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A practical introduction to skeletons for the plant sciences.

Alexander Bucksch¹

¹School of Biology and School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia 30332 USA.

Applications in Plant Sciences

|September 10, 2014

Summary

Digital imaging now allows detailed plant network analysis, crucial for understanding plant traits, breeding, and evolution. This study offers best practices for skeletonization to accurately measure plant architecture from images.

Keywords:

Reeb graph branching structure leaf venation medial axis plant network root skeletonization tree crown

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Area of Science:

Plant biology
Computer vision
Bioinformatics

Background:

Manual measurement of plant architecture was laborious before digital imaging.
Digital imaging has revolutionized the capture of plant network structures like roots and leaf venation.
Plant network traits derived from images link to genetics, physiology, and evolutionary studies.

Purpose of the Study:

To bridge the gap between skeletonization literature and plant science applications.
To provide best practices for deriving plant network diameters and branching hierarchies.
To enhance the accurate measurement of plant architecture using digital images.

Main Methods:

Utilizing digital imaging to capture plant network structures.
Applying skeletonization techniques as shape descriptors for plant networks.
Developing methods for deriving diameters and approximating branching hierarchies from skeletons.

Main Results:

Established best practices for skeletonization in plant science.
Improved methods for quantifying geometric traits of plant networks.
Facilitated more accurate analysis of plant architecture from imaging data.

Conclusions:

Accurate skeletonization is key to unlocking the potential of digital imaging for plant science.
Standardized methods will advance plant breeding, evolutionary studies, and growth simulations.
This work supports robust quantitative analysis of plant morphology.