Liang Yinyin, Zhou Kai, Cao Lin
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China.
Front Plant Sci. 2024 Jul 25;15:1356078. doi: 10.3389/fpls.2024.1356078. eCollection 2024.
The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination ( ) = 0.99, root-mean-square error (RMSE) = 0.41 cm] outperformed the results of 3D models (MST-based: = 0.71, RMSE = 2.20 cm; TIN-based: = 0.54, RMSE = 2.80 cm). Additionally, the MST-based model (F1 = 0.81) outperformed the TIN-based model (F1 = 0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 ( = 0.94, < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.
植物根系表型分析对于提高植物生产力和适应性至关重要。然而，传统的根系表型信息收集技术存在局限性，且往往 labor-intensive，尤其是对于木本植物而言。在本研究中，一种先进的方法——基于精确详细定量结构模型（AdQSM）的根系表型测量（ARPM）被开发出来，用于自动从树木根系中提取表型。该方法涉及对从地面激光扫描（TLS）获得的点云进行三维（3D）重建，以提取关键表型参数，包括根直径（RD）、长度、表面积和体积。为了评估所提出的方法，使用了两种方法[基于最小生成树（MST）和基于不规则三角网（TIN）]从点云重建根系，并提取侧根数以及根直径，并与传统方法进行比较。结果表明，直接从点云提取的根直径[决定系数（ ）= 0.99，均方根误差（RMSE）= 0.41厘米]优于3D模型的结果（基于MST： = 0.71，RMSE = 2.20厘米；基于TIN： = 0.54，RMSE = 2.80厘米）。此外，在检测一级和二级侧根数量方面，基于MST的模型（F1 = 0.81）优于基于TIN的模型（F1 = 0.80）。每个表型特征随不同的云参数（CP）而波动，发现CP值为0.002（ = 0.94， < 0.01）有利于更好地提取结构表型。本研究有助于根系表型的提取和定量分析，并增强了我们对树木根系结构参数与相应生理功能之间关系的理解。
