Graph Based Automated Analysis for Plant Root Phenotyping
Nigel Lee, Hsiang Sing Naik · Iowa State University Digital Repository (Iowa State University) · 2014
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.