A fast phenotype approach of 3D point clouds of Pinus massoniana seedlings

Honghao Zhou; Yang Zhou; Wei Long; Bin Wang; Zhichun Zhou; Yue Chen

doi:10.3389/fpls.2023.1146490

A fast phenotype approach of 3D point clouds of Pinus massoniana seedlings

Front Plant Sci. 2023 Jun 26:14:1146490. doi: 10.3389/fpls.2023.1146490. eCollection 2023.

Authors

Honghao Zhou^{1

2}, Yang Zhou^{1

2}, Wei Long³, Bin Wang³, Zhichun Zhou³, Yue Chen⁴

Affiliations

¹ College of Electronic and Information Engineering, Zhejiang University of Science and Technology, HangZhou, ZheJiang, China.
² College of Biological and Chemical Engineering, Zhejiang University of Science and Technology, HangZhou, ZheJiang, China.
³ Research Institute of Subtropical Forestry, Chinese Academy of Forestry, HangZhou, ZheJiang, China.
⁴ Horticulture Institute, Zhejiang Academy of Agricultural Sciences, HangZhou, ZheJiang, China.

Abstract

The phenotyping of Pinus massoniana seedlings is essential for breeding, vegetation protection, resource investigation, and so on. Few reports regarding estimating phenotypic parameters accurately in the seeding stage of Pinus massoniana plants using 3D point clouds exist. In this study, seedlings with heights of approximately 15-30 cm were taken as the research object, and an improved approach was proposed to automatically calculate five key parameters. The key procedure of our proposed method includes point cloud preprocessing, stem and leaf segmentation, and morphological trait extraction steps. In the skeletonization step, the cloud points were sliced in vertical and horizontal directions, gray value clustering was performed, the centroid of the slice was regarded as the skeleton point, and the alternative skeleton point of the main stem was determined by the DAG single source shortest path algorithm. Then, the skeleton points of the canopy in the alternative skeleton point were removed, and the skeleton point of the main stem was obtained. Last, the main stem skeleton point after linear interpolation was restored, while stem and leaf segmentation was achieved. Because of the leaf morphological characteristics of Pinus massoniana, its leaves are large and dense. Even using a high-precision industrial digital readout, it is impossible to obtain a 3D model of Pinus massoniana leaves. In this study, an improved algorithm based on density and projection is proposed to estimate the relevant parameters of Pinus massoniana leaves. Finally, five important phenotypic parameters, namely plant height, stem diameter, main stem length, regional leaf length, and total leaf number, are obtained from the skeleton and the point cloud after separation and reconstruction. The experimental results showed that there was a high correlation between the actual value from manual measurement and the predicted value from the algorithm output. The accuracies of the main stem diameter, main stem length, and leaf length were 93.5%, 95.7%, and 83.8%, respectively, which meet the requirements of real applications.

Keywords: 3D point cloud; Pinus massoniana seedlings; phenotyping; skeletonization; slicing.

Grants and funding

Three Institute and Nine Party" Agricultural Science and Technology Cooperation Plan of Zhejiang Province(2023SNJF027), Zhejiang Key Research and Development Program (2021C02038 &2021C02061), "Pioneer" and "Leading Goose" R&D Program of Zhejiang (2023C02028), Zhejiang Province Commonweal Projects (LGG21F010003), General Projects of Agricultural and Social Development in Hangzhou (202203B06).