Phenotypic Analysis of Edible Mushroom Fruiting Bodies in Monocular RGB Images: A Problem-Oriented Critical Review

Monocular RGB imaging offers a low-cost, flexible approach to morphological measurement, quality assessment, growth monitoring, and production automation for edible mushroom fruiting bodies. However, the relationships among visible phenotypes, visual methods, measurement reliability, and production requirements remain insufficiently integrated. This problem-oriented review synthesizes 76 core studies published between 2016 and the final search date in 2026 through a framework linking visible phenotypes, visual tasks, key bottlenecks, and production applications. It covers individual localization and separation, structural measurement, spatial estimation, quality and species recognition, temporal analysis, and production deployment. The reviewed studies reveal a transition from static two-dimensional detection and counting toward instance-level morphological measurement, three-dimensional parameter estimation, and spatiotemporal growth modeling, extending phenotyping from visible appearance description to spatial trait estimation and growth prediction. Quantitative results also highlight the importance of evaluation conditions: for example, MSH-YOLOv8 achieved an AP50 of 98.49% on the Fungi dataset, whereas AP50:95 and small-object AP were 75.29% and 39.73%, respectively, indicating that high AP50 alone does not adequately characterize detection performance under stricter localization criteria or for small targets. These study-specific results cannot be directly extrapolated to commercial production environments. Severe occlusion, projection errors, inconsistent phenotype definitions, and temporal instability continue to constrain measurement reliability. Moreover, reliable performance without extensive retraining following changes in strains, substrates, or lighting systems remains insufficiently demonstrated. Future research should prioritize standardized multi-task and temporal datasets, unified phenotype definitions, uncertainty evaluation, occlusion-robust spatial and temporal modeling, and closed-loop production validation. Commercial scaling of low-cost monocular RGB imaging in protected mushroom cultivation depends on translating its affordability into reliable performance under severe occlusion and across production conditions, thereby enabling accessible automation for small and medium-scale producers.