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TURFGRASS SCIENCE Quantifying Turfgrass Color Using Digital Image Analysis Douglas E. Karcher* and Michael D. Richardson ABSTRACT

on subjective data is debatable (Karcher, 2000) as the data tend to be discrete and ordinal rather than continuous. Timely quantification of turfgrass color that uses readily accessible equipment would strengthen the validity of study results without adding significant burden to the evaluation process. Several techniques have been used to objectively measure turf color, including reflectance measurements (Birth and McVey, 1968), chlorophyll and amino acid analysis (Johnson, 1973; Nelson and Sosulski, 1984), and comparison with standardized colors (Beard, 1973). All of these methods have certain disadvantages compared with subjective color ratings. Reflectance, chlorophyll, and amino acid measurements all require relatively expensive equipment, and transport of samples to a laboratory for analysis. In addition, correlations between color and chlorophyll or amino acid measurements are either species or cultivar dependent. The use of standardized charts to measure turf color is effective, but results in qualitative descriptions of color that are not possible to statistically analyze with traditional ANOVA techniques. Recently, Landschoot and Mancino (2000) demonstrated that the color of creeping bentgrass cultivars could be successfully quantified with a colorimeter. Values from the colorimeter were significantly correlated with visual color assessments averaged across five evaluators. Other researchers have successfully used colorimeters to evaluate varying turf color due to seasonal changes (Kimura et al., 1989) or differences among cultivars and genetic lines (Thorogood et al., 1993). Although promising, a potential shortcoming of the colorimeter used in those studies is the relatively small measurement area (⬍20 cm2). In the absence of extremely uniform surface conditions, numerous subsample measurements with the colorimeter would be necessary to accurately represent the color of typical turfgrass field plots. In recent years, digital photography has become a common and affordable means for the scientific community to document and present images. Digital cameras, in conjunction with image analysis software, are being used to quantify wheat (Triticum aestivum L.) senescence (Adamsen et al., 1999) and canopy coverage in wheat (Lukina et al., 1999) and soybeans [Glycine max L. (Merr.)] (Purcell, 2000). Recently, digital image analysis was used to quantify turf coverage with increased precision over more traditional evaluation methods

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 (r2 ⫽ 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).

T

urf color is a key component of aesthetic quality and a good indicator of water and nutrient status (Beard, 1973). Therefore, color is often evaluated in turfgrass experiments. Color is traditionally evaluated by visually rating turf plots on a scale of 1 to 9, with 1 representing yellow or brown turf and 9 representing optimal, dark green turf. Although color ratings provide quick data acquisition without the need for specialized equipment, they are a subjective measure from which human bias is difficult to remove. As a result, inconsistencies often exist among raters when evaluating the same turf plots. Relatively poor correlations existed among experienced researchers (r ⬍ 0.68) when rating the same turf plots for density, color, and leaf spot (Skogley and Sawyer, 1992; Horst et al., 1984). Correlations this low would probably be considered unacceptable when using other evaluation tools (e.g., balances, spectrometers, pH meters) to measure the same turf sample. Furthermore, the applicability of standard ANOVA procedures and traditional means separation tests Dep. of Horticulture, Univ. of Arkansas, 308 Plant Sci. Building, Fayetteville, AR 72701. Received 4 April 2002. *Corresponding author ([email protected]).

Abbreviations: AS, ammonium sulfate; DAT, days after treatment; DGCI, dark green color index; HSB, hue, saturation, and brightness; NTEP, National Turfgrass Evaluation Program; PCU, polymer-coated urea; RGB, red, green, and blue; SCU, sulfur-coated urea.

Published in Crop Sci. 43:943–951 (2003).

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CROP SCIENCE, VOL. 43, MAY–JUNE 2003

