I-Corps: Translation Potential of a Nanosensor Platform for Detection of Molecular Markers Associated with Crop Diseases

This I-Corps project focuses on the development of a nanosensing technology that detects molecular markers associated with crop diseases and environmental contaminants. The project initially focuses on maize production. Farmers lose millions of dollars annually due to pest outbreaks and contaminants that go undetected until crops are severely affected. Existing detection tools are often too slow, too general, or unable to identify early-stage issues when the molecular markers indicating disease infestations are not abundant. This new technology aims to provide real-time, precise detection of changes in chemical signals in the field, allowing for earlier and more targeted detection of crops disease and intervention. For farmers operating under strict pesticide regulations, especially those in organic agriculture, this early detection capability is essential as they often rely on prevention rather than treatment to ensure crop health. By addressing a critical gap in early pest, contaminant, and disease detection, the technology holds promise for reducing economic losses and increasing the efficiency of crop management across the agricultural sector. This I Corps project utilizes experiential learning, coupled with a first-hand investigation of the industry ecosystem, to assess the translation potential of the technology. This solution is based on the development of engineered proteins that function as highly specific receptors for molecular markers associated with crop plagues and pollutants. Unlike conventional detection methods that lack specificity for organic crop applications, this approach employs machine learning algorithms to design nanoreceptors that distinguish between structurally similar compounds commonly found in organic farming environments. The nanoreceptors are integrated into an optical sensing system that operates through Förster Resonance Energy Transfer, using graphene oxide substrates to significantly enhance signal sensitivity. This enhancement enables the quantification of trace-level targets previously undetectable outside of laboratory conditions, offering actionable insights before visible symptoms on crops emerge. The platform supports real-time monitoring, delivering results within minutes and eliminating the delay associated with sample collection and lab-based analysis. The modular design of the sensing platform permits adaptation for different crops and target molecules by exchanging the machine learning algorithms, creating a flexible and scalable tool for precision agriculture beyond the initial application in maize. 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.