Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.

翻译：休眠期葡萄藤的机器人修剪是促进藤蔓平衡与果实品质的重要研究方向，但目前机器人相关研究主要集中于平面化、简化的藤蔓形态，无法代表商业化葡萄园的真实场景。本文旨在通过扩展植物骨架化技术，提升机器人对更密集、复杂藤蔓结构的感知能力，以满足修剪需求。所提出的处理流程生成的葡萄藤骨架模型，在重投影误差和连通性方面均优于基线算法。同时，我们展示了三维与骨架信息如何使密集藤蔓的修剪重量预测精度超越先前研究，其中修剪重量作为影响剪枝位置选择的关键藤蔓指标。