Reconstruction of root systems in Cryptomeria japonica using root point coordinates and diameters

Reconstruction of Conifer Root Systems Mapped with Point Cloud Data Obtained by 3D Laser Scanning Compared with Manual Measurement

Three-dimensional (3D) root system architecture (RSA) is a predominant factor in anchorage failure in trees. Only a few studies have used 3D laser scanners to evaluate RSA, but they do not check the accuracy of measurements. 3D laser scanners can quickly obtain RSA data, but the data are collected as a point cloud with a large number of points representing surfaces. The point cloud data must be converted into a set of interconnected axes and segments to compute the root system traits. The purposes of this study were: (i) to propose a new method for easily obtaining root point data as 3D coordinates and root diameters from point cloud data acquired by 3D laser scanner measurement; and (ii) to compare the accuracy of the data from main roots with intensive manual measurement. We scanned the excavated root systems of two Pinus thunbergii Parl. trees using a 3D laser scanner and neuTube software, which was developed for reconstructing the neuronal structure, to convert the point cloud data into root point data for reconstructing RSA. The reconstruction and traits of the RSA calculated from point cloud data were similar in accuracy to intensive manual measurements. Roots larger than 7 mm in diameter were accurately measured by the 3D laser scanner measurement. In the proposed method, the root point data were connected as a frustum of cones, so the reconstructed RSAs were simpler than the 3D root surfaces. However, the frustum of cones still showed the main coarse root segments correctly. We concluded that the proposed method could be applied to reconstruct the RSA and calculate traits using point cloud data of the root system, on the condition that it was possible to model both the stump and ovality of root sections.

Preliminary Application of Ground-Penetrating Radar for Reconstruction of Root System Architecture in Moso Bamboo

Root system architecture (RSA) refers to the geometric features and topology of the root system. Ground-penetrating radar (GPR) is a possible method of RSA reconstruction. However, because the topology of the root system is not directly accessible by GPR, GPR-based reconstruction must be complemented by manual connection of root points, resulting in limited accuracy. In this study, we used both GPR and direct excavation to obtain 3D coordinates (XYZ coordinates) and diameters of moso bamboo rhizomes on an orthogonal grid. A score function for selecting the best-connected root points was developed using rhizome diameter, depth, extension angle, and measured line spacing, which was then used to recover the topology of discrete root points. Based on the recovered topology, the 3D RSA of the rhizomes was reconstructed using a smoothing function. Based on the excavation data, the reconstructed RSA was generally consistent with the measured RSA, with 78.13% of root points correctly connected. The reconstructed RSA based on GPR data thus provided a rough approximation of the measured RSA, with errors arising due to missing root points and rhizome displacement. The proposed algorithm for reconstructing 3D RSA further enriches the application of ground-penetrating radar to root detection.

Theoretical Development of Plant Root Diameter Estimation Based on GprMax Data and Neural Network Modelling

The in situ non-destructive quantitative observation of plant roots is difficult. Traditional detection methods are not only time-consuming and labor-intensive, but also destroy the root environment. Ground penetrating radar (GPR), as a non-destructive detection method, has great potential in the estimation of root parameters. In this paper, we use GprMax software to perform forward modeling of plant roots under different soil dielectric constants, and analyze the situation of plant roots with different dielectric constants and different root diameters under 1.5 GHz frequency antenna detection. Firstly, root systems with increasing diameter under different values of root and soil dielectric constant were scanned. Secondly, from the scanning results, two time points T1 and T2 of radar wave entering and penetrating the root system were defined, and the correlation between root diameter D and time interval ΔT between T1 and T2 was analyzed. Finally, the least square regression model and back propagation (BP) neural network model for root diameter parameter estimation were established, and the estimation effects of the two models were compared and evaluated. The research results show that the root diameter (12–48 mm) is highly correlated with the time interval. Given the dielectric constants of the root and soil, the prediction results of the two models are accurate, but the prediction result of the neural network model is more stable, and the residual between the predicted value and the actual value is mainly concentrated in the [−1.5 mm, 1.5 mm] range, as well as the average of prediction error percentage being 3.62%. When the dielectric constants of the root and soil are unknown, the accuracy of the prediction results of the two models is decreased, but the stability of the neural network model is still superior to the least squares model, and the residual error is mainly concentrated in the range of [−5.3 mm, 5.0 mm], the average of prediction error percentage is 10.19%. This study uses GprMax to simulate root system detection and reveals the theoretical potential of GPR technology for non-destructive estimation of root diameter parameters. It is also pointed out that in the field exploration process, if the dielectric constants of the root and soil in the experimental site are sampled and measured first, the prediction accuracy of the model for root diameter would be effectively improved. This research is based on simulation experiments, so further simulation followed by laboratory and field testing is warranted using non-uniform roots and soil.