PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

, , , , , , , , , , , , , , , , , ,
1 Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America
2 Current Address: Monsanto Company, St. Louis, Missouri, United States of America
3 Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, Oklahoma, United States of America
4 Computational and Systems Biology Program, Washington University in St. Louis, St. Louis, Missouri, United States of America
5 Current Address: Unidev, St. Louis, Missouri, United States of America
6 Arkansas Biosciences Institute, Arkansas State University, Jonesboro, Arkansas, United States of America
7 Current Address: Department of Plant Biology, University of Georgia, Athens, Georgia, United States of America
8 Current Address: CiBO Technologies, Cambridge, Massachusetts, United States of America
9 Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Jonesboro, Arkansas, United States of America
10 Current Address: Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln, Nebraska, United States of America
11 Cosmos X, Tokyo, Japan
12 Missouri University of Science and Technology, Rolla, Missouri, United States of America
DOI
10.7287/peerj.preprints.3225v1
Published
Accepted
Subject Areas
Agricultural Science, Bioinformatics, Computational Biology, Plant Science, Data Mining and Machine Learning
Keywords
plant phenotyping, image analysis, computer vision, machine learning, morphometrics
Copyright
© 2017 Gehan et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T. 2017. PlantCV v2.0: Image analysis software for high-throughput plant phenotyping. PeerJ Preprints 5:e3225v1

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 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.

Author Comment

This is a preprint submission to PeerJ Preprints. We intend to submit this preprint to PeerJ for peer review.