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Computer Vision Phenomics and Quantitative Genetics of Sweet Corn Ear Architecture and Fungal Disease Resistance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer Vision Phenomics and Quantitative Genetics of Sweet Corn Ear Architecture and Fungal Disease Resistance./
Author:	Gonzalez Garcia, Juan Manuel.
Description:	1 online resource (226 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Agriculture. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29063682click for full text (PQDT)
ISBN:	9798802700655

Computer Vision Phenomics and Quantitative Genetics of Sweet Corn Ear Architecture and Fungal Disease Resistance.
Gonzalez Garcia, Juan Manuel.

Computer Vision Phenomics and Quantitative Genetics of Sweet Corn Ear Architecture and Fungal Disease Resistance. - 1 online resource (226 pages)

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--University of Florida, 2022.

Includes bibliographical references

Computer vision, genomics, quantitative genetics, and big data analytics are transforming plant breeding by improving phenotyping methods and selection efficacy through the use of digital imaging, genetic markers, and predictive models. Sweet corn's proximity to row crops and similarity to vegetables make it an ideal model for translating digital tools to specialty crop and vegetable breeding. Herein, we develop computer vision-based tools to quantitatively phenotype ear architecture traits and disease resistance. Ear architecture traits in fresh market sweet corn play a key role in marketability for producers, distributors, and consumers. Resistance to Southern Corn Leaf Blight is increasingly important in subtropical regions where disease pressure is high and conventional chemical controls are not sustainable in the long term. We demonstrate the utility of computer vision tools by phenotypically characterizing a diverse sweet corn population. We leverage this phenomic dataset with a corresponding genomic dataset in an applied quantitative genetic analysis. This analysis enhances breeding strategies for complex traits in sweet corn by identifying elite inbreds, describing inheritance patterns and genetic correlations, identifying putative loci and genes associated with traits of interest, and building predictive models. Implementing digital breeding tools will efficiently increase genetic gain in sweet corn.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802700655Subjects--Topical Terms:

518588
Agriculture.
Subjects--Index Terms:

Computer VisionIndex Terms--Genre/Form:

542853
Electronic books.

Computer Vision Phenomics and Quantitative Genetics of Sweet Corn Ear Architecture and Fungal Disease Resistance.
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500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Resende, Marcio.
502 $a Thesis (Ph.D.)--University of Florida, 2022.
504 $a Includes bibliographical references
520 $a Computer vision, genomics, quantitative genetics, and big data analytics are transforming plant breeding by improving phenotyping methods and selection efficacy through the use of digital imaging, genetic markers, and predictive models. Sweet corn's proximity to row crops and similarity to vegetables make it an ideal model for translating digital tools to specialty crop and vegetable breeding. Herein, we develop computer vision-based tools to quantitatively phenotype ear architecture traits and disease resistance. Ear architecture traits in fresh market sweet corn play a key role in marketability for producers, distributors, and consumers. Resistance to Southern Corn Leaf Blight is increasingly important in subtropical regions where disease pressure is high and conventional chemical controls are not sustainable in the long term. We demonstrate the utility of computer vision tools by phenotypically characterizing a diverse sweet corn population. We leverage this phenomic dataset with a corresponding genomic dataset in an applied quantitative genetic analysis. This analysis enhances breeding strategies for complex traits in sweet corn by identifying elite inbreds, describing inheritance patterns and genetic correlations, identifying putative loci and genes associated with traits of interest, and building predictive models. Implementing digital breeding tools will efficiently increase genetic gain in sweet corn.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Agriculture. $3 518588
650 4 $a Agronomy. $3 2122783
650 4 $a Agricultural engineering. $3 3168406
650 4 $a Artificial intelligence. $3 516317
653 $a Computer Vision
653 $a Genome wide association study
653 $a Genomic selection
653 $a Phenomics
653 $a Quantitative genetics
653 $a Sweet corn
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of Florida. $b Plant Molecular and Cellular Biology. $3 3703261
773 0 $t Dissertations Abstracts International $g 83-12B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29063682 $z click for full text (PQDT)