PFI-TT: Sensing Stress-Induced Chemical Fingerprints Emitted by Plants Using a Network of Electronic Noses

North Carolina State University, Raleigh NC

Investigators

Abstract

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project will be on enhancing food security, the environment, as well as health and workforce safety. The proposed project is the first step to make a remotely operable, networked, low-cost, fast, sensitive, and selective detection method that identifies plant diseases in field and storage settings. Plant diseases cause up to 30% yield loss worldwide on an annual basis. To preserve yield, farmers rely on fungicide application to protect their crops from pathogen infections. Several major pathogens of staple crops produce toxins that are extremely hazardous to humans and animals when ingested. In this project, Fusarium Head Blight in wheat and barley will be targeted. Successful commercialization of this technology may translate into higher crop yield, reduced environmental pollution, and improved conditions for human health. The proposed translational research and technology development project will advance the understanding of volatiles released by plants under various stress conditions and develop the required sensor technology and data processing techniques to detect specific plant volatomes to diagnose stress before visible symptoms. The PFI team will collect volatiles released from plants exposed to specific disease stress under controlled settings and will analyze the samples to identify the volatomic fingerprint for the types of stress with high impact, specifically Fusarium Head Blight in wheat caused by the fungus Fusarium graminearum. The PFI team will also improve the sensor design to enable differential testing (sample vs. clean air) capability in the field environment as well as increase the immunity of the sensor response to common environmental changes. Innovation in the fields of plant biology, chemistry, electrical engineering, and data science will be brought together to enable the successful commercialization of this technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.