Accuracy Evaluation and Branch Detection Method of 3D Modeling Using Backpack 3D Lidar SLAM and UAV-SfM for Peach Trees during the Pruning Period in Winter

Abstract

In the winter pruning operation of deciduous fruit trees, the number of pruning branches and the structure of the main branches greatly influence the future growth of the fruit trees and the final harvest volume. Terrestrial laser scanning (TLS) is considered a feasible method for the 3D modeling of trees, but it is not suitable for large-scale inspection. The simultaneous localization and mapping (SLAM) technique makes it possible to move the lidar on the ground and model quickly, but it is not useful enough for the accuracy of plant detection. Therefore, in this study, we used UAV-SfM and 3D lidar SLAM techniques to build 3D models for the winter pruning of peach trees. Then, we compared and analyzed these models and further proposed a method to distinguish branches from 3D point clouds by spatial point cloud density. The results showed that the 3D lidar SLAM technique had a shorter modeling time and higher accuracy than UAV-SfM for the winter pruning period of peach trees. The method had the smallest RMSE of 3084 g with an R2 = 0.93 compared to the fresh weight of the pruned branches. In the branch detection part, branches with diameters greater than 3 cm were differentiated successfully, regardless of whether before or after pruning.

Plant phenotyping relevance

桃樹の3D形状取得と枝検出・重量推定法を開発し、UAV-SfMと3D LiDAR SLAMを比較評価しており、植物形質取得手法が研究の中心である。

Code and data availability

The article's Data Availability Statement explicitly says 'This study did not report any data.' No public phenotype datasets, point clouds, images, author code, or trained models are deposited. CloudCompare is only a generic third-party tool used for ICP alignment, not a paper-specific asset, and all other URLs are to

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