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RootGraph: a graphic optimization tool for automated image analysis of plant roots
Author(s) -
Jinhai Cai,
Zhanghui Zeng,
Jason N. Connor,
Chun Huang,
Vanessa Melino,
Pankaj Kumar,
Stanley J. Miklavcic
Publication year - 2015
Publication title -
journal of experimental botany
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.616
H-Index - 242
eISSN - 1460-2431
pISSN - 0022-0957
DOI - 10.1093/jxb/erv359
Subject(s) - robustness (evolution) , root (linguistics) , computer science , software , artificial intelligence , throughput , process (computing) , focus (optics) , plant roots , graph , plant root , data mining , pattern recognition (psychology) , theoretical computer science , botany , biology , telecommunications , philosophy , biochemistry , linguistics , physics , gene , optics , wireless , programming language , operating system , horticulture
This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions.

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