Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

Abstract

The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.

Mobile 3D scan LiDAR: a literature review

“…Internal crown gaps are difficult to be detected with traditional methods, and this can cause volume overestimation (Westling et al 2020). Even branches information like typology, length and number are utilized as indicators in productive orchards, giving an indication about trees growth and yields (Zhang et al 2020). In the last decade, the laser scanner technologies had, and still has, an important role in the automatization of these processes.…”

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

“…However, algorithmic pruning optimization has wider potential applicability to complement rule-based solutions in the developing field of automated pruning [37]. The progress of scanning technology and computer vision allows producing increasingly faithful digital reconstructions of real trees [38][39][40]. A wider availability of relatively low-cost devices equipped with LiDAR scanners should enable further advance of the field, as already demonstrated by some recent research [11,41].…”

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

“…Xie et al's [100] results indicate that a vertical thickness of 30 cm for point cloud slices was best for DBH estimation (i.e., R square was 0.89 when compared with manual measurements) at the single tree level from BLS point clouds and DBH extracted from BLS was smaller than that from manual measurements. Zhang et al [101] compared the potential of TLS and BLS for the estimation of apple tree branches and the results show that TLS is better in the estimation of branch length and BLS is better for the number counting of branches. However, the application of BLS is still in an early stage, and its measurement accuracy and error sources have not been systematically explored [100].…”