Unverified paper record
Unveiling Nature’s Architecture: Geometric Morphometrics as an Analytical Tool in Plant Biology
Plants · 5 Mar 2025 · 10.3390/plants14050808
Abstract
Geometric morphometrics (GMM) is an advanced morphometric method enabling quantitative analysis of shape and size variations in biological structures. Through high-resolution imaging and mathematical algorithms, GMM provides valuable insights into taxonomy, ecology, and evolution, making it increasingly relevant in plant science. This review synthesizes the existing literature and explores methodological details, research questions, and future directions, establishing a strong foundation for further study in plant biology. Following PRISMA 2020 guidelines, a rigorous literature search finally identified 83 studies for review. The review organized data on plant species, organs studied, GMM objectives, and methodological aspects, such as imaging and landmark positioning. Leaf and flower structures emerged as the most frequently analyzed organs, primarily in studies of shape variations. This review assesses the use of GMM in plant sciences, identifying knowledge gaps and inconsistencies, and suggesting areas for future research. By highlighting unaddressed topics and emerging trends, the review aims to guide researchers towards methodological challenges and innovations necessary for advancing the field.
Plant phenotyping relevance
植物器官の形状・サイズを画像と幾何学的形態計測で定量化する手法を中心に、植物科学での利用法と方法論的課題を体系的にレビューしている。
abstractGeometric morphometrics (GMM) is an advanced morphometric method enabling quantitative analysis of shape and size variations in biological structures.
abstractThis review assesses the use of GMM in plant sciences, identifying knowledge gaps and inconsistencies, and suggesting areas for future research.
Code and data availability
This is a review article on geometric morphometrics in plant biology. The authors state 'No new data were proposed with the present work.' The only supplement (Table S1) is a curated list of reviewed reports and collected metadata, not a phenotype dataset, image collection, analysis code, or trained model. No paper-own
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