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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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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
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Summary
This summary is machine-generated.

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.

Keywords:
Procrustesleafmorphometricspseudo‐landmarksshape

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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.