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

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          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.

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          Most cited references34

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Python for Scientific Computing

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              The NumPy array: a structure for efficient numerical computation

              In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                1 December 2017
                2017
                : 5
                : e4088
                Affiliations
                [1 ]Donald Danforth Plant Science Center , St. Louis, MO, United States of America
                [2 ]Department of Plant Biology, Ecology, and Evolution, Oklahoma State University , Stillwater, OK, United States of America
                [3 ]Computational and Systems Biology Program, Washington University in St. Louis , St. Louis, MO, United States of America
                [4 ]Arkansas Biosciences Institute, Arkansas State University , Jonesboro, AR, United States of America
                [5 ]Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University , Jonesboro, AR, United States of America
                [6 ]Cosmos X , Tokyo, Japan
                [7 ]Missouri University of Science and Technology , Rolla, MO, United States of America
                [8 ] Current affiliation:  Monsanto Company , St. Louis, MO, United States of America
                [9 ] Current affiliation:  Unidev , St. Louis, MO, United States of America
                [10 ] Current affiliation:  Department of Plant Biology, University of Georgia , Athens, GA, United States of America
                [11 ] Current affiliation:  CiBO Technologies , Cambridge, MA, United States of America
                [12 ] Current affiliation:  Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln , Lincoln, NE, United States of America
                Article
                4088
                10.7717/peerj.4088
                5713628
                29209576
                b0f161a4-b958-416b-9b4e-def4f173a12c
                ©2017 Gehan et al.

                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) and either DOI or URL of the article must be cited.

                History
                : 7 September 2017
                : 3 November 2017
                Funding
                Funded by: Donald Danforth Plant Science Center
                Funded by: US National Science Foundation
                Award ID: IIA-1430427
                Award ID: IIA-1430428
                Award ID: IIA-1355406
                Award ID: IOS-1202682
                Award ID: MCB-1330562
                Award ID: DBI-1156581
                Funded by: US Department of Energy
                Award ID: DE-AR0000594
                Award ID: DE-SC0014395
                Funded by: US Department of Agriculture
                Award ID: MOW-2012-01361
                Award ID: 2016-67009-25639
                This work was supported by the Donald Danforth Plant Science Center, the US National Science Foundation (IIA-1430427, IIA-1430428, IIA-1355406, IOS-1202682, MCB-1330562, and DBI-1156581), the US Department of Energy (DE-AR0000594, DE-SC0014395), and the US Department of Agriculture (MOW-2012-01361 and 2016-67009-25639). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Agricultural Science
                Bioinformatics
                Computational Biology
                Plant Science
                Data Mining and Machine Learning

                plant phenotyping,image analysis,computer vision,machine learning,morphometrics

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