Reflectance-Based Sensor to Predict Visual Quality Ratings of Turfgrass Plots

Keskin, Muharrem; Han, Yuechen; Dodd, Roy B.; Khalilian, Ahmad

doi:10.13031/2013.25355

Cited by 19 publications

(29 citation statements)

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“…The coefficients of determination {r) between NDVI and visual quality ranged from 0.38 to 0.83 in the bluegrasses and were lesser, albeit significant, in tall fescue (Table 1). These r^ values are similar to results from other sttidies that have indicated significant relationships between NDVI and visual quality (Trenholm et al, 1999;Bell et al, 2002;Fitz-Rodriguez and Choi, 2002;Keskin et al, 2008;Lee et al, 2011).…”

Section: Resultssupporting

confidence: 88%

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Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Bremer

Lee

et al. 2011

Crop Science

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Canopy spectrai reflectance may provide an objective means to evaluate visual quality of turfgrass, but evaluations of visual quality may be confounded by differences in reflectance among species or cultivars. In this 3-yr study near Manhattan, KS, we examined effects of species and cultivars on relationships between normalized difference vegetation index (NDVI) and visual quality ratings in Kentucky bluegrass {Poa pratensis L., 'Apollo'), two Kentucky bluegrass x Texas biuegrass {Poa arachnifera Ton.) hybrids (Thermal Blue' and 'Reveille'), and tall fescue {Festuca arundinacea Schreb., 'Dynasty'). A broad range of visual quality was imposed on all four grasses through deficit irrigation and NDVI was measured using broadband spectral radiometry across this range for each grass. Distinct linear regression models of visual quality were found for each grass, and models were also distinct among years in each grass. Relationships between NDVI and visual quality were stronger in the bluegrasses (r^ = 0.41 to 0.83) because they had a greater range in quality under deficit irrigation than tall fescue. The 95% confidence intervals surrounding predictions of visual quality from NDVI ranged from ± 1.25 to 2.10 (on a 1 to 9 scale). Results indicated that the requirement to develop separate models for each grass and in each year, combined with relatively wide confidence intervals, represents a practical limitation to predicting visual quality with NDVI.

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Section: Resultssupporting

confidence: 88%

“…X C. transvaalensis Burtt Davy, 'Midiron']. In other studies, vegetation indices calculated from reflectance data were also strongly correlated with visual quality in a number of turfgrass species and under different cultural practices (Bell et al, 2002;Fitz-Rodriguez and Choi, 2002;Keskin et al, 2008;Lee et al, 2011).…”

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confidence: 89%

Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Bremer

Lee

et al. 2011

Crop Science

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“…calculated from refl ectance data were also strongly correlated with visual quality in a number of turfgrass species and under diff erent cultural practices (Bell et al, 2002;Fitz-Rodriguez and Choi, 2002;Keskin et al, 2008). In this 2-yr study near Manhattan, KS, we examined effects of mowing height on relationships between normalized difference vegetation index (NDVI) and visual quality ratings in Kentucky bluegrass (KBG; Poa pratensis L., 'Apollo') and in a KBG × Texas bluegrass (Poa arachnifera Torr.)…”

Section: Relationships Between Normalized Diff Erence Vegetation Indementioning

confidence: 99%

“…Bell et al (2002) reported greater correlations between turfgrass quality and vegetation indices in turfgrasses mown at high than at low heights, suggesting that diff erences in biomass quantities aff ected relationships between refl ectance and turfgrass quality. Keskin et al (2008) developed an optical sensor to predict visual quality in turfgrass from refl ectance measurements but acknowledged that diff erent mowing heights may confound the practical application of their sensor. Keskin et al (2008) developed an optical sensor to predict visual quality in turfgrass from refl ectance measurements but acknowledged that diff erent mowing heights may confound the practical application of their sensor.…”

Section: Relationships Between Normalized Diff Erence Vegetation Indementioning

confidence: 99%

Relationships between Normalized Difference Vegetation Index and Visual Quality in Turfgrasses: Effects of Mowing Height

Lee

Bremer

et al. 2011

Crop Science

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Canopy spectral reflectance may provide an objective means to evaluate visual quality of turfgrass, but evaluations of quality may be confounded by cultural practices that affect reflectance, such as mowing height. In this 2‐yr study near Manhattan, KS, we examined effects of mowing height on relationships between normalized difference vegetation index (NDVI) and visual quality ratings in Kentucky bluegrass (KBG; Poa pratensis L., ‘Apollo’) and in a KBG × Texas bluegrass (Poa arachnifera Torr.) hybrid (HBG; ‘Thermal Blue’). Mowing heights were 7.62 cm (high) and 3.81 cm (low). The NDVI averaged 4.5 to 7% greater in high‐ than in low‐mown plots. Distinct regression models of visual quality were found at each mowing height and in each species (r2 from 0.40 to 0.81); separate relationships between NDVI and visual quality were also found between years in the same plots. Correlations between NDVI and visual quality were stronger at high than at low mowing heights, possibly because of greater green biomass at high mowing heights. The 95% confidence intervals surrounding predictions of visual quality from NDVI ranged from ±1.34 to 2.75 (on a 1‐to‐9 scale). Thus, lack of precision is a concern when using these models for detection of differences between treatments. Results indicate that when using NDVI to evaluate turfgrass quality, evaluations should be limited to plots maintained at the same mowing height and with the same species to reduce variability in NDVI.

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“…Keskin et al (2004) used 4 wavelength bands at 550, 680, 770, and 810 nm to predict nitrogen content of golf course turfgrass clippings (R 2 > 0.82). Keskin et al (2008) developed a portable sensor to predict the visual quality of turfgrass plots from the reflectance of red and NIR bands (R 2 = 0.60). Burchia et al (2010) treated Asiatic lily plants with K-sulfate and/or sucrose and evaluated tepal colors by portable spectrocolorimeter in CIE L*a*b* coordinates; treatment resulted in significant improvements in color quality.…”

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confidence: 99%

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

Keskin¹,

Karanlık²,

Keskin³

et al. 2013

Turk J Agric For

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10 m with a sampling rate of 1 min from 2005 to 2009 were used. In the second part of the study, autoregressive (AR) and artificial neural network (ANN) models were applied for prediction of wind speed. The prediction methods were assessed by comparing the characteristic frequency components of the prediction series and the real series. The best results were obtained from the ANN model; however, the AR model was found to moderately show the spectral characteristics.

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Reflectance-Based Sensor to Predict Visual Quality Ratings of Turfgrass Plots

Cited by 19 publications

References 0 publications

Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Relationships between Normalized Difference Vegetation Index and Visual Quality in Turfgrasses: Effects of Mowing Height

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

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