Comparative Analysis of YOLOv8 and YOLOv11 Models for Phenotypic Traits of Edible Mushrooms

Mushrooms have long been economically and nutritionally important crops, and recent advances in digital agriculture have increased interest in automating phenotypic evaluation. Due to the limitation of traditional phenotype assessment, various artificial intelligence (AI) models including YOLOv8 have been introduced to evaluate mushroom phenotypes non-destructively and efficiently. However, unlike previous models, few studies of mushroom phenotype assessment with YOLOv11 were published. In this study, using Pleurotus ostreatus and Flammulina velutipes, comparison of mushroom phenotype analysis between YOLOv8 and YOLOv11 was processed. All images were captured under controlled conditions and conducted to be preprocessed for the model evaluation. The results demonstrated that YOLOv11 achieved segmentation accuracy comparable to YOLOv8 (ΔmAP50–95 < 0.01) while substantially improving computational efficiency with a reduction of approximately 15–20%. In validation with the physical measurements of mushroom phenotype, both models showed biologically meaningful and moderate correlations across phenotypic traits (r ≈ 0.2–0.44; R² ≈ 0.72–0.83), confirming that YOLO-derived measurements captured essential dimensional variation. Inter-model comparisons revealed strong consistency (r ≥ 0.94, R² ≥ 0.96, MAE ≤ 0.40), indicating that YOLOv11 maintained the predictive reliability of YOLOv8 while operating with superior computational efficiency. This study establishes YOLOv11 as a robust foundation for AI-assisted digital breeding and automated quality monitoring systems in fungal research and precision agriculture.