4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

Abstract

Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Recent advances in methods for in situ root phenotyping

“… Shao et al (2021) generated highly precise 3D models of maize root crowns via CT and created computations pipelines that could measure 71 features from each sample. Herrero-Huerta et al (2021) developed a spatial-temporal root architecture modeling method based on CT, enabling the extraction of key root traits, including root number, length, angle, diameter, and volume of lateral roots. However, the application of CT technology is limited because it requires expensive equipment, and there are limits on the soil volume that can be scanned ( Morris et al, 2017 ).…”

4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography

“…It encloses all the noise coming from the ground-root segmentation process. The 4DRoot methodology is already validated with ground truth volume's measurements using the digital root scan (Herrero-Huerta et al, 2021), while the focus of this study is to introduce the fully automatic 4DRoot software. Figure 5 shows the overlapping between the cylindrical model against the CT scan, colored depending on the ramification order, and how the lateral roots are detected.…”

“…Major global challenges such as climate change, environmental degradation, and food insecurity demand cost-effective phenotyping methods to guarantee the fiber, fuel, and food necessities. Recently, image-based phenotyping has become an integral part of plant science analysis, noninvasively providing large volumes of data specifying plant architecture (Mairhofer et al, 2016;Gerth et al, 2021;Meline et al, 2021). Still, innovative digital approaches that may potentially increase the usability of breakthrough imaging technologies to potentially overcome the above challenges are urgently needed (McGrail et al, 2020).…”

High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings

“…Automatic extraction of RSA is a current bottleneck and an active topic in bio-image analysis. The community attempts to develop solutions to process root systems 2D images (Lobet et al, 2011;Yasrab et al, 2019;Takahashi, 2021), 3D CT observations (Mairhofer et al, 2016;Gaggion et al, 2021;Herrero-Huerta et al, 2021), or multi-angle 3D reconstructions (Symonova et al, 2015); see (Ndour et al 2017) for a detailed description of standard root system phenotyping setups. The proposed processing pipelines for reconstructing RSA from 2D or 3D images typically include a step of image segmentation (root detection and background removal) and a step of RSA reconstruction by optimal path search in the segmented structures (Yasrab et al, 2019;Möller et al 2021).…”

“…Automatic extraction of RSA is a current bottleneck and an active topic in bio-image analysis. The community attempts to develop solutions to process root systems 2D images (Lobet et al ., 2011; Yasrab et al ., 2019; Takahashi, 2021), 3D CT observations (Mairhofer et al, 2016; Gaggion et al ., 2021; Herrero-Huerta et al, 2021), or multi-angle 3D reconstructions (Symonova et al ., 2015); see (Ndour et al . 2017) for a detailed description of standard root system phenotyping setups.…”