Deep phenotyping: deep learning for temporal phenotype/genotype classification

被引:0
作者
Sarah Taghavi Namin
Mohammad Esmaeilzadeh
Mohammad Najafi
Tim B. Brown
Justin O. Borevitz
机构
[1] Australian National University,Research School of Biology
[2] Australian National University,Research School of Engineering and Computer Science
来源
Plant Methods | / 14卷
关键词
Deep learning; Temporal information; Deep features; Accession classification;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 172 条
[1]  
Rivers J(2015)Genomic breeding for food, environment and livelihoods Food Secur. 7 375-382
[2]  
Warthmann N(2014)Traitcapture: genomic and environment modelling of plant phenomic data Curr Opin Plant Biol. 18 73-79
[3]  
Pogson B(2016)1,135 genomes reveal the global pattern of polymorphism in Cell. 166 481-491
[4]  
Borevitz J(2014)Objective definition of rosette shape variation using a combined computer vision and data mining approach PLoS One. 9 e96889-2
[5]  
Brown T(2015)Advanced phenotyping and phenotype data analysis for the study of plant growth and development Front Plant Sci. 6 619-131
[6]  
Cheng R(2015)From image processing to computer vision: plant imaging grows up Funct Plant Biol. 42 1-102
[7]  
Sirault X(2015)Image analysis: the new bottleneck in plant phenotyping IEEE Signal Process Mag. 32 126-121
[8]  
Rungrat T(2014)Phenotyping and beyond: modelling the relationships between traits Curr Opin Plant Biol. 18 96-439
[9]  
Murray K(2016)Watching plants grow–a position paper on computer vision and IET Comput Vis. 11 113-124
[10]  
Trtilek M(2013)Cell to whole-plant phenotyping: the best is yet to come Trends Plant Sci. 18 428-991