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The Cinderella discipline: morphometrics and their use in botanical classification

Christodoulou, M. D. ORCID: https://orcid.org/0000-0003-0968-2311, Clark, J. Y. and Culham, A. ORCID: https://orcid.org/0000-0002-7440-0133 (2020) The Cinderella discipline: morphometrics and their use in botanical classification. Botanical Journal of the Linnean Society. ISSN 0024-4074

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To link to this item DOI: 10.1093/botlinnean/boaa055

Abstract/Summary

Between the 1960s and the present day, the use of morphology in plant taxonomy suffered a major decline, in part driven by the apparent superiority of DNA-based approaches to data generation. However, in recent years computer image recognition has re-kindled the interest in morphological techniques. Linear or geometric morphometric approaches have been employed to distinguish and classify a wide variety of organisms; each has strengths and weaknesses. Here we review these approaches with a focus on plant classification and present a case for the combination of morphometrics with statistical/machine learning. There is a large collection of classification techniques available for biological analysis and selecting the most appropriate one is not trivial. Performance should be evaluated using standardised metrics such as accuracy, sensitivity, and specificity. The gathering and storage of high-resolution images, combined with the processing power of desktop computers, makes morphometric approaches practical as a time- and cost-efficient way of non-destructive identification of plant samples.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
Central Services > Academic and Governance Services > University Museums and Special Collections
ID Code:91282
Uncontrolled Keywords:Plant taxonomy, geometric morphometrics, linear morphometrics, statistical learning, machine learning, identification, classification, neural networks.
Publisher:Oxford University Press

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