Title :
Image Analysis for Mapping Immeasurable Phenotypes in Maize [Life Sciences]
Author :
Shyu, Chi-Ren ; Green, Jason M. ; Lun, Daniel P K ; Kazic, Toni ; Schaeffer, Mary ; Coe, Ed
Author_Institution :
Missouri-Columbia Univ., Columbia, MO
fDate :
5/1/2007 12:00:00 AM
Abstract :
This work will allow bio-informaticians to analyze the ever-increasing gene sequence data, discover valuable knowledge in maize biology and related plant; development, and understand subtle variations among different phenotypes. Furthermore, successful measuring of visual phenotypes will advance plant research by finding the genes and/or environmental factors that cause a given visual phenotype. In what follows, the field of plant genetics is introduced (particularly quantitative trait loci and disease scoring) to the signal processing community, discuss the challenges involved, and present an image analysis system for precisely quantifying and mapping immeasurable phenotypes in maize
Keywords :
biology computing; crops; genetics; image sequences; gene sequence data; image analysis; maize biology; mapping immeasurable phenotypes; plant genetics; signal processing; visual phenotype; Bioinformatics; Couplings; Crops; Diseases; Environmental factors; Genetics; Genomics; Image analysis; Image sequence analysis; Ontologies;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2007.361609