Model-based plant phenomics on morphological traits using morphometric descriptors.

Area of Science:

• Plant Science
• Computational Biology
• Biotechnology

Background:

• Plant morphological traits are crucial for functions like radiation interception, gas exchange, and disease resistance.
• Advancements in molecular breeding necessitate improved plant phenotyping techniques.
• Current phenotyping methods face challenges like data-to-phenotype gaps, low quantitativity, and low throughput due to a lack of suitable models.

Purpose of the Study:

• To introduce and review morphological descriptors for plant phenotyping.
• To address the limitations in current plant phenotyping methodologies.
• To highlight model-based approaches for robust and scalable plant trait quantification.

Main Methods:

• Geometric morphometrics for single-unit analysis.
• Topological data analysis for quantifying multi-scale topological characteristics of hierarchical plant structures.
• Theoretical morphological models for specific anatomical structures.

Main Results:

• Morphological descriptors offer a model-based approach to plant phenotyping.
• These descriptors enable robust quantification even with limited datasets.
• Geometric morphometrics and topological data analysis provide new quantitative avenues.

Conclusions:

• Model-based plant phenotyping using advanced descriptors can overcome current limitations.
• Future systems integrating model-based measurement and refinement can enhance phenotyping scalability and model availability.
• These advancements are vital for accelerating molecular breeding strategies.