TY  - JOUR
AU  - Lobet, Guillaume
AU  - Koevoets, Iko T.
AU  - Noll, Manuel
AU  - Meyer, Patrick E.
AU  - Tocquin, Pierre
AU  - Pagès, Loïc
AU  - Périlleux, Claire
TI  - Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines
JO  - Frontiers in Functional Plant Ecology
VL  - 8
SN  - 1664-462X
CY  - Lausanne
PB  - Frontiers Media88991
M1  - FZJ-2017-02668
SP  - 447
PY  - 2017
AB  - 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 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000398172200001
C6  - pmid:28421089
DO  - DOI:10.3389/fpls.2017.00447
UR  - https://juser.fz-juelich.de/record/828812
ER  -