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United States Department of Agriculture

Agricultural Research Service

Research Project: USING GENETIC DIVERSITY OF IMPROVE QUANTITATIVE DISEASE RESISTANCE AND AGRONOMIC TRAITS OF CORN Title: PhenoPhyte: A flexible affordable method to quantify visual 2D phenotypes

Authors
item Green, Jason -
item Appel, Heidi -
item Rehrig, Erin -
item Harnsomburana, Jaturon -
item Chang, Jai-Fu -
item Chintamanani, Satya -
item Balint-Kurti, Peter
item Shyu, Chi-Ren -

Submitted to: Plant Physiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 31, 2012
Publication Date: November 6, 2012
Citation: Green, J., Appel, H., Rehrig, E., Harnsomburana, J., Chang, J., Chintamanani, S., Balint Kurti, P.J., Shyu, C. 2012. PhenoPhyte: A flexible affordable method to quantify visual 2D phenotypes. Plant Physiology. 8:45.

Interpretive Summary: Scoring (or quantifying) certain plant traits, like growth, disease resistance, herbivory etc., is quite difficult to do in an efficient and objective way. Here we describe an automated image analysis system that allows for relatively fast and accurate scoring of complex traits.

Technical Abstract: Identification of altered phenotypes in plants through forward or reverse genetics is critical to assigning biological functions to genes. Although techniques for measuring and characterizing mutant phenotypes have been developed, most are not cost effective or are too imprecise or subjective to reliably identify subtler differences in complex traits like growth, color change, or defense responses. To address this issue, we designed an imaging protocol that facilitates automatic quantification of 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 lab and field and can be used in less than optimal imaging conditions due to customizable, automated color and scale normalization. The protocol was used with Arabidopsis thaliana, Brassica rapa, Glycine max, and Zea mays to measure a variety of traits including growth, leaf area, herbivory, and disease resistance. We show that digital phenotyping can reduce human subjectivity in expression measurements to increase accuracy and improve the precision of phenotypic measurements, which are crucial for differentiating mutants and understanding gene function. We provide examples of the use of this method to characterize a variety of phenotypic differences and discuss other potential applications of this approach.

Last Modified: 12/21/2014
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