(Richardson et al., 2001). Through digital photography, researchers can instantaneously obtain millions of bits of information on a relatively large turfgrass canopy. For example, a digital image taken of a turf plot using a 1280 ⫻ 960 pixel resolution contains 1 228 800 pixels, with each pixel containing independent color information about the turf plot. Therefore, digital photography and subsequent image analysis may be capable of quantifying turfgrass color in field experiments. The information contained in a digital image includes the amount of red, green, and blue (RGB) light emitted for each pixel in the image. Although it may be intuitive to use the green levels of the RGB information to quantify the green color of an image, the intensity of red and blue will confound how green an image appears. To ease the interpretation of digital color data, RGB values can be converted directly to HSB values that are based on human perception of color (Fig. 1). In HSB color description, hue is defined as an angle on a continuous circular scale from 0 to 360⬚ (0⬚ ⫽ red, 60⬚ ⫽ yellow, 120⬚ ⫽ green, 180⬚ ⫽ cyan, 240⬚ ⫽ blue, 300⬚ ⫽ magenta), saturation is the purity of the color from 0% (gray) to 100% (fully saturated color), and brightness is the relative lightness or darkness of the color from 0% (black) to 100% (white) (Adobe Systems, 2002). Among HSB, hue has been found to be the best indicator of the visual color of a turf (Landschoot and Mancino, 2000; Thorogood et al., 1993). However, preliminary work at the University of Arkansas has demonstrated that visual differences in turf color were sometimes the result of color saturation differences between turf plots rather than hue differences (Karcher, 2000, unpublished data). The objective of the following research was to determine if readily available equipment (a digital camera and commercially available software) could accurately quantify turfgrass color using an HSB color scale. Digital images were taken of standard color objects (Munsell Plant Tissue color chips) to determine the accuracy of digital image analysis with regard to the quantification of color parameters. Digital images were collected of turfgrass field plots varying in visual color due to either N fertility or genetically controlled differences to determine if digital image analysis was capable of quantifying color differences. MATERIALS AND METHODS Color Quantification of Digital Images The process used to determine the average color of a digital image included: (i) acquiring an image with digital photography, (ii) obtaining the average RGB pixel levels for the image, and (iii) converting the RGB levels to the more intuitive HSB parameters. All digital images in these studies were taken with an Olympus C-3030 camera (Olympus America Inc., Melville, NY). The images were collected in the JPEG (joint photographic experts group, .jpg) format, with a color depth of 16.7 million colors, and an image size of 1280 ⫻ 960 pixels (≈260 kilobytes per image). Camera settings included a shutter speed of 1/400 s, an aperture of f/4.0, and a focal length of 32 mm. Images were downloaded to a personal computer for subsequent analysis.

The average RGB levels of the digital images were calculated using SigmaScan Pro version 5.0 software (SPSS, 1998). The entire image was selected for analysis by including all possible hue and saturation levels in the color threshold option of the software. The average red, average green, and average blue measurement settings were used to obtain the average RGB levels for an image. The average RGB levels were then pasted into an MS Excel spreadsheet (Microsoft Corporation, 1999) created by the authors to automate the conversion of RGB to HSB values. The programmed formulas in the spreadsheet converted absolute RGB levels (measured on a scale of 0 to 255) to percentage RGB levels by dividing each level by 255. Percentage RGB levels were then converted to average HSB levels by the following algorithms (Adobe Systems, 2002): Hue

If max(R,G,B) ⫽ R, 60{(G ⫺ B)/[max(R,G,B) ⫺ min(R,G,B)]} If max(R,G,B) ⫽ G, 60(2 ⫹ {(B ⫺ R)/[max(R,G,B) ⫺ min(R,G,B)]}) If max(R,G,B) ⫽ B, 60(4 ⫹ {(R ⫺ G)/[max(R,G,B) ⫺ min(R,G,B)]}) Saturation

[max(R,G,B) ⫺ min(R,G,B)]/max(R,G,B) Brightness

max(R,G,B). Camera Calibration A series of digital images were taken of color chips from Munsell Color Charts for Plant Tissues (GretagMacbeth LLC, New Windsor, NY). Six images of varying hue were collected, ranging from yellowish green to green (chip numbers 5Y 6/6, 2.5GY 6/6, 5GY 6/6, 7.5GY 6/6, 2.5G 6/6, 5G 6/6). Eight images of varying saturation were collected, ranging from grayish green to bright green (chip numbers 7.5GY 6/2, 7.5GY 5/2, 7.5GY 6/4, 7.5GY 5/4, 7.5GY 6/6, 7.5GY 5/6, 7.5GY 6/8, 7.5GY 5/8). Ten images of varying brightness were collected, ranging from light green to dark green (chip numbers 7.5GY 8/4, 7.5GY 7/4, 7.5GY 6/4, 7.5GY 5/4, 7.5GY 4/4, 7.5GY 8/6, 7.5GY 7/6, 7.5GY 6/6, 7.5GY 5/6, 7.5GY 4/6). These Munsell color chips were chosen because they covered a relatively broad range of HSB levels and visually corresponded with plant tissue HSB levels typical of turfgrass (Beard, 1973). Calibration images were taken under dark conditions using only the camera flash as a light source. The images were analyzed for HSB levels using the methods described above. To determine the accuracy of HSB measurement with digital image analysis, the actual HSB levels of the Munsell color chips were determined using Munsell Conversion software version 4.1 (Munsell Color, 2000). Three separate linear regression analyses were performed using PROC REG in SAS Statistical Software (SAS Institute., 1996). The H, S, and B values from digital image analysis were analyzed as the independent variables and the actual H, S, and B values of the Munsell color chips were the dependent variables. For each HSB parameter, digital image analysis was considered to significantly detect color differences among color chips when the slope of the regression line was signifi-

