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

PeerJ

article

Malia A. Gehan¹  Noah Fahlgren¹  Arash Abbasi¹  Jeffrey C. Berry¹  Steven T. Callen¹  Leonardo Chavez¹  Andrew N. Doust³  Max J. Feldman¹  Kerrigan B. Gilbert¹  John G. Hodge³  J. Steen Hoyer¹  Andy Lin¹  Suxing Liu⁶  César Lizárraga¹  Argelia Lorence⁹  Michael Miller¹  Eric Platon^1,11  Monica Tessman¹  Tony Sax^1,12 

[1] Donald Danforth Plant Science Center;Monsanto Company;Department of Plant Biology, Ecology, and Evolution, Oklahoma State University;Computational and Systems Biology Program, Washington University in St. Louis;Unidev;Arkansas Biosciences Institute, Arkansas State University;Department of Plant Biology, University of Georgia;CiBO Technologies;Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University;Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln;Cosmos X;Missouri University of Science and Technology

关键词: Plant phenotyping;    Image analysis;    Computer vision;    Machine learning;    Morphometrics;   

DOI  :  10.7717/peerj.4088

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY