4D root trait analysis platform for carbon sequestration modeling in corn using x-ray computed tomography

Overview

This solution leverages X-Ray Computed Tomography (CT) to generate digital twins of corn root systems, enabling precise quantification of root architectural traits linked to soil organic carbon sequestration. Corn is one of the world's most widely produced crops, with annual production exceeding 700 million tons, making it a critical target for carbon farming initiatives. By accurately measuring variation in root systems, breeders and agronomists can select for favorable root characteristics—particularly rooting depth—that enhance the ability of crops to capture and store atmospheric carbon in soil. The platform addresses an urgent need for advanced computational methods to quantify root dynamics-based carbon sequestration at scale.

Technical specifications

Imaging and data collection:

• X-Ray CT proximal sensing generates 3D root digital twins from corn samples
• Experimental design covers 5 corn samples across 8 genotypes at 4 phenotypic stages
• Scans capture spatial-temporal root architecture through repeated imaging over time

Root trait extraction:

• Automated extraction of key architectural traits including elongation rate, root number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots
• A spatial-temporal root architectural model processes the imagery into structured trait datasets
• Extracted traits are validated against on-field measurements for accuracy

Carbon sequestration modeling:

• A numerical model links extracted root traits to quantitative carbon sequestration estimates
• Previous validation on tomato and corn roots achieved a coefficient of determination of 0.84 (P < 0.001) between model-computed volume and root digital twin measurements

Technology readiness level

The underlying 4D structural root architecture modeling approach has been validated and published, demonstrating strong correlation between model outputs and digital twin measurements. Current work focuses on extending the platform specifically to carbon sequestration quantification in corn, including new dataset generation and model codification. Future validation will be conducted at the Phenotyping Facility at Purdue University, comparing digital computed root traits against field measurements across multiple corn genotypes. The full study is projected to take approximately 6 months to complete. The research team is actively seeking industry partnership to fund key personnel and material costs for this next phase of development.