On-site detection for agriculture and food systems using DNA nanotechnology

Project Overview

Project Type: Graduate Student
Funds awarded in 2011: $12,705.00
Projected End Date: 12/31/2012
Grant Recipient: Cornell University
Region: Northeast
State: New York
Graduate Student:
Faculty Advisor:
Dr. Dan Luo
Cornell University
Faculty Advisor:
Dr. Keith Perry
Cornell University

Annual Reports


  • Fruits: grapes
  • Vegetables: cucurbits, tomatoes


  • Animal Production: animal protection and health, preventive practices, therapeutics
  • Crop Production: biological inoculants, tissue analysis
  • Pest Management: disease vectors, field monitoring/scouting
  • Production Systems: general crop production

    Proposal abstract:

    Infectious diseases of crops account for an estimated $33 billion dollars in damage in the US each year (Pimental 2005). Crops must be monitored for infection, but most current methods for pathogen detection require special training and expensive equipment, and are impractical for on-site use. Often diagnosis requires the sample to be shipped to a central laboratory, which is relatively slow and inefficient. In order to most effectively monitor our agriculture and food systems for crop pathogens, we propose to apply DNA nanotechnology to develop an on-site multiplexed detection technology for crop viruses. This detection system will be inexpensive, easy to use, and compatible with on-site testing.

    Project objectives from proposal:

    (1) Demonstrate we can detect crop pathogens with high sensitivity and specificity using our DNA nanobarcode technology. We will initially focus on the detection of two representative plant viruses, one with a DNA genome (tomato yellow leaf curl virus) and one with an RNA genome (cucumber mosaic virus). We will carry out these initial tests under controlled laboratory conditions.
    (2) Once we have demonstrated detection in the laboratory, we will translate this technology to a field-ready detection platform. We will combine the above detection method with sample preparation and signal readout components into an integrated device. We will demonstrate extraction and detection of model viruses from infected plant tissue and evaluate the accuracy and robustness of our test under real world operating conditions.

    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.