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ARS Home » Northeast Area » Kearneysville, West Virginia » Appalachian Fruit Research Laboratory » Innovative Fruit Production, Improvement, and Protection » Research » Publications at this Location » Publication #338926
Title: A robotic vision system to measure tree traits

Author
item Tabb, Amy
item MEDEIROS, HENRY - Marquette University

Submitted to: IEEE RSJ International Conference on Intelligent Robots and Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2017
Publication Date: 12/14/2017
Citation: Tabb, A., Medeiros, H. 2017. A robotic vision system to measure tree traits. IEEE RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/IROS.2017.8206497.
DOI: https://doi.org/10.1109/IROS.2017.8206497

Interpretive Summary: In order to automate the problems of dormant pruning and structural phenotyping, it is necessary to be able to sense the shape of trees. This work uses a robot, cameras, and small truck and algorithms to sense the tree shape and then autonomously take measurements of apple trees. The accuracy of the system is assessed and reported. The impact of this work is it was shown that reliable tree measurements can be done in the field for complex tree crops.

Technical Abstract: The autonomous measurement of tree traits, such as branching structure, branch diameters, branch lengths, and branch angles, is required for tasks such as robotic pruning of trees as well as structural phenotyping. We propose a robotic vision system called the Robotic System for Tree Shape Estimation (RoTSE) to determine tree traits in field settings. The process is composed of the following stages: image acquisition with a mobile robot unit, segmentation, reconstruction, curve skeletonization, conversion to a graph representation, and then computation of traits. Quantitative and qualitative results on apple trees are shown in terms of accuracy, computation time, and robustness. Compared to ground truth measurements, the RoTSE produced the following estimates: branch diameter (mean-squared error 0.99 mm), branch length (mean-squared error 45.64 mm), and branch angle (mean-squared error 10.36 degrees). The average run time was 8.47 minutes when the voxel resolution was 3 mm.