Quantifying turfgrass color using digital image analysis

Color is a major component of the aesthetic quality of turf and often evaluated in field studies. Digital image analysis may be an improved, objective method to quantify turf color. Studies were conducted to determine if digital image analysis with SigmaScan software (SPSS, Chicago, IL) was capable of. (i) accurately determining the hue, saturation, and brightness (HSB) levels of Munsell Plant Tissue color chips, (ii) quantifying visual color differences among zoysiagrass (Zoysia japonica Steud.) and creeping bentgrass (Agrostis palustris Huds. [= A. stolonifera var palustris (Huds.) Farw.]) plots receiving various N treatments, and (iii) quantifying genetic color differences among bermudagrass (Cynodon spp.) cultivars. Digital images of turf plots were analyzed with SigmaScan software to determine average HSB levels for each image. A dark green color index (DGCI) was created from HSB values for direct comparison with visual ratings. Digital image analysis accurately quantified the HSB levels (r(2) = 0.99, 0.96, and 0.97, respectively) of Munsell color chips corresponding to turf colors. Significant HSB differences were present among N treatments in creeping bentgrass, while only significant hue differences existed in zoysiagrass. Significant hue and saturation differences were present among bermudagrass cultivars. There was strong agreement between DGCI values and visual ratings. The relative variances of the HSB and DGCI were significantly less than the variance associated with multiple raters. This evaluation technique may facilitate objective comparisons of turf color across researchers, locations, and years when images are collected under equal lighting conditions (i.e., the use of an artificial light source at night or in an enclosed system).