A Pilot Image Analysis Pipeline for Automated Leaf Morphology in Pithecellobium dulce

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 Pithecellobium dulce 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 (0.6700–0.699) 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.

Plant phenotyping relevance

葉形態を画像解析で自動・再現可能に定量化するパイプラインの開発が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

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