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

Abstract

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 ∆T 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.

A Novel Method of Hyperbola Recognition in Ground Penetrating Radar (GPR) B-Scan Image for Tree Roots Detection

“…In Figure 1, an example of the synthetic model from simulation data generated by gprMax, its B-Scan image, and amplitude graph were shown. In this example, the domain is 0.6 m in depth and 2.5 m in length, the discretization of space in the x, y, and z directions is 0.002 m respectively as well as the radius of the roots is 0.01 m. The relative permittivity values of the domain and root are 6 and 12 [49][50][51]. As for the set-up for the GPR, the antenna frequency is 900 MHz and the sampling number is 512.…”

Estimation of Coarse Root System Diameter Based on Ground-Penetrating Radar Forward Modeling

“…It can be observed that the distance between the upper and lower hyperbolas increases as the diameter of the root increases, which represents the time interval (∆T) for the electromagnetic wave to pass through the root. This is because the propagation distance of electromagnetic waves at the root increases with size, resulting in a longer time between two reflected waves [63]. T 1 and T 2 can be represented by pixel values p 1 and p 2 in the picture after threshold processing, while ∆T can be represented by ∆p.…”

Training data augmentations for improving hyperbola recognition in ground penetrating radar B-scan image for tree roots detection

“…In this study, the simulation data had a crucial role in extending the diversity of the dataset. When generating simulated data, the parameter variables were controlled so that the GprMax software parameters remained consistent with the ground penetrating radar equipment parameters, where: the depth of the domain was 0.6 m, the lateral length was 6 m, the root had a radius from 0.01 to 0.035 m, the soil and root system have relative dielectric constants of 6 and 12 (Attia al Hagrey 2007; Liang et al 2021), respectively, and the sampling number was 512, the antenna frequency of the GPR setting was 900 MHz, and a total of 759 simulated images were generated.…”