Comparing digital image analysis and visual rating of gamma ray induced Kentucky bluegrass (Poa pratensis) mutants


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Authors

  • AJAI KUMAR TIWARI Senior Scientist, Directorate of Floricultural Research, Shivajinagar, Pune, Maharashtra 410 005
  • RAMESH KUMAR Director, Directorate of Floricultural Research, Shivajinagar, Pune, Maharashtra 410 005
  • GUNJEET KUMAR Scientist, Directorate of Floricultural Research, Shivajinagar, Pune, Maharashtra 410 005
  • GANESH B KADAM Scientist, Directorate of Floricultural Research, Shivajinagar, Pune, Maharashtra 410 005
  • T N SAHA Scientist, Directorate of Floricultural Research, Shivajinagar, Pune, Maharashtra 410 005
  • GIRISH K S Scientist, Directorate of Floricultural Research, Shivajinagar, Pune, Maharashtra 410 005

https://doi.org/10.56093/ijas.v85i8.50835

Keywords:

Dark green color index, Dark image analysis, Digital image analysis, Poa pratensis

Abstract

Variability was generated in Kentucky bluegrass (Poa pratensis) through gamma-ray irradiation and genotypes were evaluated for their response to low management, induction of dwarfness and other quality attributes. The main objective of this study was to judge the suitability of digital image analysis over visual rating of turf quality and to identify changes in mutants, and correlations among visual rating and digital image analysis were computed. Differences were significant among mutants with respect to hue, brightness and saturation. Significant and positive correlations of hue and DGCI were observed with all the parameters of visual rating. There were non-significant correlations of brightness with quality, brightness with texture, saturation and texture. These relationships were better in DGCI and color (r2=0.123) DGCI and brightness (r2=0.0849); DGCI and hue (r2=0.0772) and DGCI texture (r2=0.0325). Nonlinear relationship was noticed between DGCI and saturation (r2=0.0011).

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References

Da Costa, M, Wang Z, and Huang B. 2004. Physiological adaptation of Kentucky bluegrass to localized soil drying. Crop Science 44: 1 307–14 DOI: https://doi.org/10.2135/cropsci2004.1307

Diaz-Lago, J E, Stuthman D D, Leonard K J. 2003. Evaluation of components of partial resistance to oat crown rust using digital image analysis. Plant Disease 84(6): 667. DOI: https://doi.org/10.1094/PDIS.2003.87.6.667

Freund, R J and Wilson W J. 1993. Statistical Methods. Academic Press, San Diego, CA.

Goodenough A E and Goodenough A S. 2012. Development of a rapid and precise method of digital image analysis to quantify canopy density and structural complexity. ISRN Ecology Article ID 619842:11 http://dx.doi.org/10.5402/2012/619842. DOI: https://doi.org/10.5402/2012/619842

Ikemura Y. 2003. Using digital image analysis to measure the nitrogen concentration of turf grasses. M Sc thesis, University of Arkansas, Fayetteville.

Karcher, DE and Richardson, M D 2003. Quantifying turfgrass color using digital image analysis. Crop Science 43: 943–51. DOI: https://doi.org/10.2135/cropsci2003.9430

Keskin M, Dodd R B, Han Y J and Khalilian. 2003.Predicting visual quality ratings of turfgrass research plots using spectral reflectance. Paper No. 031114, ASABE Meeting Presentation, American Society of Agricultural Engineers, St. Joseph, MI.

Mirik. M, Michels G J .J, KassymzhanovaMirik S, Elliott N C, Catana V, Jones D B, Blowing. R. 2006. Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat. Computers and Electronics in Agriculture 51(1-2): 86–98. DOI: https://doi.org/10.1016/j.compag.2005.11.004

Morris K N. 2002. A guide to NTEP Turfgrass Ratings. A publication of the National Turfgrass Evalution Program (NTEP), NTEP. Beltsville, MD 11pd 30–9.

Newton A C. 2007. Forest Ecology and Conservation: A Handbook of Techniques, p 454, Oxford University Press, Oxford, UK.

Stafford R, Adam G H and Goodenough A E 2013. Using Long- Term Volunteer Records to Examine Dormouse (Muscardinusa vellanarius) Nestbox Selection. Public Library of Science 8 (6). DOI: https://doi.org/10.1371/journal.pone.0067986

Steddom K, McMullen M, Schatz B and Rush C M. 2004. Assessing foliar disease of wheat image analysis. (In) The 2004 Summer Crops Field Day at Bushland, TX sponsored by the Cooperative Research, Education & Extension Team (CREET), Bushland, TX, pp 32–8.

Tiwari A K, Bhuj B D and S K Mishra 2010. Impact of certain chemicals on vase life of different cultivars of china aster and gladioli. Indian Journal of Horticulture 67(2): 255–9

Tiwari A K Kumar R, Kumar G, Kadam G B, Saha T N, Girish K S and Tiwari B. 2014a. Mutagenesis and digital image analysis of mutants for quality attributes of native Cynodon dactylon. Indian Journal of Agricultural Sciences 84(6): 733–6.

Tiwari A K Kumar G, Kadam G B, and Saha T N. 2014b. Comparing digital image analysis and visual rating of gamma ray induced perennial rye grass (Lolium perenne) mutants. HortFlora Research Spectrum 3(3): 211–7.

Tiwari A K, Kumar R Kumar G, Kadam G B and Saha T N. 2015. Comparing digital image analysis and visual rating of gamma ray induced Bent grass (Agrostis stolonifera) mutants. Indian Journal of Agricultural Sciences 85(1): 93–106.

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Submitted

2015-08-06

Published

2015-08-06

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Articles

How to Cite

TIWARI, A. K., KUMAR, R., KUMAR, G., KADAM, G. B., SAHA, T. N., & S, G. K. (2015). Comparing digital image analysis and visual rating of gamma ray induced Kentucky bluegrass (Poa pratensis) mutants. The Indian Journal of Agricultural Sciences, 85(8), 1046-1049. https://doi.org/10.56093/ijas.v85i8.50835
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