Robotic strawberry harvesting remains challenging under partial occlusion, where leaves obscure fruit geometry and make grasp decisions based on a single shape estimate unreliable. A partial observation may admit multiple plausible 3D completions, so a grasp feasible on one completion may fail on another. Existing uncertainty-aware grasping methods estimate uncertainty in pose, shape, or individual candidates, but do not aggregate grasp feasibility across completion hypotheses for an object-level attempt-or-abstain decision. We present UNCLE-Grasp, a task-adapted framework integrating learned shape completion, dropout-based variability estimation, physically grounded grasp evaluation, and risk-aware target-level decisions for leaf-occluded strawberries. Monte Carlo dropout generates multiple completion samples. For each sample, retained grasp candidates are combined into a wrench space to compute a completion-level force-closure score. Variability in this score across plausible completions, rather than geometric point variability alone, quantifies target-level grasp uncertainty. A conservative lower confidence bound determines whether to grasp or abstain. We evaluate the framework in simulation and on a physical robot under increasing synthetic and real leaf occlusion. At the highest simulated occlusion, UNCLE-Grasp raises success among attempted grasps from 0.780 for the strongest completed baseline to 0.870, with similar attempt rates of 0.860 and 0.880, respectively, and raises overall success from 0.680 to 0.740. On the physical robot at approximately 87% synthetic occlusion, it achieves 0.800 success among attempted grasps versus 0.483 for the strongest completed baseline, although at a lower attempt rate. These results show that UNCLE-Grasp enables more reliable risk-aware execution through selective abstention, with a trade-off between grasp reliability and harvesting yield.

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