Phenotyping of architecture traits of loblolly pine trees using stereo machine vision and deep learning: Stem diameter, branch angle, and branch diameter

Loblolly pine is one of the most planted forest tree species in the Southern United States for sawtimber production. The sawtimber yield potential of a pine tree is significantly impacted by its stem and branch architecture, which is of important focus in tree improvement programs. However, phenotyping these traits in the upper crown of pine trees is currently based on subjective visual assessments. This study investigated the feasibility of quantifying stem diameter, branch angle, and branch diameter of six-year-old loblolly pine trees in a progeny test using stereo 3D imaging, deep learning-based instance segmentation, and image and point cloud processing techniques. Instance segmentation of branches and stems was performed as well as principal component analysis (PCA) in 2D images, followed by 3D reconstruction of the segmented organs. The resulting 3D point clouds were further processed using random sample consensus (RANSAC) and statistical outlier removal to extract stem diameter, branch angle, and branch diameter. When compared to the manual ground-truth measurements, the three system-derived parameters achieved RMSEs of 0.055 m, 5.0˚, and 5.6 mm, respectively. In addition, Bland-Altman analyses showed that the stem diameter and branch angle estimations were found with limits of agreement of ± 0.098 m and ± 9.8˚, respectively, with nonsignificant biases. On the other hand, branch diameter estimation showed −12.1 mm and 9.3 mm for lower and upper limits of agreement with a bias. The proposed system demonstrates promising potential as a high-throughput low-cost precision phenotyping tool for the characterization of loblolly pine tree architecture under field conditions, facilitating the selection of superior genotypes with improved sawtimber properties.