KARCHER & RICHARDSON: DIGITAL IMAGE ANALYSIS OF TURF COLOR

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Fig. 1. Quantifying turfgrass color in the hue, saturation, and brightness (HSB) color space. (A) The hue is measured on a continuous scale from 0 to 360ⴗ. Turfgrass hues are typically between 70ⴗ and 110ⴗ. (B) For a specific turfgrass hue, here 90ⴗ, the saturation and brightness levels affect how dark green the color appears.

cantly different from zero (P ⬍ 0.05) (Freund and Wilson, 1993).

Nitrogen Fertility Color Differences Two ongoing N fertility field studies were used to assess the ability of digital image analysis to quantify visual color

differences among turf plots due to N treatments. The first experimental area was established with ‘Meyer’ zoysiagrass during the summer of 1996 on a silt loam (Typic Hapludult, pH 6.2). Individual plots were 1.4 m2 and mowed at a height of 1.9 cm. The second experimental area was a ‘Crenshaw’ creeping bentgrass putting green built in 1998 according to USGA recommendations (United States Golf Association,

Fig. 5. Color analysis of various turfgrass plots. (A) The plot receiving the higher N rate has a darker green color as a result of an increased hue angle and decreased brightness level. (B) ‘Shanghai’ bermudagrass has darker green color, the result of significantly decreased saturation level when compared with ‘Mini-Verde’. H, hue; S, saturation; B, brightness.

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1993). Individual plots were 1.5 m2 and mowed at a height of 0.4 cm. Both experimental areas were located at the University of Arkansas Research and Extension Center in Fayetteville, AR. The zoysiagrass study consisted of two treatment factors, N source (7 levels) and N rate (3 levels). The N source treatment levels included: (i) 100% ammonium sulfate (AS); (ii) 100% polymer-coated urea (PCU); (iii) 100% sulfur-coated urea (SCU); (iv) 33% AS, 67% PCU; (v) 33% AS, 67% SCU; (vi) 67% AS, 33% PCU; and (vii) 67% AS, 33% SCU. Each N source was applied at three N rate levels: (i) 4.8, (ii) 7.2, and (iii) 9.6 g m⫺2. Each of the resultant 21 fertility treatments was replicated four times in a randomized complete block design. Treatment applications were made in mid-May and mid-August in 2000. The creeping bentgrass study consisted of one treatment factor, N rate (7 levels). The N rate treatment levels included 0, 1, 2, 3, 4, 5, and 6 g m⫺2. The N source for all treatments was methylene urea. Each N rate was applied four times in a completely randomized design. Treatment applications were made monthly from June through September in 2000. Digital images were collected from each plot on 28 Sept. 2000 on the zoysiagrass study [44 d after treatment (DAT)] and on 16 Nov. 2001 on the creeping bentgrass study (55 DAT) between 1300 and 1400 h during mostly sunny conditions (illuminance ≈ 50 000 lux). Images were collected by a researcher standing immediately next to the plot while holding the camera directly over the center of the plot ≈1.5 m above the turf canopy. Care was taken to avoid casting shadows on the turf inside plot. Concurrent to the collection of digital images, the zoysiagrass and creeping bentgrass studies were visually rated for color by five and three independent researchers (rater experience ranged from a minimum of 2 yr to ⬎10 yr), respectively. Color ratings were based on a 1 to 9 scale where 1 ⫽ tan or brown turf, 6 ⫽ minimum acceptable color, and 9 ⫽ optimal dark green color. A DGCI was created from the HSB values to obtain a single value from digital image color analysis for comparison with values from subjective visual ratings. The index was created to measure the relative dark green color of an image using the following equation:

ance. Since the visual rating scale was unrelated to color values obtained from digital image analysis, the relative variances (coefficients of variation) were used for statistical comparison. Sample variances were calculated as the within-plot mean square for each color quantification method. Confidence bounds (95%) were constructed for the sample means and the withinplot variances and were used to calculate confidence bounds for the coefficients of variation. The relative variances of the methods were determined to be significantly different if the respective confidence bounds for the coefficients of variation did not overlap.

