distinct growth stages (maturity, growing period, and juvenile stage) as well as a dedicated category for deformed mushrooms. The images feature realistic characteristics, including complex lighting, occlusion, dense distribution, and varying viewing angles. This dataset can be widely applied to object detection tasks, including intelligent grading of shiitake mushrooms, visual recognition for harvesting robots, yield estimation, production management, and decision-making for market release. It provides a high-quality data foundation for intelligent production in the edible fungi industry. The dataset is freely available for academic research and technical exchange.
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