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| DOI: | 10.2135/cropsci2011.10.0553 |
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| Web URL(s): | https://dl.sciencesocieties.org/publications/cs/articles/52/5/2365
Last checked: 11/07/2016
Access conditions: Item is within a limited-access website
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Last checked: 11/07/2016
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Access conditions: Item is within a limited-access website |
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| Publication Type:
| Refereed |
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| Author(s): | Ghali, Ihab E.;
Miller, Grady L.;
Grabow, Garry L.;
Huffman, Rodney L. |
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| Author Affiliation: | Ghali, Grabow, and Huffman: Department of Biological and Agricultural Engineering; Miller: Department of Crop Science, North Carolina State University |
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| Title: | Using variability within digital images to improve tall fescue color characterization |
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| Section: | Turfgrass science
Other records with the "Turfgrass science" Section
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| Source: | Crop Science. Vol. 52, No. 5, September 2012, p. 2365-2374. |
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| Publishing Information: | Madison, Wisconsin: Crop Science Society of America |
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| # of Pages: | 10 |
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| Related Web URL: | https://dl.sciencesocieties.org/publications/cs/abstracts/52/5/2365
Last checked: 11/07/2016
Notes: Abstract only
https://dl.sciencesocieties.org/publications/cs/articles/52/5/2365?show-t-f=tables&wrapper=no
Last checked: 11/07/2016
Notes: Tables only
https://dl.sciencesocieties.org/publications/cs/articles/52/5/2365?show-t-f=figures&wrapper=no
Last checked: 11/07/2016
Notes: Figures only |
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| Keywords: | TIC Keywords: Calibrations; Color; Festuca arundinacea; Image analysis
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| Abstract/Contents: | "Digital image analysis (DIA) provides an accurate, nondestructive, and objective assessment of turf color. Previous research developed an index known as the dark green color index (DGCI) via DIA as an indicator of turf color. The objective of this study was to use DGCI variability to better predict a visual rating (VR) index used to evaluate tall fescue (Festuca arundinacea Schreb.) color under different irrigation treatments. To develop DGCI statistics, two freeware software packages (Image J and R) were used to extract and process information from digital images. The model to predict VR from DIA was developed and calibrated using candidate DGCI statistical moments from 120 images in a calibration data set using a multiple linear regression procedure. Fitness of calibration and validation models were verified using the adjusted coefficient of determination, root mean square error, and the Mallow's Cp statistic. The two-variable model produced more precise estimates (adjusted R2 = 0.926 and 0.899) than the model that only used one term in predicting the VR values (adjusted R2 = 0.879 and 0.843) for calibration and validation sets, respectively. These data suggest incorporating a measure of color uniformity improves the use of DGCI in predicting VR values compared to using only the mean of DGCI values to predict VR values. Model refinements may be needed for other turf species, but current work suggests using additional statistical moments such as SD improves VR estimate precision and accuracy." |
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| Language: | English |
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| References: | 40 |
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| Note: | Pictures, color
Figures
Tables
Graphs |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Ghali, I. E., G. L. Miller, G. L. Grabow, and R. L. Huffman. 2012. Using variability within digital images to improve tall fescue color characterization. Crop Sci. 52(5):p. 2365-2374. |
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| DOI: 10.2135/cropsci2011.10.0553 |
| Web URL(s):
https://dl.sciencesocieties.org/publications/cs/articles/52/5/2365
Last checked: 11/07/2016
Access conditions: Item is within a limited-access website
https://dl.sciencesocieties.org/publications/cs/pdfs/52/5/2365
Last checked: 11/07/2016
Requires: PDF Reader
Access conditions: Item is within a limited-access website |
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MSU catalog number: b2211522a |
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