Related Experiment Video
Updated: Apr 28, 2026
08:14
LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
Published on: January 21, 2013
28.4K
Procrustean pseudo-landmark methods in Python to measure massive quantities of leaf shape data
Asia Hightower1,2,3, Svea Hall1, Ricardo Urquidi Camacho4
1Department of Plant Biology Michigan State University East Lansing Michigan USA.
Applications in Plant Sciences
|April 27, 2026
Summary
This study introduces a user-friendly leaf shape analysis method using pseudo-landmarks and generalized Procrustes analysis. This tool enables detailed comparisons of leaf shapes across species and developmental stages.
Area of Science:
- Botany
- Computational Biology
- Morphometrics
Background:
- Comparing diverse leaf shapes and marginal points is challenging biologically and statistically.
- Existing methods may lack the precision for detailed shape analysis.
Purpose of the Study:
- To develop a user-friendly computational method for leaf shape analysis.
- To enable robust comparison of leaf shapes within and between species.
- To explore leaf shape variation across development and its relation to genetic diversity.
Main Methods:
- Developed a leaf shape analysis method using pseudo-landmarks and generalized Procrustes analysis.
- Utilized Jupyter Notebook and Python for implementation.
- Created a repository of real leaf data from eight experimental datasets.
Main Results:
- Pseudo-landmarks facilitate comprehensive leaf shape comparisons using dimension reduction (e.g., principal component analysis).
- Leaf shape transitions during development (phyllotaxy) can be mapped.
- Leaf shape variation can be linked to genetic diversity.
Conclusions:
- The generalized Procrustes analysis and pseudo-landmarks offer a powerful tool for leaf shape examination.
- This method is applicable across biological, ecological, evolutionary, and developmental scales.
