Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis...

Area of Science:

• Plant biology
• Agricultural science
• Imaging technology

Background:

• Root system architecture (RSA) is crucial for plant water and nutrient uptake.
• RSA plasticity allows plants to adapt to diverse soil conditions, vital for developing stress-tolerant crops.
• Non-invasive X-ray computed tomography (CT) aids RSA evaluation, but tracking individual root growth over time from 3D images remains challenging.

Purpose of the Study:

• To develop a semi-automatic workflow for tracking individual root growth from time-series 3D X-ray CT images.
• To enable efficient analysis of root system architecture development and plasticity over time.

Main Methods:

• Developed a semi-automatic workflow named RSAtrace4D.
• Utilized 3D alignment of time-series RSA images via iterative closest point registration.
• Implemented backward prediction of vectorization based on root growth patterns.

Main Results:

• The workflow successfully tracked individual root growth in upland rice using 21 time-series CT volumes.
• Observed that root elongation speed increases with plant age.
• Reduced manual labor time by 95% compared to workflows without backward prediction.

Conclusions:

• RSAtrace4D efficiently generates time-series RSA vectors from time-series X-ray CT volumes.
• The workflow is suitable for analyzing RSA development and plasticity over time.
• This advancement supports research in crop improvement for enhanced stress tolerance.