DGCI value ⫽ [(H ⫺ 60)/60 ⫹ (1 ⫺ S) ⫹ (1 ⫺ B)]/3.

RESULTS Camera Calibration

The color index was calculated from the average of transformed HSB parameters. Each transformed parameter measures dark green color on a scale of zero to one. Since the hue of most turfgrass images ranges between 60⬚ (yellow) and 120⬚ (green), the maximum dark green hue was assigned as 120⬚. Therefore, the dark green hue transform was calculated as (hue ⫺ 60)/60, so that hues of 60⬚ and 120⬚ would yield dark green hue transforms of zero and one, respectively. Since lower saturation and brightness values corresponded to darker green colors, (1 ⫺ saturation) and (1 ⫺ brightness) were used to calculate the dark green saturation and brightness transforms, respectively. The average of the transformed HSB values yielded a single measure of dark green color, the DGCI value, which ranged from zero to one with higher values corresponding to darker green color. Analyses of variance were performed using PROC GLM in SAS Statistical Software (SAS Institute, 1996) on the visual rating, HSB, and DGCI data sets. For a given color parameter, treatment and/or interaction effects were determined significant when the corresponding ANOVA f test had a P value ⱕ 0.05. In such cases, a Fisher’s protected LSD test was performed to separate treatment means (Freund and Wilson, 1993). Three digital images were taken on plots from the zoysiagrass and creeping bentgrass studies to compare the variance of digital image analysis with subjective visual rater vari-

Cultivar Color Differences Plots from a bermudagrass cultivar trial were used to assess the ability of digital image analysis to quantify visual color differences among cultivars. The trial was established in the summer of 1997 at the University of Arkansas Research and Extension Center in Fayetteville, AR (silt loam, Typic Hapludults, pH 6.2), and was a test site for the 1997 National Turfgrass Evaluation Program (NTEP) bermudagrass trial (NTEP, 1999). Individual plots were 1.4 m2 and maintained at a 1.9-cm mowing height. The study was replicated three times in a completely randomized design. Digital Images were taken as described previously on each replication of four cultivars that varied in green color (NTEP, 1999): ‘Cardinal’ (strong yellow-green), ‘Shanghai’ (dark graygreen), ‘Mini-Verde’ (strong dark yellow-green), and ‘Tifway’ (typical bermudagrass green color). The plots were photographed on 21 Sept. 2000 between 1325 and 1335 h during overcast conditions (illuminance ≈ 5000 lux). One-way ANOVAs were performed using PROC GLM in SAS Statistical Software (SAS Institute, 1996) on the HSB and DGCI data sets, with cultivar as the treatment variable. For a given color parameter, differences were determined significant among cultivars when the ANOVA f test had a corresponding P value ⱕ 0.05. In such cases, a Fisher’s protected LSD test was performed to separate cultivar differences (Freund and Wilson, 1993).

Digital image analysis differentiated HSB levels of the Munsell Plant Tissue color chips chosen for this study (Fig. 2, 3, and 4). Hue and saturation measurements obtained through digital image analysis were statistically equal to the actual hue and saturation values as the slopes and intercepts of the hue and saturation regression lines were not significantly different (P ⬍ 0.05) from 1 and 0, respectively. Brightness measurements were slightly less accurate, but could be effectively corrected (r2 ⫽ 0.96) by the following equation: actual brightness ⫽ 0.60 (measured brightness) ⫹ 0.37.

Nitrogen Fertility Color Differences Differences in turfgrass color resulting from various N fertility treatments were quantified with digital image analysis (Tables 1, 2). Although there were no differences among treatments with regard to saturation and brightness levels in the zoysiagrass study, hue and DGCI values were significantly affected by N source and N rate treatments. In the creeping bentgrass study, HSB and DGCI values were all significantly affected by N rate. In both studies, similar treatment rankings were ob-


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Fig. 2. Linear regression analysis between hue quantified by digital image analysis and the actual hue of Munsell plant tissue color chips.

tained by digital image analysis and subjective ratings (Tables 1 and 2). The 100% PCU treatment had significantly lower DGCI and visual rating means than all other treatments (with the exception the 67% PCU mean for DGCI). In addition, there were significant differences among all three N rate treatment means (9.6 g m⫺2 ⬎ 7.2 g m⫺2 ⬎ 4.8 g m⫺2) with regard to DGCI and visual ratings. In both studies, the coefficients of variation for HSB

and DGCI ranged from 2 to 18 times less than that of visual ratings (Tables 3, 4). All coefficients of variation for the digital image analysis parameters were statistically smaller than the CV% for the visual ratings based on the 95% confidence intervals.

