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Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry
BACKGROUND: Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191992/ https://www.ncbi.nlm.nih.gov/pubmed/32352533 http://dx.doi.org/10.1093/gigascience/giaa030 |
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author | Feldmann, Mitchell J Hardigan, Michael A Famula, Randi A López, Cindy M Tabb, Amy Cole, Glenn S Knapp, Steven J |
author_facet | Feldmann, Mitchell J Hardigan, Michael A Famula, Randi A López, Cindy M Tabb, Amy Cole, Glenn S Knapp, Steven J |
author_sort | Feldmann, Mitchell J |
collection | PubMed |
description | BACKGROUND: Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis. RESULTS: We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape. CONCLUSIONS: Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops. |
format | Online Article Text |
id | pubmed-7191992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71919922020-05-07 Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry Feldmann, Mitchell J Hardigan, Michael A Famula, Randi A López, Cindy M Tabb, Amy Cole, Glenn S Knapp, Steven J Gigascience Research BACKGROUND: Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis. RESULTS: We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape. CONCLUSIONS: Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops. Oxford University Press 2020-04-30 /pmc/articles/PMC7191992/ /pubmed/32352533 http://dx.doi.org/10.1093/gigascience/giaa030 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Feldmann, Mitchell J Hardigan, Michael A Famula, Randi A López, Cindy M Tabb, Amy Cole, Glenn S Knapp, Steven J Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
title | Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
title_full | Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
title_fullStr | Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
title_full_unstemmed | Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
title_short | Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
title_sort | multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191992/ https://www.ncbi.nlm.nih.gov/pubmed/32352533 http://dx.doi.org/10.1093/gigascience/giaa030 |
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