A robotic vision system to measure tree traits
This work addresses the need for automated tree trait measurement for applications such as robotic pruning and structural phenotyping, representing an incremental advancement in domain-specific robotics.
The researchers tackled the problem of autonomously measuring tree traits like branch diameters, lengths, and angles, and developed a robotic vision system called RoTSE that achieved errors of 2.97 mm for branch diameter, 136.92 mm for branch length, and 31.07 degrees for branch angle, with an average run time of 8.47 minutes.
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 (root mean-squared error $2.97$ mm), branch length (root mean-squared error $136.92$ mm), and branch angle (mean-squared error $31.07$ degrees). The average run time was $8.47$ minutes when the voxel resolution was $3$ mm$^3$.