Graph Based Automated Analysis for Plant Root Phenotyping
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The Symposium provides undergraduates from all academic disciplines with an opportunity to share their research with the university community and other guests through conference-style oral presentations. The Symposium represents part of a larger effort of Iowa State University to enhance, support, and celebrate undergraduate research activity.
Though coordinated by the University Honors Program, all undergraduate students are eligible and encouraged to participate in the Symposium. Undergraduates conducting research but not yet ready to present their work are encouraged to attend the Symposium to learn about the presentation process and students not currently involved in research are encouraged to attend the Symposium to learn about the broad range of undergraduate research activities that are taking place at ISU.
The first Symposium was held in April 2007. The 39 students who presented research and their mentors collectively represented all of ISU's Colleges: Agriculture and Life Sciences, Business, Design, Engineering, Human Sciences, Liberal Arts and Sciences, Veterinary Medicine, and the Graduate College. The event has grown to regularly include more than 100 students presenting on topics that span the broad range of disciplines studied at ISU.
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There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits have not been used as selection criterion mainly due to the difficulty in measuring them, as well as their quantitative mode of inheritance. Seedling root traits offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. Currently no strong relationships between seedling and adult root traits have been established with the traits and tools available so far. To enable fast, efficient and accurate trait extraction from images, we developed a new software framework to capture various traits from a single image of seedling roots. This framework is based on the mathematical notion of converting images of roots into an equivalent graph. We used various mathematical algorithms to quantify the data from the images. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. Also, this framework has been used to analyze corn images to obtain numerical data. Therefore, this software framework is not limited to roots alone, but can also be used to analyze different types of images.