Potatoes are a vital global food crop, but they are often affected by diseases like early blight, which harms leaves and reduces yields. Farmers usually manage this disease through visual scouting and frequent fungicide use, which can be time-consuming, costly, and bad for the environment. Our study explores using airborne hyperspectral imaging, a technology that captures massive volumes of imagery beyond the visible wavelengths of light, to detect early blight in potato fields. Over five growing seasons in Wisconsin, we collected hyperspectral data and compared it with ground-based disease inspections. We found that this technology can reliably distinguish between healthy and diseased plants, offering a more efficient way to monitor early blight. Key spectral regions helped us identify early signs of infection, such as changes in water content and plant structure, before crown-dieback. While our models performed well with combined data from multiple years, they struggled when applied to new seasons, illustrating the need to continue development comprehensive data sets for crop monitoring. Overall, our findings highlight the potential of hyperspectral imaging for improving potato disease management, reducing reliance on chemicals, and supporting sustainable farming practices.