Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System...

Area of Science:

• Plant Biology
• Computational Biology
• Agricultural Science

Background:

• Root system architecture (RSA) is crucial for plant interaction with soil.
• Current RSA study methods face trade-offs between data precision and natural conditions.
• Functional-structural plant models (FSPMs) offer potential but require robust parameterization.

Purpose of the Study:

• To develop and evaluate a novel method for studying adventitious RSA in *Populus trichocarpa* stem cuttings.
• To integrate time-series image-based phenotyping with FSPMs for enhanced RSA analysis.
• To automate FSPM parameter extraction and simulation evaluation using RhizoVision software.

Main Methods:

• Utilized a germination paper system for high-resolution root growth imaging.
• Employed time-series image-based phenotyping of *Populus trichocarpa* stem cuttings.
• Applied RhizoVision software for automated FSPM parameter extraction and simulation evaluation.

Main Results:

• A significant correlation was found between root initiation timing and thermal time (P=0.0061, R²=0.875), but not strongly with RSA.
• Automated parameterization achieved high accuracy (83.5% sensitivity) for predicting 2D root growth.
• Accuracy decreased significantly for 3D growth prediction (38.5%–48.7% sensitivity).

Conclusions:

• The integrated phenotyping and FSPM approach enables high-throughput RSA studies.
• The method shows high accuracy in 2D growth prediction, with limitations in 3D.
• This work bridges advancements in time-series phenotyping and FSPM applications.