Premium
Software for Measuring Root Characters from Digital Images
Author(s) -
Ingram Keith T.,
Leers Gary A.
Publication year - 2001
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2001.934918x
Subject(s) - root (linguistics) , length measurement , mathematics , measure (data warehouse) , software , digital image , operator (biology) , field (mathematics) , statistics , computer science , image (mathematics) , image processing , computer vision , optics , physics , biology , data mining , programming language , philosophy , linguistics , biochemistry , repressor , transcription factor , gene , pure mathematics
Root research methods are often tedious, labor intensive, and prone to large variability. Minirhizotron technology has the potential to greatly enhance root research capabilities, but quantifying minirhizotron data is very time consuming. This note presents new software that allows rapid, accurate measurement of root length from digital images—Root Measurement System (RMS). In addition to measuring root lengths and diameters, RMS records number of roots in an image and calculates their total volume, total surface area, and length density. Ten untrained RMS users averaged 654 ± 42 s to measure the length of a 24 mm s‐shaped line 10 times. The standard error of the mean for repeated length measurements was <0.1 mm for all but one of the operators. In a subsequent test with 10 different operators having various levels of experience, operators averaged a total of 2324 ± 213 s to measure the lengths and diameters of 10 images of pseudo roots made from wires. There was no significant difference among operators for total length measured, but operators did differ in lengths apportioned among 0.1‐mm‐diam. classes. For minirhizotron images collected in a field study, an experienced operator could analyze from 17 to 38 images h −1 depending on number and length of roots in the images. With its speed, accuracy, and versatility, RMS offers the possibility to analyze sufficient numbers of minirhizotron images to allow detection of treatment effects in field research.