Premium
Quantifying Turfgrass Cover Using Digital Image Analysis
Author(s) -
Richardson M. D.,
Karcher D. E.,
Purcell L. C.
Publication year - 2001
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2001.1884
Subject(s) - cynodon dactylon , digital image analysis , cover (algebra) , digital image , pixel , zoysia japonica , software , statistics , sampling (signal processing) , agronomy , mathematics , remote sensing , biology , computer science , image processing , image (mathematics) , artificial intelligence , computer vision , engineering , mechanical engineering , filter (signal processing) , geology , programming language
Accurate cover estimates in turfgrass research plots are often difficult to obtain because of the time involved with traditional sampling and evaluation techniques. Subjective ratings are commonly used to estimate turfgrass cover, but the data can be quite variable and difficult to reproduce. New technologies and software related to digital image analysis (DIA) may provide an alternative method to measure turfgrass parameters more accurately and efficiently than current techniques. A series of studies was conducted to determine the applicability of DIA for turfgrass cover estimates. In the first study, plots containing a range (1–16) of bermudagrass [ Cynodon dactylon (L.) Pers.] plugs of specific diameter (15.0 cm) were established to represent values of turfgrass cover from 0.75 to 12%, by 0.75% increments. Digital images (1280 by 960 pixels) were taken with a digital camera and processed for percent green color to a software package. Estimates of green turfgrass cover by DIA were highly correlated ( r 2 > 0.99) to the calculated values of turfgrass cover. In a second study, DIA of turfgrass cover was compared by subjective analysis (SA) and line‐intersect analysis (LIA) methods for estimating cover in eight plots of zoysiagrass ( Zoysia japonica Steudel). The mean variance of percent cover determined by DIA (0.65) was significantly lower than SA (99.12) or LIA (13.18). Digital image analysis proved to be an effective means of determining turfgrass cover, producing both accurate and reproducible data. In addition, the technique effectively removes the inherent error and evaluator bias commonly associated with subjective ratings.