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000828812 1001_ $$0P:(DE-Juel1)171180$$aLobet, Guillaume$$b0$$eCorresponding author
000828812 245__ $$aUsing a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines
000828812 260__ $$aLausanne$$bFrontiers Media88991$$c2017
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000828812 520__ $$aRoot system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, three levels of noise were created. This library was used to evaluate the accuracy and usefulness of several image descriptors classically used in root image analysis softwares. Our analysis highlighted that the accuracy of the different traits is strongly dependent on the quality of the images and the type, size, and complexity of the root systems analyzed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic library to improve the estimation of several root system traits. Overall, our analysis is a call to caution when using automatic root image analysis tools. If a thorough calibration is not performed on the dataset of interest, unexpected errors might arise, especially for large and complex root images. To facilitate such calibration, both the image library and the different codes used in the study have been made available to the community.
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000828812 7001_ $$0P:(DE-HGF)0$$aKoevoets, Iko T.$$b1
000828812 7001_ $$0P:(DE-HGF)0$$aNoll, Manuel$$b2
000828812 7001_ $$0P:(DE-HGF)0$$aMeyer, Patrick E.$$b3
000828812 7001_ $$0P:(DE-HGF)0$$aTocquin, Pierre$$b4
000828812 7001_ $$0P:(DE-HGF)0$$aPagès, Loïc$$b5
000828812 7001_ $$0P:(DE-HGF)0$$aPérilleux, Claire$$b6
000828812 773__ $$0PERI:(DE-600)2711035-7$$a10.3389/fpls.2017.00447$$gVol. 8$$p447$$tFrontiers in Functional Plant Ecology$$v8$$x1664-462X$$y2017
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