The ability to non-destructively 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 non-destructive imaging and analysis system for automated phenotyping and trait ranking of RSA.

SVM Analysis of Root Traits

In the first year of this project we have focused on developing a pipeline for analysis of 2D root images that is both flexible and efficient. Images are pre-processed with a variety of techniques and then analyzed using a set of automated computational tools that extract (i) geometric; (ii) network; and (iii) topological information from the image and build upon prior methods to characterize RSA traits.

Using a non-invasive imaging system, the Benfey lab has acquired images of a variety of rice cultivars. These include both temperate and tropical Japonicas as well as Indicas. Visual inspection of images from individuals from each genotype revealed striking similarities in the root architecture within each genotype and equally striking differences between genotypes.

Images of roots of different genotypes of rice