Cultivar Color Differences There were significant differences among bermudagrass cultivars with regard to hue, saturation, and DGCI

Fig. 3. Linear regression analysis between color saturation quantified by digital image analysis and the actual color saturation of Munsell plant tissue color chips.

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Fig. 4. Linear regression analysis between color brightness quantified by digital image analysis and the actual color brightness of Munsell plant tissue color chips.

(Table 5). Cultivar hue ranged from 71⬚ to 92⬚, while saturation and DGCI levels ranged between 29 to 42% and 0.39 to 0.55, respectively. ‘Cardinal’, with an average hue of 76.2⬚, was ≈10⬚ (and significantly) lighter in hue than the other three cultivars. This result was consistent with ‘Cardinal’ appearing a lighter shade of green to the eye than the other three cultivars. ‘Cardinal’ also ranked lowest in genetic color among 28 cultivars in the 1997 NTEP trials when results were averaged across

18 trial locations (NTEP, 1999). ‘Shanghai’, which appeared darker to the eye than the other cultivars, had a significantly lower saturation level than the other cultivars (Table 5). The dark color of this cultivar was apparently due to its grayish green color (less saturation), rather than it being a darker shade of green (higher hue). The ‘Cardinal’ DGCI mean ranked significantly (P ⬍ 0.05) lower than the other three cultivars, which were statistically equal. In addition, the increased DGCI for

Table 1. Color analyses by subjective visual ratings and digital image analysis of zoysiagrass turf fertilized with various N sources and rates, 28 Sept. 2000 (44 d after treatment). Visual rating†

Hue‡

Saturation§

Degrees N source Ammonium sulfate Polymer-coated urea Sulfur-coated urea 1/3 AS ⫺ 2/3 PCU 1/3 AS ⫺ 2/3 SCU 2/3 AS ⫺ 1/3 PCU 2/3 AS ⫺ 1/3 SCU N rate, g m⫺2 4.8 7.2 9.6 ANOVA Source (df) N source (6) N rate (2) N source ⫻ N rate (14) Error (60) CV%

Brightness¶

DGCI#

%

6.5ab†† 5.2c 6.7a 6.1b 6.7a 6.3ab 6.5ab

86.5ab 82.6c 86.3ab 82.7c 84.8b 85.5ab 86.7a

43.4a 43.6a 42.8a 43.5a 44.2a 44.5a 43.9a

58.3a 59.5a 58.6a 58.5a 58.6a 58.2a 58.7a

0.475a 0.449d 0.474a 0.453cd 0.462bc 0.466ab 0.473ab

5.3c 6.6b 6.9a

83.6c 84.9b 86.6a

44.0a 43.8a 43.2a

59.0a 58.5a 58.4a

0.454c 0.464b 0.476a

mean squares 0.04 0.05 0.06 0.06 5.6

0.02 0.03 0.03 0.03 7.4

15.89*** 99.41*** 1.12 1.92 22.1

36.03*** 63.83*** 1.59 5.10 2.7

0.811*** 0.655*** 0.119 0.226 4.2

*** Significant at the 0.001 level of probability. † 1 ⫽ tan/brown turf, 6 ⫽ minimum acceptable color, 9 ⫽ optimal dark green color. ‡ 0ⴗ ⫽ red, 60ⴗ ⫽ yellow, 120ⴗ ⫽ green, 180ⴗ ⫽ cyan, 240ⴗ ⫽ blue, and 300ⴗ ⫽ magenta. § 0% ⫽ gray and 100% ⫽ fully saturated color. ¶ 0% ⫽ black and 100% ⫽ white. # Dark green color index. A combination of HSB parameters for a single measurement of dark green color: DGCI ⫽ [(Hue ⫺ 60)/60 ⫹ (1 ⫺ Saturation) ⫹ (1 ⫺ Brightness)]/3. †† Within each effect and column, means sharing a letter are not statistically different according to Fisher’s protected LSD test (␣ ⫽ 0.05).

