Small fruits and partially occluded contours make strawberry instance segmentation difficult in greenhouse images. This study develops PMH-YOLO11-seg from YOLO11l-seg to examine whether finer spatial features and auxiliary localization supervision improve mask prediction. A stride-4 P2 branch extends the original P3/P4/P5 feature hierarchy. MultiSEAM modules process P2/P3/P4, while P5 connects directly to the segmentation head. The enhanced P2 feature supplies both mask prototypes and a center heatmap branch. During training, the center branch learns box-derived Gaussian targets using error-weighted binary cross-entropy; it is bypassed during inference. Evaluation used a separate test set of 200 images containing 1132 strawberry instances. In repeated training experiments, the model with a center-loss weight of 0.4 achieved mask mAP50 and mAP50–95 of $97.41\pm 0.07$% and $84.35\pm 0.11$%, respectively (mean ± sample standard deviation). The corresponding improvements over the baseline means were 1.33 and 3.71 percentage points. In the single-run ablation, the full model exceeded P2 + MultiSEAM by only 0.03 percentage points in mAP50–95, calculated before rounding. Repeated component comparisons are needed to determine whether this small additional gain is consistent across training runs.