Computer vision and machine learning for robust phenotyping in genome-wide studies
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Computer vision and machine learning for robust phenotyping in genome-wide studies
Authors
Keywords
-
Journal
Scientific Reports
Volume 7, Issue 1, Pages -
Publisher
Springer Nature
Online
2017-03-08
DOI
10.1038/srep44048
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Machine Learning for High-Throughput Stress Phenotyping in Plants
- (2016) Arti Singh et al. TRENDS IN PLANT SCIENCE
- Uncovering the novel characteristics of Asian honey bee, Apis cerana, by whole genome sequencing
- (2015) Doori Park et al. BMC GENOMICS
- Genomic Selection for Quantitative Adult Plant Stem Rust Resistance in Wheat
- (2015) Jessica E. Rutkoski et al. Plant Genome
- Genome-Wide Association Studies Identifies Seven Major Regions Responsible for Iron Deficiency Chlorosis in Soybean (Glycine max)
- (2014) Sujan Mamidi et al. PLoS One
- Accelerating the Switchgrass (Panicum virgatum L.) Breeding Cycle Using Genomic Selection Approaches
- (2014) Alexander E. Lipka et al. PLoS One
- Genomewide Selection when Major Genes Are Known
- (2013) Rex Bernardo CROP SCIENCE
- High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis
- (2013) Céline Rousseau et al. Plant Methods
- Mutually Exclusive Alterations in Secondary Metabolism Are Critical for the Uptake of Insoluble Iron Compounds by Arabidopsis and Medicago truncatula
- (2013) J. Rodriguez-Celma et al. PLANT PHYSIOLOGY
- Feruloyl-CoA 6'-Hydroxylase1-Dependent Coumarins Mediate Iron Acquisition from Alkaline Substrates in Arabidopsis
- (2013) N. B. Schmid et al. PLANT PHYSIOLOGY
- GAPIT: genome association and prediction integrated tool
- (2012) Alexander E. Lipka et al. BIOINFORMATICS
- Identification of Candidate Genes Underlying an Iron Efficiency Quantitative Trait Locus in Soybean
- (2012) G. A. Peiffer et al. PLANT PHYSIOLOGY
- Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
- (2011) Jeffrey B. Endelman Plant Genome
- Genome-Wide Association Analysis Identifies Candidate Genes Associated with Iron Deficiency Chlorosis in Soybean
- (2011) Sujan Mamidi et al. Plant Genome
- Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean
- (2010) J. Vollmann et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging
- (2010) C. H. Bock et al. CRITICAL REVIEWS IN PLANT SCIENCES
- Establishing a soybean germplasm core collection
- (2010) Marcelo F. Oliveira et al. FIELD CROPS RESEARCH
- Genome-wide association studies of 14 agronomic traits in rice landraces
- (2010) Xuehui Huang et al. NATURE GENETICS
- A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals
- (2009) Brian L. Browning et al. AMERICAN JOURNAL OF HUMAN GENETICS
- Iron Uptake and Transport in Plants: The Good, the Bad, and the Ionome
- (2009) Joe Morrissey et al. CHEMICAL REVIEWS
- Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle
- (2009) J. Berni et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Visual Rating and the Use of Image Analysis for Assessing Different Symptoms of Citrus Canker on Grapefruit Leaves
- (2008) C. H. Bock et al. PLANT DISEASE
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started