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Bumblebee: A Path Towards Fully Autonomous Robotic Vine Pruning

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

Dormant season grapevine pruning requires skilled seasonal workers during the winter season which are becoming less available. As workers hasten to prune more vines in less time amid to the short-term seasonal hiring culture and low wages, vines are often pruned inconsistently leading to imbalanced grapevines. In addition to this, currently existing mechanical methods cannot selectively prune grapevines and manual follow-up operations are often required that further increase production cost. In this paper, we present the design and field evaluation of a rugged, and fully autonomous robot for end-to-end pruning of dormant season grapevines. The proposed design incorporates novel camera systems, a kinematically redundant manipulator, a ground robot, and novel algorithms in the perception system. The presented research prototype robot system was able to spur prune a row of vines from both sides completely in 213 sec/vine with a total pruning accuracy of 87%. Initial field tests of the autonomous system in a commercial vineyard have shown significant variability reduction in dormant season pruning when compared to mechanical pre-pruning trials. The design approach, system components, lessons learned, future enhancements as well as a brief economic analysis are described in the manuscript.

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2025 1

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representative citing papers

Learning to Prune Branches in Modern Tree-Fruit Orchards

cs.RO · 2025-07-30 · conditional · novelty 6.0

A simulation-trained optical-flow controller guides a robot cutter to pruning points and orients it perpendicular to branches, with about 30% success in simulation and 35% on a lab tree.

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  • Learning to Prune Branches in Modern Tree-Fruit Orchards cs.RO · 2025-07-30 · conditional · none · ref 9 · internal anchor

    A simulation-trained optical-flow controller guides a robot cutter to pruning points and orients it perpendicular to branches, with about 30% success in simulation and 35% on a lab tree.