Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/101193
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Type: Journal article
Title: Quantifying the onset and progression of plant senescence by color image analysis for high throughput applications
Author: Cai, J.
Okamoto, M.
Atieno, J.
Sutton, T.
Li, Y.
Miklavcic, S.
Citation: PLoS One, 2016; 11(6):0157102-1-0157102-21
Publisher: Public Library of Science
Issue Date: 2016
ISSN: 1932-6203
1932-6203
Editor: Kalaitzis, P.
Statement of
Responsibility: 
Jinhai Cai, Mamoru Okamoto, Judith Atieno, Tim Sutton, Yongle Li, Stanley J. Miklavcic
Abstract: Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.
Keywords: Cicer
Triticum
Algorithms
Color
High-Throughput Screening Assays
Plant Development
Optical Imaging
Rights: Copyright: © 2016 Cai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI: 10.1371/journal.pone.0157102
Grant ID: ARC
http://purl.org/au-research/grants/arc/LP140100347
Appears in Collections:Agriculture, Food and Wine publications
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