[en] 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 analysed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic library to improve the estimation of several root system traits.
Overall, our analysis is a call to caution when using automatic root image analysis tools. If a thorough calibration is not performed on the dataset of interest, unexpected errors might arise, especially for large and complex root images. To facilitate such calibration, both the image library and the different codes used in the study have been made available to the community.
Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines
Publication date :
2017
Journal title :
Frontiers in Plant Science
eISSN :
1664-462X
Publisher :
Frontiers Research Foundation, Lausanne, Switzerland
Volume :
8
Pages :
447
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE] BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique
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