418-28 Evaluating Small Unmanned Aerial Systems for Detecting Turfgrass Stress with an Emphasis on Drought.

Poster Number 812

See more from this Division: C05 Turfgrass Science
See more from this Session: Turfgrass Science: II

Wednesday, November 18, 2015
Minneapolis Convention Center, Exhibit Hall BC

Dale J. Bremer1, Deon van der Merwe2, Jack D. Fry3, Steven J. Keeley1, Jared A Hoyle3 and Megan M. Kennelly4, (1)Department of Horticulture, Forestry and Recreation Resources, Kansas State University, Manhattan, KS
(2)Department of Diagnostic Medicine/Pathobiology, Kansas State University, Manhattan, KS
(3)Department of Horticulture and Natural Resources, Kansas State University, Manhattan, KS
(4)Department of Plant Pathology, Kansas State University, Manhattan, KS
Abstract:
A critical challenge facing the turfgrass industry is increasingly limited water supplies that may result in intermittent to extended periods without irrigation. Recent advances in technology may offer the potential to use small unmanned aircraft systems (sUAS) as management tools for turfgrass. This project evaluated a combination of sUAS and remote sensing techniques to detect drought stress and other management issues in turfgrass. Our objectives included: 1) measuring spectral reflectance (e.g., normalized difference vegetation index; NDVI) and percentage green cover of turfgrass across a gradient of irrigation regimes, from overwatered to severe deficit irrigation, and comparing measurements between traditional (handheld) and sUAS techniques; and 2) measuring spectral reflectance with sUAS on a golf course during the growing season to assess turfgrass health and diagnose potential patterns of turfgrass stress. For objective 1, six irrigation treatments were applied to induce a gradient of drought stress symptoms in the turfgrass, including 25, 50, 75, 100, 125, and 150% evapotranspiration (ET) replacement. Weekly to biweekly, spectral reflectance and percentage green cover were measured and visual quality estimated. Objective 2 was a case study to evaluate the utility of using sUAS to diagnose plant health and patterns of stress in turfgrass on functioning golf courses. Selected holes were flown with sUAS to measure the NDVI of the greens, fairways, and roughs during the hottest, most stressful period of summer and again during more optimal periods in the fall. Using remote sensing techniques with sUAS detected patterns of stress that resulted from abiotic, biotic, and cultural management stresses.

See more from this Division: C05 Turfgrass Science
See more from this Session: Turfgrass Science: II