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Table 2. Color analyses by subjective visual ratings and digital image analysis of creeping bentgrass turf fertilized with various N rates, 16 Nov. 2001 (55 d after treatment). N rate g

Visual rating†

Hue‡

4.2d 5.2c 5.9c 6.8b 7.0ab 7.1ab 7.6a

Degrees 64.6e 70.3d 74.1c 77.5b 79.4ab 80.6a 80.9a

67.4a 67.2a 66.4a 65.9ab 64.7bc 64.5bc 63.3c

17.81*** 0.90 15.3

447.7*** 9.55 4.1

mean squares 0.27*** 0.04 0.3

m⫺2

0.0 1.0 2.0 3.0 4.0 5.0 6.0 ANOVA Source (df) N rate (6) Error (21) CV%

Saturation§

Brightness¶

DGCI#

% 29.3a 28.3a 26.8b 24.8c 24.1cd 22.8de 22.3e

0.370e 0.405d 0.435c 0.462b 0.479ab 0.490a 0.497a

0.89*** 0.03 0.6

0.027*** 0.0005 5.1

*** Significant at the 0.001 level of probability. † 1 ⫽ tan/brown turf, 6 ⫽ minimum acceptable color, 9 ⫽ optimal dark green color. ‡ 0ⴗ ⫽ red, 60ⴗ ⫽ yellow, 120ⴗ ⫽ green, 180ⴗ ⫽ cyan, 240ⴗ ⫽ blue, and 300ⴗ ⫽ magenta. § 0% ⫽ gray and 100% ⫽ fully saturated color. ¶ 0% ⫽ black and 100% ⫽ white. # Dark green color index. A combination of HSB parameters for a single measurement of dark green color: DGCI ⫽ [(Hue ⫺ 60)/60 ⫹ (1 ⫺ Saturation) ⫹ (1 ⫺ Brightness)]/3. †† Within each effect and column, means sharing a letter are not statistically different according to Fisher’s protected LSD test (␣ ⫽ 0.05).

‘Shanghai’ compared with ‘Tifway’ and ‘Mini-Verde’ was nearly significant (P ⫽ 0.07). These differences in color are in strong agreement with results from the 1997 NTEP trials where all four cultivars were significantly different: ‘Shanghai’ ⬎ ‘Tifway’ ⬎ ‘Mini-Verde’ ⬎ ‘Cardinal’ (NTEP, 1999). Although ‘Tifway’ and ‘MiniVerde’ were not significantly different in DGCI using digital image analysis, they only differed by 0.3 rating units in the 1997 NTEP trial (LSD0.05 ⫽ 0.2).

DISCUSSION Digital photography and image analysis were able to quantify color differences among standard Munsell Plant Tissue color chips, zoysiagrass and creeping bentgrass receiving various N fertility treatments, and bermudagrass cultivars of varying genetic color. When visual ratings and digital image analysis were both performed, the statistical ranking of treatment means were similar between the two methods. However, DGCI variance was significantly lower than rater variance when the same turf plots were evaluated multiple times,

probably the result of removing either rater bias or rater error from the color evaluation process. These results confirm that visual ratings can be used to separate treatment effects on turf color. In most cases, raters ranked the turf plots similarly although differences existed in their absolute rating values. Therefore, color ratings remain a valid evaluation tool if data are not compared across raters. However, the accuracy of digital image analysis, demonstrated in the calibration experiments, enables researchers to record reflected turfgrass color on a standardized scale rather than using arbitrary rating values. Therefore, valid comparisons of color data across researchers, locations, and years are possible with digital image analysis. Creeping bentgrass plots had significant differences in HSB levels, whereas zoysiagrass plots were significantly different only with regard to hue. This may be due to a genetic difference in N uptake and utilization between the two species. However, in both species, significant DGCI differences existed due to N treatments. Therefore, the DGCI is a more consistent measure of dark

Table 3. Comparison of variance between subjective raters and digital image analysis for color evaluation of zoysiagrass turf, 28 Sept. 2000 (44 d after treatment). Visual ratings† Sampling information Subsampling units Experimental units n df Statistics x¯ 95% confidence interval for ␮ s 95% confidence interval for ␴ CV% CV% confidence bounds††

