3D Modeling Beneath Ground: Plant Root Detection and Reconstruction Based on Ground-Penetrating Radar

Abstract

3D object reconstruction based on deep neural networks has been gaining attention in recent years. However, recovering 3D shapes of hidden and buried objects remains to be a challenge. Ground Penetrating Radar (GPR) is among the most powerful and widely used instruments for detecting and locating underground objects such as plant roots and pipes, with affordable prices and continually evolving technology. This paper first proposes a deep convolution neural network-based anchor-free GPR curve signal detection net- work utilizing B-scans from a GPR sensor. The detection results can help obtain precisely fitted parabola curves. Furthermore, a graph neural network-based root shape reconstruction network is designated in order to progressively recover major taproot and then fine root branches’ geometry. Our results on the gprMax simulated root data as well as the real-world GPR data collected from apple orchards demonstrate the potential of using the proposed framework as a new approach for fine-grained underground object shape reconstruction in a non-destructive way.