Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/87813
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Type: Journal article
Title: Root phenotyping by root tip detection and classification through statistical learning
Author: Kumar, P.
Huang, C.
Cai, J.
Miklavcic, S.
Citation: Plant and Soil: international journal on plant-soil relationships, 2014; 380(1-2):193-209
Publisher: Springer International Publishing
Issue Date: 2014
ISSN: 0032-079X
1573-5036
Statement of
Responsibility: 
Pankaj Kumar, Chunyuan Huang, Jinhai Cai, Stanley J. Miklavcic
Abstract: Aims: Root branching is a fundamental phenotypic property of a root system. In this paper we present a system for the fully automated detection and classification of root tips in root images obtained either by 2D flat bed scanning or by 3D digital camera imaging. With our system we aim to provide a robust, efficient and accurate means of phenotyping of roots. Methods: Structural information derived from image features such as root ends and root branches is utilised for the detection and classification processes. A statistical analysis based on training data sets of root tips and non-root tips is used to assign image features to one of three different classes: non-root tips, primary root tips and lateral root tips. The automated procedure is optimised to ensure as high true detection rate and low false detection rate as possible. Results: We apply the method to images of barley, rice, and corn roots taken either by 2D scanning of washed and cut roots or digital camera images of plant roots growing in a transparent medium. The results of our detection and classification procedure are validated by a comparison with manually labelled images for all three species. Our results are also compared to two established platforms, EZ-Rhizo and WinRHIZO. Finally, we demonstrate the utility of the statistical learning approach by quantifying root phenotypic properties of barley double haploid lines. Conclusions: The method of statistical learning of characteristic features is an accurate means of not only counting root numbers, but also discriminating between primary and lateral roots. The fully-automated procedure presented in this paper can be used reliably in high throughput situations to characterise quantitative phenotypic variation.
Keywords: Root phenotyping; Zernike moments; Machine learning; Gaussian mixture models; Primary and lateral roots
Rights: © Springer International Publishing Switzerland 2014
DOI: 10.1007/s11104-014-2071-3
Appears in Collections:Agriculture, Food and Wine publications
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