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

Author:

Gehan Malia A.1,Fahlgren Noah1,Abbasi Arash1,Berry Jeffrey C.1,Callen Steven T.12,Chavez Leonardo1,Doust Andrew N.3,Feldman Max J.1,Gilbert Kerrigan B.1,Hodge John G.3,Hoyer J. Steen14,Lin Andy15,Liu Suxing67,Lizárraga César18,Lorence Argelia9,Miller Michael110,Platon Eric11,Tessman Monica13,Sax Tony12

Affiliation:

1. Donald Danforth Plant Science Center, St. Louis, MO, United States of America

2. Monsanto Company, St. Louis, MO, United States of America

3. Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America

4. Computational and Systems Biology Program, Washington University in St. Louis, St. Louis, MO, United States of America

5. Unidev, St. Louis, MO, United States of America

6. Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States of America

7. Department of Plant Biology, University of Georgia, Athens, GA, United States of America

8. CiBO Technologies, Cambridge, MA, United States of America

9. Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, United States of America

10. Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln, NE, United States of America

11. Cosmos X, Tokyo, Japan

12. Missouri University of Science and Technology, Rolla, MO, United States of America

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.

Funder

Donald Danforth Plant Science Center

US National Science Foundation

US Department of Energy

US Department of Agriculture

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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