UAV remote sensing based estimation of green cover during turfgrass establishment

Abstract

Turfgrass is an important urban crop in the United States. Determining the percent green cover (PGC) to assess turfgrass quality/health and the rate of establishment is a crucial parameter for evaluating different species and experimental lines within species. However, evaluating the PGC of individual plots within large breeding nurseries in a conventional way, either visually or through digital image analysis is a time-consuming and laborious process. In the present study, we used the unmanned aerial vehicle (UAV) with multispectral and RGB sensors to estimate PGC during turfgrass establishment. We evaluated thirty approaches with different levels of complexity based on vegetation indices, supervised and unsupervised machine learning classification methods, and image processing methods for high-throughput turfgrass PGC estimation. An HSV (Hue-Saturation-Value) color space-based green pixel identification (GPI) method was introduced for the first time for estimating UAV derived PGC (UAVPGC). The results indicate that the GPI achieved the highest coefficient of determination, 0.86–0.96, with lowest mean absolute error when compared to ground percent green cover (GroundPGC). Overall, UAV-derived RGB image-based support vector machine methods were in agreement with GroundPGC (R2 = 0.88–0.95). This suggests that UAV-derived RGB images are adequate in accurately determining percent green cover (green vegetation within an experimental plot); however, multispectral images might offer a solution to determine turfgrass coverage (green and non-green vegetation within an experimental plot) during turfgrass establishment to account for non-green vegetation which is not captured by RGB (visible light spectrum) based estimation of PGC.