Underground Plant Exploration: Non-Destructive 3D Root Assessment with GPR Based on Point Graph Neural Network

This paper introduces a novel application of machine learning in agriculture for non-destructive 3D root structure reconstruction. Plant roots are critical for providing resources for the entire plant. Ground Penetrating Radar (GPR) is a key tool for identifying subterranean objects with easy and obvious shapes, such as large pipes, but remaining challenging to assess the 3D shapes of roots. In our study, we introduce a novel approach specifically designed based on GPR signal shape priors to detect target signals and perform curve parameter regression based on multiple B-scans from GPR. This process enables the derivation of a precise curve from the detection and regression outcomes. To achieve the reconstruction of a comprehensive 3D root structure, we have developed a shape reconstruction network that processes sparse sliced 3D points through a dedicated point graph network and an upsampling network module. Our method has been rigorously trained and validated using synthetic 3D root datasets and GPR data simulated by gprMax, as well as real GPR data.