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2000
Volume 4, Issue 1
  • ISSN: 2210-299X
  • E-ISSN: 2210-3007
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Abstract

Introduction

Leaf morphology is vital for plant identification, but traditional methods are subjective and inconsistent.

Methods

This pilot study presents an image analysis pipeline for leaves using ImageJ and MATLAB. Steps included grayscale conversion, Sobel/Canny edge detection, GLCM texture analysis, and SSIM comparison.

Results

Canny edge detection showed higher edge density than Sobel. Texture metrics were consistent, and SSIM scores (0.6700d-0.699) indicated high structural similarity among leaves.

Discussion

Canny edge detection captured finer venation than Sobel, while GLCM and SSIM confirmed strong structural similarity among leaves. The pipeline demonstrated reproducible, objective, and scalable quantification of leaf morphology, reducing observer bias and enabling automated phenotyping.

Conclusion

The pipeline offers reproducible, objective leaf analysis, reducing bias and supporting applications in taxonomy and digital phenotyping.

© 2026 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-11-26
2026-03-08

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A Pilot Image Analysis Pipeline for Automated Leaf Morphology in Pithecellobium dulce
Curr. Indian Sci. 4, E2210299X414735 (2026); https://doi.org/10.2174/012210299X414735251119065127
/content/journals/cis/10.2174/012210299X414735251119065127
/content/journals/cis/10.2174/012210299X414735251119065127
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  • Article Type:     Research Article
Keyword(s): Computational morphology; Digital botany; GLCM; Leaf analysis; Objective phenotyping; SSIM

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