North American migratory monarch butterfly (Danaus plexippus plexippus) populations have declined significantly over the past two decades prompting considerable conservation attention and listing by the IUCN Red List of Threatened Species as Endangered. Efforts to help reverse these declines have often focused on actions to sustain, enhance, restore, and increase suitable habitat across a broad range of different landscapes, including those managed by entities from energy and transportation sectors. The resulting Nationwide Candidate Conservation Agreement with Assurances for Monarch Butterfly on Energy and Transportation Lands promotes vegetation management practices that benefit monarchs. The availability of milkweed host resources is critical to support monarch breeding and spring recolonization. Building on ground truthed milkweed surveys, we explored the application of remote sensing from unoccupied aircraft systems (UAS, or drones) in conjunction with advanced machine learning techniques (deep learning) for automated detection of milkweed for long-term population monitoring along Florida Department of Transportation-managed roadsides. UAS imagery and video were collected at multiple sites, altitudes (10 - 25 m), and lighting conditions during the early bud and blooming stage peaks of the milkweed growing season in March and April 2022. Ground reference and validation data were collected by surveying plant locations and denoting growth stages with a high-precision GPS. Approximately 2,400 images were collected over milkweed sites, which were divided into sample pools for training and testing the deep learning models. Deep learning models (including RetinaNet and TridentNet) were trained and tested using the Annotation Interface for Data-driven Ecology (AIDE, v2.2) to label tiled images, refine the models through cycles of training and validation, and conduct predictions on test image sets for comparison with the ground validation data. The ultimate goal is to generate a trained deep learning model and workflow that can quickly identify and enumerate milkweed in areas of interest using images or video obtained from UAS.