ArticlePlant methods2021
4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Integrating 3D phenotyping and functional-structural plant models for crop ideotype breeding.Nature communications · 2026Review
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- High-throughput plant phenotyping identifies and discriminates biotic and abiotic stresses in tomato.Plant phenomics (Washington, D.C.) · 2025Article
- Non-Invasive Phenotyping of Sugar Beet and Maize Roots Using Field-Scale Spectral Electrical Impedance Tomography.Plant, cell & environment · 2025Article
- Multiscale Mathematical Modeling in Systems Biology: A Framework to Boost Plant Synthetic Biology.Plants (Basel, Switzerland) · 2025Review
- Local mapping of root orientation traits by X-ray micro-CT and 3d image analysis: A study case on carrot seedlings grown in simulated vs real weightlessness.Plant methods · 2024Article
- Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray.Plant methods · 2023Article
- Quantification of the three-dimensional root system architecture using an automated rotating imaging system.Plant methods · 2023Article
- High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings.Plant methods · 2022Article
- Recent advances in methods forPeerJ · 2022Review
- 4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography.Frontiers in plant science · 2022Article
- The Invisible Frontline: High-Tech Root Imaging for Crop Stress Adaptation.Physiologia plantarumReview
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Authors and funding
6 authors.
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Abstract
backgroundBreakthrough 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).
resultsRoots 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
conclusionsThe 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.
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