Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems.

Plant Physiology
Anjali S Iyer-PascuzziPhilip N Benfey

Abstract

The ability to nondestructively image and automatically phenotype complex root systems, like those of rice (Oryza sativa), is fundamental to identifying genes underlying root system architecture (RSA). Although root systems are central to plant fitness, identifying genes responsible for RSA remains an underexplored opportunity for crop improvement. Here we describe a nondestructive imaging and analysis system for automated phenotyping and trait ranking of RSA. Using this system, we image rice roots from 12 genotypes. We automatically estimate RSA traits previously identified as important to plant function. In addition, we expand the suite of features examined for RSA to include traits that more comprehensively describe monocot RSA but that are difficult to measure with traditional methods. Using 16 automatically acquired phenotypic traits for 2,297 images from 118 individuals, we observe (1) wide variation in phenotypes among the genotypes surveyed; and (2) greater intergenotype variance of RSA features than variance within a genotype. RSA trait values are integrated into a computational pipeline that utilizes supervised learning methods to determine which traits best separate two genotypes, and then ranks the traits according ...Continue Reading

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Citations

May 28, 2013·Trends in Plant Science·Stijn DhondtDirk Inzé
Sep 27, 2012·Proceedings of the National Academy of Sciences of the United States of America·Jesse L SilverbergSharon J Gerbode
Jan 31, 2013·Proceedings of the National Academy of Sciences of the United States of America·Suqin FangPhilip N Benfey
Apr 13, 2013·Proceedings of the National Academy of Sciences of the United States of America·Christopher N ToppPhilip N Benfey
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