Scalable Early Detection of Plant-Pathogen Interactions with Airborne Imaging Spectroscopy: A Remote Sensing Approach
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Early detection of plant diseases is one of the most promising and challenging applications of imaging spectroscopy.This dissertation advances scalable approaches for the detection of plant disease with remote sensing, focusing on viral infections in vineyards in Lodi, California. Using high-resolution airborne imaging spectrometers such as AVIRIS-NG, this work demonstrates the feasibility of detecting asymptomatic Grapevine Leafroll-associated Virus 3 (GLRaV-3) infections in commercial vineyards. Beyond disease identification, I explore the utility of imaging spectroscopy in distinguishing red and white grape varietals and introduce cloud-native machine learning workflows, outlining a path for commercial deployment. These findings show the importance of linking physiological plant responses with spectral traits and evaluating models in spatial, temporal, and ecological contexts. Together, this work lays the foundation for scaling plant disease monitoring from the local to the regional level, offering a path toward data-driven agricultural management and early warning systems using next-generation remote sensing platforms.