Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches that rely on manual scanning or fixed camera setups remain a major bottleneck in this process. In this work, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The proposed system integrates the classical Octomap representation with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. While a low-resolution Octomap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize a set of 3D Gaussians for high-fidelity scene reconstruction. We further introduce simple yet effective strategies to enhance robustness against segmentation noise and reduce memory consumption. Simulation experiments demonstrate that our method outperforms purely occupancy-based approaches in both runtime efficiency and reconstruction accuracy, enabling precise fruit counting and volume estimation. Compared to a 0.01m-resolution Octomap, our approach achieves an improvement of 6.6% in fruit-level F1 score under noise-free conditions, and up to 28.6% under segmentation noise. Additionally, it achieves a 50% reduction in runtime, highlighting its potential for scalable, real-time semantic reconstruction in agricultural robotics.

翻译：农业场景的语义重建在表型分析和产量估计等任务中扮演着关键角色。然而，依赖人工扫描或固定相机设置的传统方法仍是该过程中的主要瓶颈。本研究提出一种利用移动机械臂的园艺环境主动三维重建框架。所提出的系统将经典的Octomap表示与3D高斯泼溅相结合，以实现精确高效的目标感知建图。低分辨率Octomap提供概率占据信息以支持信息化的视点选择和无碰撞规划，而3D高斯泼溅则利用几何、光度和语义信息来优化一组3D高斯分布，实现高保真度的场景重建。我们进一步引入了简单而有效的策略，以增强对分割噪声的鲁棒性并降低内存消耗。仿真实验表明，我们的方法在运行效率和重建精度上均优于纯占据式方法，能够实现精确的果实计数和体积估计。与0.01米分辨率的Octomap相比，我们的方法在无噪声条件下将果实级F1分数提升了6.6%，在存在分割噪声时最高可提升28.6%。此外，运行时间减少了50%，凸显了其在农业机器人领域实现可扩展实时语义重建的潜力。