Objective Definition of Rosette Shape Variation Using a Combined Computer Vision and Data Mining Approach

Awduron Sefydliadau
  • Anyela Camargo-Rodriguez(Awdur)
  • Dimitra Papadopoulou(Awdur)
    Aristotle University of Thessaloniki
  • Zoi Spyropoulou(Awdur)
    Aristotle University of Thessaloniki
  • Konstantinos Vlachonasios(Awdur)
  • John Doonan(Awdur)
  • Alan Gay(Awdur)
  • Hector Candela(Golygydd) (Golygydd)
Math Erthygl
Iaith wreiddiolSaesneg
Tudalennau (o-i)e96889
CyfnodolynPLoS One
Rhif y cyfnodolyn5
Dangosyddion eitem ddigidol (DOIs)
StatwsCyhoeddwyd - 07 Mai 2014
Arddangos ystadegau lawrlwytho
Gweld graff cysylltiadau
Fformatau enwi


Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.