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

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

doi:10.7717/peerj.4088

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

Journal Article · 01 December 2017 · PeerJ

DOI: https://doi.org/10.7717/peerj.4088 · OSTI ID:1417015

Gehan, Malia A. ^[1]; Fahlgren, Noah ^[1]; Abbasi, Arash ^[1]; Berry, Jeffrey C. ^[1]; Callen, Steven T. ^[2]; Chavez, Leonardo ^[1]; Doust, Andrew N. ^[3]; Feldman, Max J. ^[1]; Gilbert, Kerrigan B. ^[1]; Hodge, John G. ^[3]; Hoyer, J. Steen ^[4]; Lin, Andy ^[5]; Liu, Suxing ^[6]; Lizárraga, César ^[7]; Lorence, Argelia ^[8]; Miller, Michael ^[9]; Platon, Eric ^[10]; Tessman, Monica ^[11]; Sax, Tony ^[12]

Donald Danforth Plant Science Center, St. Louis, MO (United States)
Donald Danforth Plant Science Center, St. Louis, MO (United States); Monsanto Company, St. Louis, MO (United States)
Oklahoma State Univ., Stillwater, OK (United States). Dept. of Plant Biology, Ecology, and Evolution
Donald Danforth Plant Science Center, St. Louis, MO (United States); Washington Univ., St. Louis, MO (United States). Computational and Systems Biology Program
Donald Danforth Plant Science Center, St. Louis, MO (United States); Unidev, St. Louis, MO (United States)
Arkansas State Univ., Jonesboro, AR (United States). Arkansas Biosciences Inst.; Univ. of Georgia, Athens, GA (United States). Dept. of Plant Biology
Donald Danforth Plant Science Center, St. Louis, MO (United States); CiBO Technologies, Cambridge, MA (United States)
Arkansas State Univ., Jonesboro, AR (United States). Arkansas Biosciences Inst., Dept. of Chemistry and Physics
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
Cosmos X, Tokyo (Japan)
Donald Danforth Plant Science Center, St. Louis, MO (United States); Oklahoma State Univ., Stillwater, OK (United States). Dept. of Plant Biology, Ecology, and Evolution
Missouri Univ. of Science and Technology, Rolla, MO (United States)

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.

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Research Organization:: Donald Danforth Plant Science Center, St. Louis, MO (United States); Univ. of Nebraska, Lincoln, NE (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC)

Contributing Organization:: National Science Foundation (NSF)

Grant/Contract Number:: AR0000594; SC0014395

OSTI ID:: 1417015

Journal Information:: PeerJ, Vol. 5; ISSN 2167-8359

Publisher:: PeerJ Inc.Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 121 works

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