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Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that construc...

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Autores principales: Jubery, Talukder Z., Shook, Johnathon, Parmley, Kyle, Zhang, Jiaoping, Naik, Hsiang S., Higgins, Race, Sarkar, Soumik, Singh, Arti, Singh, Asheesh K., Ganapathysubramanian, Baskar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5243820/
https://www.ncbi.nlm.nih.gov/pubmed/28154570
http://dx.doi.org/10.3389/fpls.2016.02066
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author Jubery, Talukder Z.
Shook, Johnathon
Parmley, Kyle
Zhang, Jiaoping
Naik, Hsiang S.
Higgins, Race
Sarkar, Soumik
Singh, Arti
Singh, Asheesh K.
Ganapathysubramanian, Baskar
author_facet Jubery, Talukder Z.
Shook, Johnathon
Parmley, Kyle
Zhang, Jiaoping
Naik, Hsiang S.
Higgins, Race
Sarkar, Soumik
Singh, Arti
Singh, Asheesh K.
Ganapathysubramanian, Baskar
author_sort Jubery, Talukder Z.
collection PubMed
description Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use.
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spelling pubmed-52438202017-02-02 Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity Jubery, Talukder Z. Shook, Johnathon Parmley, Kyle Zhang, Jiaoping Naik, Hsiang S. Higgins, Race Sarkar, Soumik Singh, Arti Singh, Asheesh K. Ganapathysubramanian, Baskar Front Plant Sci Plant Science Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use. Frontiers Media S.A. 2017-01-19 /pmc/articles/PMC5243820/ /pubmed/28154570 http://dx.doi.org/10.3389/fpls.2016.02066 Text en Copyright © 2017 Jubery, Shook, Parmley, Zhang, Naik, Higgins, Sarkar, Singh, Singh and Ganapathysubramanian. http://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) or licensor 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
Jubery, Talukder Z.
Shook, Johnathon
Parmley, Kyle
Zhang, Jiaoping
Naik, Hsiang S.
Higgins, Race
Sarkar, Soumik
Singh, Arti
Singh, Asheesh K.
Ganapathysubramanian, Baskar
Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
title Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
title_full Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
title_fullStr Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
title_full_unstemmed Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
title_short Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
title_sort deploying fourier coefficients to unravel soybean canopy diversity
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5243820/
https://www.ncbi.nlm.nih.gov/pubmed/28154570
http://dx.doi.org/10.3389/fpls.2016.02066
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