The phenotypic features and yield of Lentinula edodes fruiting bodies are key metrics in breeding, cultivation, and quality evaluation. To overcome the inefficiency and physical damage associated with manual measurement, this paper proposes an automated approach using a lightweight YOLOv11-Seg model. On the basis of the YOLOv11-Seg model, the ShuffleNetV2 network, the C3k2-FasterBlock feature extraction module, and the convolutional block attention module (CBAM) were introduced to construct a lightweight YOLO-SFCB model, which significantly reduced the complexity and computational cost of the model. The experimental results show that the parameters, floating point operations (FLOPs), and mAP50-95 of the YOLO-SFCB model reach 2.0 M, 7.8 G, and 80.5%, respectively, while the GPU-based inference time is only 15.7 ms. Compared with the original model, parameters and FLOPs were reduced by 29% and 25%, inference time was shortened by 9.8%, and mAP50-95 increased by 0.9%. Based on the YOLO-SFCB model, OpenCV was used to extract the minimum rotation circumscribed rectangle of the stipe and pileus segmentation areas, and the stipe height, stipe diameter, pileus width, and pileus thickness were measured; the average residual is less than 0.35 mm. Finally, using the least squares method, a yield prediction model for Lentinula edodes fruiting bodies was developed. The average prediction errors for fresh weight and dry weight were controlled within 0.5 g and 0.15 g, respectively. The YOLO-SFCB model and the method for extracting phenotypic features and predicting yield of Lentinula edodes proposed in this study can help promote the development of Lentinula edodes breeding and cultivation and stabilize market supply and demand.