Abstract: This study aimed to develop a whole-surface detection and grading method for strawberry external quality by combining three-view image acquisition with an improved YOLOv11n-seg model. The method was intended to overcome the incomplete surface observation caused by single-view imaging and to realize integrated evaluation of maturity, bruising, weight, and shape for postharvest strawberry grading. A hanging-type online imaging system was developed to acquire strawberry images from three viewpoints under non-occluded conditions. A total of 1,030 strawberries were collected, generating 3,090 images. Among them, 2,490 images from 830 fruits were used to construct the instance segmentation dataset, and 600 images from the remaining 200 fruits were used for grading validation. The original segmentation architecture was improved by replacing the backbone with MobileNetV4, embedding the Convolutional Block Attention Module (CBAM), and introducing the Efficient Intersection over Union (EIoU) loss for localization optimization. The improved model was used to segment ripe bruise-free regions, unripe regions, and bruised regions on the strawberry surface. Based on the segmentation masks obtained from three views, the surface coloration ratio and bruise ratio were calculated. Geometric features were further extracted from the fruit mask after convex hull completion and pose normalization, and were used for weight prediction and shape grading. The three-view acquisition experiment showed that the proposed imaging scheme achieved a repeated area ratio of 7.2% to 7.3%, while no fruit-surface region was missed, indicating effective whole-surface coverage. On the test set, the improved model achieved a precision of 94.7%, a recall of 91.0%, an mAP_0.5 of 96.3%, and an mAP_0.5-0.9 of 80.8% for the bounding-box branch. For the mask branch, the corresponding values reached 95.3%, 91.5%, 96.2%, and 68.7%, respectively. For ripe bruise-free regions, both branches reached a precision of 99.8%, a recall of 100.0%, and an mAP_0.5 of 99.5%. The overall accuracy of strawberry maturity classification reached 94.0%, respectively, and the overall bruise grading accuracy reached 91.5%. For weight prediction, the multiple linear regression model achieved a coefficient of determination of 0.93, and the root mean square error was 2.59 g. For shape grading, the random forest model achieved the highest accuracy of 91.2% when the centroid-distance sequence length was 18. Compared with single-view detection, multi-view detection improved shape grading accuracy by 10.6 percentage points. In the final grading validation using 200 strawberries, the proposed method achieved an overall grading accuracy of 91.0%. After deployment with TensorRT in half-precision mode on a graphics processing unit, the average inference time was 28.8 ms. The results demonstrated that the proposed method effectively realized whole-surface acquisition, fine-grained region segmentation, and multi-index fusion grading for strawberries. The overall grading performance and deployment speed indicated that the method satisfied the practical requirements of accurate and real-time postharvest grading and provided a technical basis for intelligent strawberry quality evaluation and commercial processing.