Yinyin Liang Kai Zhou Lin Cao ^*

• Nanjing Forestry University, Nanjing, Jiangsu Province, China

The phenotyping of plant roots is essential for improving the 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 Ginkgo tree root systems. The approach involves 3D reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter, length, surface area, and volume. To evaluate the proposed method, two approaches (Minimum Spanning Tree (MSTbased) and Triangulated Irregular Network (TIN-based)) were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with root diameter (RD) were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds (R 2 =0.99, RMSE=0.41 cm) outperformed the results of 3D models (MST-based: R 2 =0.71, RMSE=2.20 cm; TIN-based: R 2 =0.54, RMSE=2.80 cm). Additionally, MST-based model (F1=0.81) outperformed TIN-based model (F1=0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with different cloud parameter (CP), and the CP value of 0.002 (r=0.94, p < 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.