Root Location and Root Diameter Estimation of Trees Based on Deep Learning and Ground-Penetrating Radar

Abstract

A full understanding of the growth and distribution of tree roots is conducive to guiding precision irrigation, fertilization, and other agricultural work during agricultural production. Detecting tree roots with a ground-penetrating radar is a repeatable detection method that does no harm to the earth surface and tree roots. In this research, a rapid and accurate automatic detection was conducted on hyperbolic waveforms formed by root targets in B-scan images based on YOLOv5s. Following this, the regions of interest containing target hyperbolas were generated. Three or more coordinate points on the hyperbola were selected according to the three-point fixed circle (TPFC) method to locate the root system and estimate the root diameter. The results show that the accuracy of hyperbola detection using YOLOv5s was 96.7%, the recall rate was 86.6%, and the detection time of a single image was only 13 ms. In the simulation image, the TPFC method was used to locate the root system and estimate the root diameter through three different frequency antennas (500 MHz, 750 MHz, and 1000 MHz). A more accurate result was obtained when the antenna frequency was 1000 MHz, with the average distance error of root system positioning being 3.17 cm, and the slope and R2 of the linear fitting result between the estimated root diameter and the actual one being 1.029 and 0.987, respectively. Verified by the pre-buried root test and wilderness field test, both root localization and root diameter estimation in our research were proved to gain good results and conform to the rules found in simulation experiments. Therefore, we believe that this method can quickly and accurately detect the root system, locate and estimate the root diameter, and provide a new perspective for the non-destructive detection of the root system and the three-dimensional reconstruction of the root system.

Plant phenotyping relevance

深層学習と地中レーダーを用いて樹木根系の位置と根径を推定する手法を開発・検証しており、植物形質の取得方法が研究の中心である。

Code and data availability

The paper describes GPR B-scan datasets (real and simulated), YOLOv5s training, and TPFC analysis, but the Data Availability Statement says data are only available on request and not publicly available. No public dataset, code, model, or supplement URL is provided.

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