Comparative Evaluation of YOLOv8 and YOLOv11 Models for Efficient Phenotypic Segmentation of Edible Mushrooms

Digital phenotyping is increasingly recognized as a critical tool for quantitative analysis of fungal morphology, particularly in controlled indoor cultivation systems where large numbers of fruiting bodies must be assessed consistently and non-destructively. While YOLOv8-based deep learning approaches have been previously applied to phenotypic analysis of edible mushrooms, the applicability of newer YOLO architectures to fungal phenotyping remains unexplored. In this study, we present a controlled-environment digital phenotyping framework for indoor mushroom cultivation and evaluate the feasibility of YOLOv11 for phenotypic analysis through a direct comparison with YOLOv8. Using bottle-cultivated Pleurotus ostreatus and Flammulina velutipes as representative edible basidiomycetes, we conducted a systematic comparison of YOLOv8-seg and YOLOv11-seg under identical datasets, preprocessing pipelines, and hyperparameter configurations. The results demonstrate that YOLOv11 achieves segmentation performance comparable to YOLOv8 across all evaluated metrics (ΔmAP50–95 < 0.01), while substantially reducing computational complexity, including fewer trainable parameters, lower FLOPs, and decreased gradient load. Validation against caliper-based physical measurements revealed moderate, trait-dependent agreement, whereas inter-model consistency between YOLOv8 and YOLOv11 was consistently high across diverse morphological and segmentation scenarios. These findings indicate that architectural refinements in YOLOv11 primarily enhance computational efficiency and training behavior without altering phenotypic interpretation. Collectively, this study provides the first validation of YOLOv11 for mushroom phenotyping and highlights its potential as an efficient analytical tool for future high-throughput fungal morphology studies.