Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications.

Clicks: 247
ID: 41450
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #1,317 of 2,056 articles by views in PloS one

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 2,056 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
cai2016quantifyingplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cai, Jinhai;Okamoto, Mamoru;Atieno, Judith;Sutton, Tim;Li, Yongle;Miklavcic, Stanley J;
Journal PloS one
Year 2016
DOI
10.1371/journal.pone.0157102
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.