Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines.

ABSTRACT: Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, three levels of noise were created. This library was used to evaluate the accuracy and usefulness of several image descriptors classically used in root image analysis softwares. Our analysis highlighted that the accuracy of the different traits is strongly dependent on the quality of the images and the type, size, and complexity of the root systems analyzed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic l

SUBMITTER: Lobet G 

PROVIDER: S-EPMC5376626 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature