Jordan Booth1, David S. McCall2, Dana Sullivan3, Andrew Morgan4, Haseeb Chaudhry5 and Kevin Kochersberger5, (1)Plant Pathology, Physiology, and Weed Science, Virginia Tech (Graduate Student), Moseley, VA (2)Virginia Tech, Blacksburg, VA (3)Turf Scout, Greensboro, NC (4)Unmanned Systems Lab, Virginia Tech, Blacksburg, VA (5)Unmanned Systems Laboratory, Virginia Tech, Blacksburg, VA
Spring dead spot (SDS) is the most important disease of bermudagrass and its hybrids in regions where cold temperatures induce dormancy, yet suppression of the disease is often unreliable with current management strategies. Patches frequently reoccur in the same locations over multiple seasons, with some microclimates being more prone to SDS development than others. Unmanned aerial vehicles (UAV), commonly referred to as drones, have been utilized to anecdotally generate golf course imagery for an enhanced perspective of turfgrass conditions. Generating maps with UAV to document SDS epidemics may be useful for improving our understanding of disease epidemiology and control. Fungicide applications based on geographic severity can allow turf mangers to limit total treated acreage and reduce costs. Additionally, such maps may help turf managers identify underlying problems that contribute to SDS, therefore increasing the probability of successful management with cultural practices. This study investigates the use of UAV to quantify spring dead spot occurrence and isolate key management zones of the disease. In the spring of 2016, aerial imagery and ground-validation data were collected on six ‘Vamont’ bermudagrass fairways with a high SDS occurrence in Richmond, Virginia. Images were mosaicked and georectified with known ground references to create continuous coverage across fairways and overlaid with ground coordinates of existing SDS patches. Algorithms were developed to spatially detect and quantify disease incidence. These disease distribution maps will be used for future research to reduce total fungicide input through site-specific management and to help better understand the underlying edaphic variables that exacerbate SDS epidemics.