The growing global demand for Agaricus bisporus has focused on automated harvesting systems, prompting the adoption of artificial intelligence to enhance precision and efficiency. This study aimed to prove the possibility of automated analysis for mushroom phenotypic traits including pileus diameter and color parameters (L*, a*, b*) by using AI model, YOLOv11-seg. Mushroom images were obtained in custom-designed imaging chamber and image training was processed using YOLOv11-seg. By achieving an mAP50 of 0.96, model demonstrated high detection and segmentation performance with stable predictive behavior. To evaluate biological validity, predicted phenotypic traits were compared with mechanically measured values. Pearson correlation coefficient analysis showed that the correlation coefficient for chromaticity was above 0.69, while the correlation coefficient for shoulder diameter was very low at 0.03. Linear regression analysis showed correlations above 0.69 for all phenotypic traits, indicating that the model analysis reflected the actual measurement variation well. Mean absolute error (MAE) analysis showed less than 10% error of 1.32, 2.43, 0.55, and 0.90 in pileus diameter, L*, a*, and b*, respectively, resulting in significant model accuracy. Based on these results, YOLO-based estimation of pileus area was processed to prove the model’s capacity to extract phenotypic traits beyond the limits of traditional analysis. These results indicate that AI models including YOLOv11 show the possibility of the automated growth monitoring for the next-generation smart cultivation systems.