SBIR Phase II: Novel Spectroscopy for the Early Detection of Crop Afflictions

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is focused on the continued development of the first high-throughput assessment tool of crop health based on hyperspectral imagery that is suitable for vehicle-mounted field deployment. This technology will support crop growers in making data-driven decisions for efficient water and fertilizer management and in the control of crop diseases. By enabling early and accurate diagnosis of crop stress—crucial for the timely and targeted use of amendments, irrigation, and crop protection—this technology supports farmers in making data driven decisions, reducing crop losses from disease and other afflictions. As a result, farmers benefit from improved yields and lower input costs, including reduced use of fungicides and fertilizers. For consumers, the proposed technology can lead to increased availability of healthier produce by reducing the use of fungicides and improving the economic viability of small farms. The intellectual merit of this project centers around a dual-detector system that overcomes the tradeoff between spectral versus spatial resolution currently faced by existing optical scanning technology by sensing a single spectrum representative of the average signal across an entire image. This system has been adapted into an embeddable, portable spectrometer that combines fast, calibrated, non-contact data and control systems with artificial intelligence models to enable instantaneous in-field diagnosis. The proposed Phase II work will integrate this portable device and the associated detection algorithms with a mountable rugged hyperspectral camera for motion-based analysis and a reporting dashboard into a complete commercial solution. This will be accomplished through the expansion and refinement of the portable system hardware and the development of a vehicle-mounted hyperspectral camera system. Additionally, to enable deployment in the viticulture sector, a comprehensive data collection and modeling framework designed to address the complexities of multi-variety viticulture disease detection will be developed. 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.