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ShinyFruit: interactive fruit phenotyping software and its application in blackberry
INTRODUCTION: Horticultural plant breeding programs often demand large volumes of phenotypic data to capture visual variation in quality of harvested products. Increasing the throughput potential of phenomic pipelines enables breeders to consider data-hungry molecular breeding strategies such as gen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585260/ https://www.ncbi.nlm.nih.gov/pubmed/37868309 http://dx.doi.org/10.3389/fpls.2023.1182819 |
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author | Chizk, T. Mason Lee, Jackie A. Clark, John R. Worthington, Margaret L. |
author_facet | Chizk, T. Mason Lee, Jackie A. Clark, John R. Worthington, Margaret L. |
author_sort | Chizk, T. Mason |
collection | PubMed |
description | INTRODUCTION: Horticultural plant breeding programs often demand large volumes of phenotypic data to capture visual variation in quality of harvested products. Increasing the throughput potential of phenomic pipelines enables breeders to consider data-hungry molecular breeding strategies such as genome-wide association studies and genomic selection. METHODS: We present an R-based web application called ShinyFruit for image-based phenotyping of size, shape, and color-related qualities in fruits and vegetables. Here, we have demonstrated one potential application for ShinyFruit by comparing its estimates of fruit length, width, and red drupelet reversion (RDR) with ImageJ and analogous manual phenotyping techniques in a population of blackberry cultivars and breeding selections from the University of Arkansas System Division of Agriculture Fruit Breeding Program. RESULTS: ShinyFruit results shared a strong positive correlation with manual measurements for blackberry length (r = 0.96) and ImageJ estimates of RDR (r = 0.96) and significant, albeit weaker, correlations with manual RDR estimation methods (r = 0.62 - 0.70). Neither phenotyping method detected genotypic differences in blackberry fruit width, suggesting that this trait is unlikely to be heritable in the population observed. DISCUSSION: It is likely that implementing a treatment to promote RDR expression in future studies might strengthen the documented correlation between phenotyping methods by maximizing genotypic variance. Even so, our analysis has suggested that ShinyFruit provides a viable, open-source solution to efficient phenotyping of size and color in blackberry fruit. The ability for users to adjust analysis settings should also extend its utility to a wide range of fruits and vegetables. |
format | Online Article Text |
id | pubmed-10585260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105852602023-10-20 ShinyFruit: interactive fruit phenotyping software and its application in blackberry Chizk, T. Mason Lee, Jackie A. Clark, John R. Worthington, Margaret L. Front Plant Sci Plant Science INTRODUCTION: Horticultural plant breeding programs often demand large volumes of phenotypic data to capture visual variation in quality of harvested products. Increasing the throughput potential of phenomic pipelines enables breeders to consider data-hungry molecular breeding strategies such as genome-wide association studies and genomic selection. METHODS: We present an R-based web application called ShinyFruit for image-based phenotyping of size, shape, and color-related qualities in fruits and vegetables. Here, we have demonstrated one potential application for ShinyFruit by comparing its estimates of fruit length, width, and red drupelet reversion (RDR) with ImageJ and analogous manual phenotyping techniques in a population of blackberry cultivars and breeding selections from the University of Arkansas System Division of Agriculture Fruit Breeding Program. RESULTS: ShinyFruit results shared a strong positive correlation with manual measurements for blackberry length (r = 0.96) and ImageJ estimates of RDR (r = 0.96) and significant, albeit weaker, correlations with manual RDR estimation methods (r = 0.62 - 0.70). Neither phenotyping method detected genotypic differences in blackberry fruit width, suggesting that this trait is unlikely to be heritable in the population observed. DISCUSSION: It is likely that implementing a treatment to promote RDR expression in future studies might strengthen the documented correlation between phenotyping methods by maximizing genotypic variance. Even so, our analysis has suggested that ShinyFruit provides a viable, open-source solution to efficient phenotyping of size and color in blackberry fruit. The ability for users to adjust analysis settings should also extend its utility to a wide range of fruits and vegetables. Frontiers Media S.A. 2023-10-05 /pmc/articles/PMC10585260/ /pubmed/37868309 http://dx.doi.org/10.3389/fpls.2023.1182819 Text en Copyright © 2023 Chizk, Lee, Clark and Worthington https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Chizk, T. Mason Lee, Jackie A. Clark, John R. Worthington, Margaret L. ShinyFruit: interactive fruit phenotyping software and its application in blackberry |
title | ShinyFruit: interactive fruit phenotyping software and its application in blackberry |
title_full | ShinyFruit: interactive fruit phenotyping software and its application in blackberry |
title_fullStr | ShinyFruit: interactive fruit phenotyping software and its application in blackberry |
title_full_unstemmed | ShinyFruit: interactive fruit phenotyping software and its application in blackberry |
title_short | ShinyFruit: interactive fruit phenotyping software and its application in blackberry |
title_sort | shinyfruit: interactive fruit phenotyping software and its application in blackberry |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585260/ https://www.ncbi.nlm.nih.gov/pubmed/37868309 http://dx.doi.org/10.3389/fpls.2023.1182819 |
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