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Title: An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

Abstract

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurementsmore » within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.« less

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Donald Danforth Plant Science Center, Saint Louis, MO (United States)
Publication Date:
Research Org.:
Donald Danforth Plant Science Center, Saint Louis, MO (United States); Univ. of Nebraska, Lincoln, NE (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC)
OSTI Identifier:
1483477
Grant/Contract Number:  
[AR0000594; SC0014395; SC0018072]
Resource Type:
Accepted Manuscript
Journal Name:
PeerJ
Additional Journal Information:
[ Journal Volume: 6]; Journal ID: ISSN 2167-8359
Publisher:
PeerJ Inc.
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 47 OTHER INSTRUMENTATION; Least-squares regression; Phenotyping; Image analysis; Large-scale biology; Image correction

Citation Formats

Berry, Jeffrey C., Fahlgren, Noah, Pokorny, Alexandria A., Bart, Rebecca S., and Veley, Kira M. An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping. United States: N. p., 2018. Web. doi:10.7717/peerj.5727.
Berry, Jeffrey C., Fahlgren, Noah, Pokorny, Alexandria A., Bart, Rebecca S., & Veley, Kira M. An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping. United States. doi:10.7717/peerj.5727.
Berry, Jeffrey C., Fahlgren, Noah, Pokorny, Alexandria A., Bart, Rebecca S., and Veley, Kira M. Thu . "An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping". United States. doi:10.7717/peerj.5727. https://www.osti.gov/servlets/purl/1483477.
@article{osti_1483477,
title = {An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping},
author = {Berry, Jeffrey C. and Fahlgren, Noah and Pokorny, Alexandria A. and Bart, Rebecca S. and Veley, Kira M.},
abstractNote = {High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.},
doi = {10.7717/peerj.5727},
journal = {PeerJ},
number = ,
volume = [6],
place = {United States},
year = {2018},
month = {10}
}

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