5 84 420 336 6.27 6.12–6.42 1.38 1.29–1.50 22.1 20.0–24.5

Hue‡ 3 21 63 42 Degrees 83.76 83.31–84.20 1.52 1.25–1.93 1.8 1.4–2.3

Saturation§

Brightness¶

3 21 63 42

3 21 63 42

DGCI# 3 21 63 42

% 44.50 43.92–45.08 0.039 0.026–0.063 4.4 3.6–5.7

58.11 57.80–58.41 0.011 0.007–0.017 1.8 1.4–2.3

0.457 0.453–0.460 0.0001 0.0001–0.0002 2.6 2.1–3.4

† 1 ⫽ tan/brown turf, 6 ⫽ minimum acceptable color, 9 ⫽ optimal dark green color. ‡ 0ⴗ ⫽ red, 60ⴗ ⫽ yellow, 120ⴗ ⫽ green, 180ⴗ ⫽ cyan, 240ⴗ ⫽ blue, and 300ⴗ ⫽ magenta. § 0% ⫽ gray and 100% ⫽ fully saturated color. ¶ 0% ⫽ black and 100% ⫽ white. # Dark green color index. A combination of HSB parameters for a single measurement of dark green color: DGCI ⫽ [(Hue ⫺ 60)/60 ⫹ (1 ⫺ Saturation) ⫹ (1 ⫺ Brightness)]/3. †† CV% confidence bounds calculated as (lower ␴ bound/upper ␮ bound, upper ␴ bound/lower ␮ bound).

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Table 4. Comparison of variance between subjective raters and digital image analysis for color evaluation of creeping bentgrass turf, 16 Nov. 2001 (55 d after treatment). Visual ratings† Sampling information Subsampling units Experimental units n df Statistics x¯ 95% confidence interval for ␮ s 95% confidence interval for ␴ CV% CV% confidence bounds††

Hue‡

3 28 84 56

3 28 84 56 Degrees 75.34 75.17–75.52 0.70 0.59–0.86 0.9 0.7–1.1

6.23 6.05–6.43 0.75 0.63–0.92 12.0 9.8–15.2

Saturation§

Brightness¶

3 28 84 56

3 28 84 56

DGCI# 3 28 84 56

% 65.62 65.42–65.82 0.008 0.007–0.009 1.2 1.0–1.5

25.51 25.26–25.75 0.010 0.008–0.012 3.8 3.2–4.7

0.448 0.447–0.449 0.003 0.003–0.004 0.7 0.6–0.9

† 1 ⫽ tan/brown turf, 6 ⫽ minimum acceptable color, 9 ⫽ optimal dark green color. ‡ 0ⴗ ⫽ red, 60ⴗ ⫽ yellow, 120ⴗ ⫽ green, 180ⴗ ⫽ cyan, 240ⴗ ⫽ blue, and 300ⴗ ⫽ magenta. § 0% ⫽ gray and 100% ⫽ fully saturated color. ¶ 0% ⫽ black and 100% ⫽ white. # Dark green color index. A combination of HSB parameters for a single measurement of dark green color: DGCI ⫽ [(Hue ⫺ 60)/60 ⫹ (1 ⫺ Saturation) ⫹ (1 ⫺ Brightness)]/3. †† CV confidence bounds calculated as (lower ␴ bound/upper ␮ bound, upper ␴ bound/lower ␮ bound).

green color across species than the individual measurements of H, S, or B. Since N fertility significantly affected the HSB levels of creeping bentgrass and zoysiagrass (Fig. 5A), color measurement using digital image analysis may be capable of assessing the N status of plant tissues. For example, zoysiagrass plots exhibiting the darkest green N responses had hue angles near 90⬚ while the most chlorotic plots had hue angles near 70⬚. Other research has demonstrated that correlations exist between the N content of creeping bentgrass tissue and its color, measured by colorimeter (Landschoot and Mancino, 2000). The significantly larger CV% with visual ratings suggest that rating values are evaluator dependent and that evaluators are likely to vary in how they rank different shades of green (Skogley and Sawyer, 1992; Horst et al., 1984). This may be a factor in multisite trials when an individual cultivar is ranked inconsistently from location to location (NTEP, 1999). Color evaluation with digital photography and image analysis may minimize variations due to locations and years and would increase the validity of comparing color data across both. Table 5. Color evaluation among bermudagrass cultivars using digital image analysis. Cultivar

Hue†

‘Cardinal’ ‘Mini-Verde’ ‘Shanghai’ ‘Tifway’ ANOVA Source (df) Cultivar (3) Error (8) CV%

