Evaluation of Key Methodology for Digital Image Analysis of Turfgrass Color Using Open‐Source Software. Issue 2 (26th October 2016)

• Record Type: Journal Article
• Authors: Zhang, Chenxi; Pinnix, Garland D.; Zhang, Zheng; Miller, Grady L.; Rufty, Thomas W.
• Is Part Of: Crop science. Volume 57:Issue 2(2017)
• Page Start: 550
• Page End: 558
• Publication Date: 2016-10-26
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.2135/cropsci2016.04.0285
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract : Digital image analysis is a frequently used research technique to provide an objective measure of turfgrass color, in addition to the traditional visual rating. A commonly used method relies on commercial software package SigmaScan Pro to quantify mean hue angle, saturation, and brightness values from turf images, and to calculate a dark green color index as the measure of color. To enable turf image analysis to function on an open‐source platform, a method was developed within ImageJ to batch process turf images for color parameters. This Java‐based ImageJ plugin quantifies hue angle, saturation, and brightness values and calculates a dark green color index. In addition, information on the variability of these color parameters can be simultaneously acquired. This new method was used to quantify color parameters of turf images collected from field plots of tall fescue ( Schedonorus arundinacea Shreb. Dumort.), Kentucky bluegrass ( Poa pratensis L.), ryegrass ( Lolium ssp.), hybrid bermudagrass ( Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy), and creeping bentgrass ( Agrostis stolonifera L.). While color parameter values differed little between ImageJ and SigmaScan, the time saved in processing images using ImageJ was considerable. Aside from software, analysis of color parameters acquired from the five turfgrass species indicated that hue angle alone can adequately measure turf color in digital images. Results also demonstrated that, in addition to light source, camera settings should remain fixed during photo capture to avoid introducing errors. The ImageJ plug‐in developed in this study is made available at www.turffiles.ncsu.edu .