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Title: PlantCV v2: Image analysis software for high-throughput plant phenotyping

Abstract

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

Authors:
 [1];  [1];  [1];  [1];  [2];  [1];  [3];  [1];  [1];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12]
  1. Donald Danforth Plant Science Center, St. Louis, MO (United States)
  2. Donald Danforth Plant Science Center, St. Louis, MO (United States); Monsanto Company, St. Louis, MO (United States)
  3. Oklahoma State Univ., Stillwater, OK (United States). Dept. of Plant Biology, Ecology, and Evolution
  4. Donald Danforth Plant Science Center, St. Louis, MO (United States); Washington Univ., St. Louis, MO (United States). Computational and Systems Biology Program
  5. Donald Danforth Plant Science Center, St. Louis, MO (United States); Unidev, St. Louis, MO (United States)
  6. Arkansas State Univ., Jonesboro, AR (United States). Arkansas Biosciences Inst.; Univ. of Georgia, Athens, GA (United States). Dept. of Plant Biology
  7. Donald Danforth Plant Science Center, St. Louis, MO (United States); CiBO Technologies, Cambridge, MA (United States)
  8. Arkansas State Univ., Jonesboro, AR (United States). Arkansas Biosciences Inst., Dept. of Chemistry and Physics
  9. Donald Danforth Plant Science Center, St. Louis, MO (United States); Univ. of Nebraska, Lincoln, NE (United States). Dept. of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology
  10. Cosmos X, Tokyo (Japan)
  11. Donald Danforth Plant Science Center, St. Louis, MO (United States); Oklahoma State Univ., Stillwater, OK (United States). Dept. of Plant Biology, Ecology, and Evolution
  12. Missouri Univ. of Science and Technology, Rolla, MO (United States)
Publication Date:
Research Org.:
Donald Danforth Plant Science Center, St. 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)
Contributing Org.:
National Science Foundation (NSF)
OSTI Identifier:
1417015
Grant/Contract Number:  
AR0000594; SC0014395
Resource Type:
Accepted Manuscript
Journal Name:
PeerJ
Additional Journal Information:
Journal Volume: 5; Journal ID: ISSN 2167-8359
Publisher:
PeerJ Inc.
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Gehan, Malia A., Fahlgren, Noah, Abbasi, Arash, Berry, Jeffrey C., Callen, Steven T., Chavez, Leonardo, Doust, Andrew N., Feldman, Max J., Gilbert, Kerrigan B., Hodge, John G., Hoyer, J. Steen, Lin, Andy, Liu, Suxing, Lizárraga, César, Lorence, Argelia, Miller, Michael, Platon, Eric, Tessman, Monica, and Sax, Tony. PlantCV v2: Image analysis software for high-throughput plant phenotyping. United States: N. p., 2017. Web. doi:10.7717/peerj.4088.
Gehan, Malia A., Fahlgren, Noah, Abbasi, Arash, Berry, Jeffrey C., Callen, Steven T., Chavez, Leonardo, Doust, Andrew N., Feldman, Max J., Gilbert, Kerrigan B., Hodge, John G., Hoyer, J. Steen, Lin, Andy, Liu, Suxing, Lizárraga, César, Lorence, Argelia, Miller, Michael, Platon, Eric, Tessman, Monica, & Sax, Tony. PlantCV v2: Image analysis software for high-throughput plant phenotyping. United States. doi:10.7717/peerj.4088.
Gehan, Malia A., Fahlgren, Noah, Abbasi, Arash, Berry, Jeffrey C., Callen, Steven T., Chavez, Leonardo, Doust, Andrew N., Feldman, Max J., Gilbert, Kerrigan B., Hodge, John G., Hoyer, J. Steen, Lin, Andy, Liu, Suxing, Lizárraga, César, Lorence, Argelia, Miller, Michael, Platon, Eric, Tessman, Monica, and Sax, Tony. Fri . "PlantCV v2: Image analysis software for high-throughput plant phenotyping". United States. doi:10.7717/peerj.4088. https://www.osti.gov/servlets/purl/1417015.
@article{osti_1417015,
title = {PlantCV v2: Image analysis software for high-throughput plant phenotyping},
author = {Gehan, Malia A. and Fahlgren, Noah and Abbasi, Arash and Berry, Jeffrey C. and Callen, Steven T. and Chavez, Leonardo and Doust, Andrew N. and Feldman, Max J. and Gilbert, Kerrigan B. and Hodge, John G. and Hoyer, J. Steen and Lin, Andy and Liu, Suxing and Lizárraga, César and Lorence, Argelia and Miller, Michael and Platon, Eric and Tessman, Monica and Sax, Tony},
abstractNote = {Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.},
doi = {10.7717/peerj.4088},
journal = {PeerJ},
number = ,
volume = 5,
place = {United States},
year = {2017},
month = {12}
}

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