76.2b# 88.1a 89.9a 86.6a

Saturation‡

Brightness§

DGCI¶

%

113.97** 12.01 4.07

40.3a 38.0a 30.0c 34.3b

61.0a 58.4a 58.4a 59.4a

mean squares 0.611*** 0.045 0.016 0.025 3.62 2.68

0.419b 0.502a 0.538a 0.502a 0.0077*** 0.00043 4.26

** Significant at the 0.01 level of probability. *** Significant at the 0.001 level of probability. † 0ⴗ ⫽ red, 60ⴗ ⫽ yellow, 120ⴗ ⫽ green, 180ⴗ ⫽ cyan, 240ⴗ ⫽ blue, and 300ⴗ ⫽ magenta. ‡ 0% ⫽ gray and 100% ⫽ fully saturated color. § 0% ⫽ black and 100% ⫽ white. ¶ Dark green color index. A combination of HSB parameters for a single measurement of dark green color: DGCI ⫽ [(Hue ⫺ 60)/60 ⫹ (1 ⫺ Saturation) ⫹ (1 ⫺ Brightness)]/3. # Within each column, means sharing a letter are not statistically different according to Fisher’s protected LSD test (␣ ⫽ 0.05).

The ability to distinguish color differences among turf plots as either H, S, or B differences is a significant advantage of digital image analysis over subjective visual ratings. For example, a turf that has a darker color because of grayish genetic color may not be as aesthetically desirable as a turf that is lighter in appearance but is saturated with green color. Consequently, there exists a potential for evaluator bias, which may have occurred in the 1997 vegetative bermudagrass NTEP trails where the dark grayish variety ‘Shanghai’ ranked among the top three cultivars in genetic color in 13 of the 18 test sites, while it ranked near the middle or bottom at the other five sites (NTEP, 1999). Rather than ‘Shanghai’ exhibiting different genetic color at the various NTEP locations, this discrepancy may have been due to varying evaluator perceptions of optimal dark green color for bermudagrass. Digital image analysis was more time consuming than visual color ratings, but far less labor intensive than traditional laboratory methods that are used to quantify turf color (amino acid and chlorophyll assays). Images were collected in the field at a rate of ≈2 images per minute and were analyzed with SigmaScan at a rate of 3 images per minute. Although subjective ratings require less time than digital image analysis, the color data obtained from digital image analysis are free from researcher bias and inaccuracies and include information on individual HSB parameters. Furthermore, SigmaScan macros have been developed for batch-analysis of an unlimited number of images (Karcher, 2001, unpublished data). Another advantage of digital image analysis over other objective color evaluation methods is the ability to measure large areas of turf in situ. The area of turf that is possible to evaluate is limited only by the height of the camera above the canopy and the subsequent field of vision. An el-shaped monopod was designed at the University of Arkansas that enables images to be taken of turf areas in excess of 30 m2 (a remote control releases the camera shutter). This is a significant improvement over standard colorimeters that typically measure areas smaller than 20 cm2 (less area than a


35-mm slide). In addition, if a turf plot is not uniformly green due to disease, injury, or dormancy, a color threshold technique can be used within SigmaScan to quantify the color of only the green portions of an image which correspond to healthy turf (Richardson et al., 2001). Another advantage of digital image analysis is that once images are obtained, they can be stored indefinitely before analysis. For instance, images of field trials can be collected regularly during the growing season, but analyzed during the off-peak months. In contrast to visual color ratings, trained, experienced researchers are not needed to evaluate turf color using digital image analysis. Light conditions may affect the results from these techniques, although successful results were obtained under both sunny and overcast conditions in these studies. Digital color analysis may not be as effective during dawn or dusk due to increased shadows within the turf canopy. In addition, comparisons of color among turf plots from different locations and times may only be possible if the images are collected under equal light conditions. This could be accomplished through the use of standard artificial light sources while collecting images either at night or in an enclosed system. A digital camera capable of acquiring high quality images is becoming commonplace in turfgrass research programs. The ability to capture extensive information of turfgrass in situ makes it a viable tool to quantify turfgrass parameters commonly of interest in field experiments. In addition to color quantification, digital image analysis has been used successfully to quantify percentage turfgrass cover (Richardson et al., 2001) and may potentially be useful in quantifying turf parameters such as weed infestation, disease incidence, herbicide phytotoxicity, leaf area, and recovery from injury. ACKNOWLEDGMENTS The authors thank the O.J. Noer Foundation for the financial support of this research and SPSS, Inc., for the assistance in the form of a copy of SigmaScan Pro software. Also, the authors are grateful for the technical assistance of Yoshi Ikemura, John McCalla, Margaret Secks, and Chris Weight.

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