In robotic fruit picking applications, managing object occlusion in unstructured settings poses a substantial challenge for designing grasping algorithms. Using strawberry harvesting as a case study, we present an end-to-end framework for effective object detection, segmentation, and grasp planning to tackle this issue caused by partially occluded objects. Our strategy begins with point cloud denoising and segmentation to accurately locate fruits. To compensate for incomplete scans due to occlusion, we apply a point cloud completion model to create a dense 3D reconstruction of the strawberries. The target selection focuses on ripe strawberries while categorizing others as obstacles, followed by converting the refined point cloud into an occupancy map for collision-aware motion planning. Our experimental results demonstrate high shape reconstruction accuracy, with the lowest Chamfer Distance compared to state-of-the-art methods with 1.10 mm, and significantly improved grasp success rates of 79.17%, yielding an overall success-to-attempt ratio of 89.58\% in real-world strawberry harvesting. Additionally, our method reduces the obstacle hit rate from 43.33% to 13.95%, highlighting its effectiveness in improving both grasp quality and safety compared to prior approaches. This pipeline substantially improves autonomous strawberry harvesting, advancing more efficient and reliable robotic fruit picking systems.