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- W4200598972 abstract "Dormant pruning for fresh market fruit trees is a relatively unexplored application of agricultural robotics for which few end-to-end systems exist. One of the biggest challenges in creating an autonomous pruning system is the need to reconstruct a model of a tree which is accurate and informative enough to be useful for deciding where to cut. One useful structure for modeling a tree is a skeleton: a 1D, lightweight representation of the geometry and the topology of a tree. This skeletonization problem is an important one within the field of computer graphics, and a number of algorithms have been specifically developed for the task of modeling trees. These skeletonization algorithms have largely addressed the problem as a geometric one. In agricultural contexts, however, the parts of the tree have distinct labels, such as the trunk, supporting branches, etc. This labeled structure is important for understanding where to prune. We introduce an algorithm which uses topological and geometric priors about Upright Fruiting Offshoot (UFO) trees to produce such a labeled skeleton. We test our skeletonization algorithm on point clouds from 29 UFO trees and, using an accuracy metric based on the number of correctly reconstructed and labeled skeleton segments, demonstrate a median construction accuracy of 70% with respect to a human-evaluated gold standard. We also make point cloud scans of 82 UFO trees open-source to other researchers. Our work represents a significant first step towards a robust tree modeling framework which can be used in an autonomous pruning system." @default.
- W4200598972 created "2021-12-31" @default.
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- W4200598972 date "2022-01-01" @default.
- W4200598972 modified "2023-09-29" @default.
- W4200598972 title "Semantics-guided skeletonization of upright fruiting offshoot trees for robotic pruning" @default.
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- W4200598972 doi "https://doi.org/10.1016/j.compag.2021.106622" @default.
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