In recent years, ground-penetrating radar (GPR) has become increasingly important as a nondestructive way to explore plant roots. Automatic recognition and localization of root objects from GPR images present a significant challenge. GPR images for the root system contain complicated hyperbolic signals that appear deformation depending on root size, orientation, aggregation degree, and soil background. This article presents a new deep learning approach, You Only Look Once v4 (YOLOv4)-hyperbola, which provides fully automatic recognition and localization of root objects from GPR images. YOLOv4-hyperbola improves the YOLOv4 architecture by introducing the keypoints detection branch in order to accurately locate roots while identifying them. The YOLOv4-hyperbola model was trained by combining field datasets and simulated datasets to simultaneously identify and locate hyperbolic features representing potential root objects across GPR images and evaluated on datasets of root detection from two experiments in the field. Compared with the randomized Hough transform (RHT) method, the proposed approach demonstrated higher accuracy and efficiency in root object detection on GPR images. YOLOv4-hyperbola was able to accurately recognize and locate abnormal hyperbolic signals caused by the complexity of the root system in nature. The validation on the two independent datasets showed that the proposed approach had good generalization and great application potential for real-time detection and location of roots over large areas in the field.

The diameter of roots is pivotal for studying subsurface root structure geometry. Yet, directly obtaining these parameters is challenging due their hidden nature. Ground-penetrating radar (GPR) offers a reproducible, nondestructive method for root detection, but estimating diameter from B-Scan images remains challenging. To address this, we developed the CycleGAN-guided multi-task neural network (CMT-Net). It comprises two subnetworks, YOLOv4-Hyperbolic Position and Diameter (YOLOv4-HPD) and CycleGAN. The YOLOv4-HPD is obtained by adding a regression header for predicting root diameter to YOLOv4-Hyperbola, which achieves the ability to simultaneously accurately locate root objects and estimate root diameter. The CycleGAN is used to solve the problem of the lack of a real root diameter training dataset for the YOLOv4-HPD model by migrating field-measured data domains to simulated data without altering root diameter information. We used simulated and field data to evaluate the model, showing its effectiveness in estimating root diameter. This study marks the first construction of a deep learning model for fully automatic root location and diameter extraction from GPR images, achieving an “Image Input–Parameter Output” end-to-end pattern. The model’s validation across various dataset scales opens the way for estimating other root attributes.