ArticleGigaScience2018
Arabidopsis phenotyping through geometric morphometrics.
Article in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Application of geometric morphometrics for variety identification inFrontiers in plant science · 2026Article
- Unveiling Nature's Architecture: Geometric Morphometrics as an Analytical Tool in Plant Biology.Plants (Basel, Switzerland) · 2025Review
- Article
- Encapsulation Reduces the Deleterious Effects of Salicylic Acid Treatments on Root Growth and Gravitropic Response.International journal of molecular sciences · 2022Article
- Model-based plant phenomics on morphological traits using morphometric descriptors.Breeding science · 2022Article
- Identification of multiple novel genetic mechanisms that regulate chilling tolerance in Arabidopsis.Frontiers in plant science · 2022Article
- Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.GigaScience · 2020Article
- High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages inPlants (Basel, Switzerland) · 2020Article
- Morphological Analysis of Size and Shape (MASS): An integrative software program for morphometric analyses of leaves.Applications in plant sciences · 2019Article
- Arabidopsis phenotyping through geometric morphometrics.GigaScience · 2018Article
- Digital morphometrics: Application of MorphoLeaf in shape visualization and species delimitation, using Cucurbitaceae leaves as a model.Applications in plant sciencesArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units. Results: Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed. Conclusions: Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.