Difference between revisions of "NSF BREAD"

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Page for phenoapps
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The PhenoApps project will converge novel advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. User-friendly mobile apps for field-based high-throughput phenotyping that utilize novel image analysis algorithms will be built and deployed to model and extract plant phenotypes. To ensure both immediate, broad deployment and functionality on a diverse set of crops, breeder networks for cassava and wheat will be engaged, providing a diverse set of target plant phenotypes, environments, breeding programs and working cultures.
  
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==Summary==
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===Overview===
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Dramatic increases in the speed and ability to collect precision phenotypic data are needed to decipher plant genomes and accelerate plant breeding. Over the past decade, the availability of genomic data has exploded while the methods to collect phenotypes have made minimal advancements. This has led to a dramatic imbalance in data sets connecting genotype to phenotype and highlighting phenotyping as the remaining major bottleneck in plant breeding programs. This project will advance the field of 3D graphics and modeling, data mining, and deep learning through integration of simultaneous ground truth phenotypic measurements and imaging with mobile technology.
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By focusing on novel algorithms delivered through mobile apps, innovative phenotyping tools can be rapidly deployed through readily available and highly-penetrant mobile technology. This approach will enable rapid dissemination and broad usability. Collectively equipping thousands of breeders around the world with tools for rapid collection, processing and analysis of complex phenotypes will provide the foundation for increasing genetic gain that will ultimately result in improved productivity, food security, nutrition, and income of smallholder farmers and their families in developing countries.
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===Workflow===
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Add graphical app workflow (prelim data → algorithms → beta users → feedback and changes → implementation → beta test → release)
 
==Wheat==
 
==Wheat==
 
===Summary===
 
===Summary===
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==Progress==
 
==Progress==
 
===Field Book===
 
===Field Book===
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http://wheatgenetics.org/fieldbook
 
===1KK===
 
===1KK===
 
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http://wheatgenetics.org/1kk
 
==Notes==
 
==Notes==

Revision as of 01:34, 23 April 2016

The PhenoApps project will converge novel advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. User-friendly mobile apps for field-based high-throughput phenotyping that utilize novel image analysis algorithms will be built and deployed to model and extract plant phenotypes. To ensure both immediate, broad deployment and functionality on a diverse set of crops, breeder networks for cassava and wheat will be engaged, providing a diverse set of target plant phenotypes, environments, breeding programs and working cultures.

Summary

Overview

Dramatic increases in the speed and ability to collect precision phenotypic data are needed to decipher plant genomes and accelerate plant breeding. Over the past decade, the availability of genomic data has exploded while the methods to collect phenotypes have made minimal advancements. This has led to a dramatic imbalance in data sets connecting genotype to phenotype and highlighting phenotyping as the remaining major bottleneck in plant breeding programs. This project will advance the field of 3D graphics and modeling, data mining, and deep learning through integration of simultaneous ground truth phenotypic measurements and imaging with mobile technology.

By focusing on novel algorithms delivered through mobile apps, innovative phenotyping tools can be rapidly deployed through readily available and highly-penetrant mobile technology. This approach will enable rapid dissemination and broad usability. Collectively equipping thousands of breeders around the world with tools for rapid collection, processing and analysis of complex phenotypes will provide the foundation for increasing genetic gain that will ultimately result in improved productivity, food security, nutrition, and income of smallholder farmers and their families in developing countries.

Workflow

Add graphical app workflow (prelim data → algorithms → beta users → feedback and changes → implementation → beta test → release)

Wheat

Summary

Traits

Trait Priority Status
Spike count 0 None
Rust quantification 0 None
Seed size and shape 0 1KK
Leaf morphology 0 None
Plant architecture 0 None
Plant physiology 0 None

Cycle

Cassava

Summary

Traits

Cycle

Traits

Trait Priority Status
Root size and shape 0 1KK
Cassava mosaic disease 0 None
Cassava brown streak disease 0 None
Whitefly count 0 None
Leaf morphology 0 None
Plant architecture 0 None
Plant physiology 0 None

Progress

Field Book

http://wheatgenetics.org/fieldbook

1KK

http://wheatgenetics.org/1kk

Notes