Full TGIF Record # 313098
Item 1 of 1
DOI:10.5660/WTS.2020.9.1.43
Web URL(s):http://www.weedturf.org/view/N0260090105.pdf
    Last checked: 12/18/2020
    Requires: PDF Reader
http://www.weedturf.org/article/?num=N0260090105
    Last checked: 12/18/2020
Publication Type:
i
Refereed
Author(s):Chang, Seog-Won
Author Affiliation:Dept. of Golf Course Management, Korea Golf University, Hoengseong, Korea
Title:Using digital image analysis to quantify turfgrass growth and disease
Source:Weed and Turfgrass Science. Vol. 9, No. 1, March 2020, p. 43-52.
Publishing Information:Korea: The Korean Society of Weed Science and Turfgrass Society of Korea
# of Pages:10
Keywords:Author-Supplied Keywords: Digital image analysis; Turfgrass disease; Turfgrass growth; Visual evaluation
Abstract/Contents:"Turfgrass researchers and golf course superintendents often rely on visual assessments to assess turfgrass growth and disease. Visual evaluation can make a unexpected subjective bias in the outcome of the assessment, depending on the experience of the evaluator or the degree of training. In this study, green up rate, coverage rate and percent diseased area were evaluated by applying image analysis program to photographed turfgrass for more accurate and reproducible investigation. The same photographic evaluation was presented to turfgrass experts who had experienced turfgrass evaluation for many years, and the results were compared with those obtained from the image analysis program. Experts evaluated the green up rate, coverage rate, and percent diseased area of turfgrass significantly different from the results of image analysis. There were also variations in the evaluated values among experts' evaluations, and the more difficult the photographic images are to calculate the acquired area, the greater the variation among individuals. The relationship between turfgrass research experience and image analysis values was not statistically correlated. However, the image analysis of turfgrass growth and disease was more accurate and reproducible than expert evaluation. Therefore, if turfgrass researchers or field managers use image analysis programs, even unskilled researchers expected be of great help in making evaluation."
Language:English
References:22
Note:Abstract appears in English
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ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
2020. Using digital image analysis to quantify turfgrass growth and disease. (In English, with English abstract.) Weed and Turfgrass Science. 9(1):p. 43-52.
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DOI: 10.5660/WTS.2020.9.1.43
Web URL(s):
http://www.weedturf.org/view/N0260090105.pdf
    Last checked: 12/18/2020
    Requires: PDF Reader
http://www.weedturf.org/article/?num=N0260090105
    Last checked: 12/18/2020
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