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

• Published in: Plant methods 2021-12, Vol.17 (1), p.123-123, Article 123
• Main Authors: Herrero-Huerta, Monica, Meline, Valerian, Iyer-Pascuzzi, Anjali S, Souza, Augusto M, Tuinstra, Mitchell R, Yang, Yang
• Format: Article
• Language: English
• Subjects: 3D modeling; Agricultural production; agricultural productivity; Algorithms; Architecture; Automation; Computed tomography; Computer applications; Computer architecture; corn; CT imaging; Digital imaging; Digital twins; Elongation; Experiments; Gene mapping; Imaging; Methods; phenotype; Phenotyping; Physiological aspects; Plant sciences; Proximal sensing; root growth; Root system architecture (RSA); root systems; Roots; Roots (Botany); Sensors; Skeleton; Structure; Time series; Tomatoes; Tomography; viability; X-radiation
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-021-00819-1

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). 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 (R.sup.2) of 0.84 and a P < 0.001. 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.