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Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet,...

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Autores principales: Zingaretti, Laura M., Monfort, Amparo, Pérez-Enciso, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139333/
https://www.ncbi.nlm.nih.gov/pubmed/34056620
http://dx.doi.org/10.34133/2021/9812910
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author Zingaretti, Laura M.
Monfort, Amparo
Pérez-Enciso, Miguel
author_facet Zingaretti, Laura M.
Monfort, Amparo
Pérez-Enciso, Miguel
author_sort Zingaretti, Laura M.
collection PubMed
description Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.
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spelling pubmed-81393332021-05-27 Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry Zingaretti, Laura M. Monfort, Amparo Pérez-Enciso, Miguel Plant Phenomics Research Article Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository. AAAS 2021-05-12 /pmc/articles/PMC8139333/ /pubmed/34056620 http://dx.doi.org/10.34133/2021/9812910 Text en Copyright © 2021 Laura M. Zingaretti et al. https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).
spellingShingle Research Article
Zingaretti, Laura M.
Monfort, Amparo
Pérez-Enciso, Miguel
Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry
title Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry
title_full Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry
title_fullStr Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry
title_full_unstemmed Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry
title_short Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry
title_sort automatic fruit morphology phenome and genetic analysis: an application in the octoploid strawberry
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139333/
https://www.ncbi.nlm.nih.gov/pubmed/34056620
http://dx.doi.org/10.34133/2021/9812910
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