Email updates

Keep up to date with the latest news and content from Plant Methods and BioMed Central.

Open Access Highly Accessed Methodology

PhenoPhyte: a flexible affordable method to quantify 2D phenotypes from imagery

Jason M Green1, Heidi Appel27*, Erin MacNeal Rehrig3, Jaturon Harnsomburana1, Jia-Fu Chang4, Peter Balint-Kurti5 and Chi-Ren Shyu6

Author Affiliations

1 Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA

2 Division of Plant Sciences, University of Missouri, Columbia, MO, 65211, USA

3 Biology/Chemistry Department, Fitchburg State University, Fitchburg, MA, 01420, USA

4 Informatics Institute, University of Missouri, Columbia, MO, 65211, USA

5 Department of Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA

6 Informatics Institute & Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA

7 371 Bond Life Sciences Center, Columbia, MO, 65211, USA

For all author emails, please log on.

Plant Methods 2012, 8:45  doi:10.1186/1746-4811-8-45

Published: 6 November 2012

Abstract

Background

Accurate characterization of complex plant phenotypes is critical to assigning biological functions to genes through forward or reverse genetics. It can also be vital in determining the effect of a treatment, genotype, or environmental condition on plant growth or susceptibility to insects or pathogens. Although techniques for characterizing complex phenotypes have been developed, most are not cost effective or are too imprecise or subjective to reliably differentiate subtler differences in complex traits like growth, color change, or disease resistance.

Results

We designed an inexpensive imaging protocol that facilitates automatic quantification of two-dimensional visual phenotypes using computer vision and image processing algorithms applied to standard digital images. The protocol allows for non-destructive imaging of plants in the laboratory and field and can be used in suboptimal imaging conditions due to automated color and scale normalization. We designed the web-based tool PhenoPhyte for processing images adhering to this protocol and demonstrate its ability to measure a variety of two-dimensional traits (such as growth, leaf area, and herbivory) using images from several species (Arabidopsis thaliana and Brassica rapa). We then provide a more complicated example for measuring disease resistance of Zea mays to Southern Leaf Blight.

Conclusions

PhenoPhyte is a new cost-effective web-application for semi-automated quantification of two-dimensional traits from digital imagery using an easy imaging protocol. This tool’s usefulness is demonstrated for a variety of traits in multiple species. We show that digital phenotyping can reduce human subjectivity in trait quantification, thereby increasing accuracy and improving precision, which are crucial for differentiating and quantifying subtle phenotypic variation and understanding gene function and/or treatment effects.

Keywords:
Herbivory; Pathogens; Genetic variation; Digital phenotyping