Effective monitoring and management of plant diseases is essential to grape production, particularly in cool climate viticultural areas where concurrent pest and disease pressures are common. This dissertation investigated multi-modal remote sensing tools for detecting and monitoring grapevine downy mildew (GDM) and grapevine leafroll-associated virus 3 (GLRaV-3) in research and commercial vineyards in New York State. The study began with an evaluation of high-resolution commercial satellite imagery for GDM surveillance. Random forest models trained on spectral bands and vegetation indices (VIs) successfully classified areas of high and low GDM incidence and severity, achieving maximum accuracies of 0.85 with SkySat (30cm resolution) and 0.92 with PlanetScope (3m resolution). Significant differences between VIs of high- and low-damage classes were not observed until late July. Cloud cover, image co-registration, and limited spectral resolution were identified as major challenges to operational satellite-based GDM monitoring. Uncrewed aerial systems (UAS) were subsequently examined as an alternate platform for consistent, high-resolution monitoring. Using a pathology research vineyard and a hemp breeding trial as test systems, an image processing pipeline was developed to generate multispectral orthomosaic time series, perform crop segmentation, and extract plant-level spectral data. A convolutional neural network and a vision foundation model produced the most accurate segmentations, with mean intersection-over-union values of 0.85 and 0.95 for vineyard and hemp imagery, respectively. Applying each model to the opposite crop dataset reduced segmentation accuracy, underscoring the need for adaptable, modular workflows for UAS-based analyses in specialty crops. To evaluate hyperspectral UAS imagery for detecting co-occurring diseases, paired UAS-ground surveys were conducted at two commercial Finger Lakes vineyards. Partial least squares regression models trained on visible-to-near infrared imagery detected GLRaV-3 symptoms in the presence of GDM, with F1 scores of 0.69-0.72 for symptomatic vines and highest model performance observed post-veraison. Models differentiating GDM and GLRaV-3 achieved accuracies of 0.55-0.82. Although GDM severity did not correlate with model uncertainty, reduced canopy size significantly increased PLSR prediction variance. Together, these investigations advanced development and implementation of optical remote sensing tools to provide grape growers with efficient, reliable means of vineyard disease monitoring.