Model-based plant phenomics on morphological traits using morphometric descriptors
Copyright © 2022 by JAPANESE SOCIETY OF BREEDING.
| Veröffentlicht in: | Breeding science. - 1998. - 72(2022), 1 vom: 01. März, Seite 19-30 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Breeding science |
| Schlagworte: | Journal Article morphometrics plant phenotyping theoretical morphology topological data analysis |
| Zusammenfassung: | Copyright © 2022 by JAPANESE SOCIETY OF BREEDING. The morphological traits of plants contribute to many important functional features such as radiation interception, lodging tolerance, gas exchange efficiency, spatial competition between individuals and/or species, and disease resistance. Although the importance of plant phenotyping techniques is increasing with advances in molecular breeding strategies, there are barriers to its advancement, including the gap between measured data and phenotypic values, low quantitativity, and low throughput caused by the lack of models for representing morphological traits. In this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits. Geometric morphometric approaches pave the way to a general-purpose method applicable to single units. Hierarchical structures composed of an indefinite number of multiple elements, which is often observed in plants, can be quantified in terms of their multi-scale topological characteristics using topological data analysis. Theoretical morphological models capture specific anatomical structures, if recognized. These morphological descriptors provide us with the advantages of model-based plant phenotyping, including robust quantification of limited datasets. Moreover, we discuss the future possibilities that a system of model-based measurement and model refinement would solve the lack of morphological models and the difficulties in scaling out the phenotyping processes |
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| Beschreibung: | Date Revised 07.09.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1344-7610 |
| DOI: | 10.1270/jsbbs.21078 |