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Accurate dye tracer concentration estimations using image analysis

Persson, Magnus LU (2005) In Soil Science Society of America Journal 69(4). p.967-975
Abstract
In this paper, the accuracy of dye tracer concentration estimations using image analysis is examined. The variability before and after application of different image correction methods was investigated in three experiments using a digital camera. In each of these experiments, one correction was applied and the remaining variability after correction was calculated as the SD of uniformly colored patches on color scale. Correction for inhomogeneous illumination results in relatively small remaining variability. The variability after correction for different color temperatures (or white point) was larger if the correction was made for image files directly from the camera. However, when using the raw data from the image sensor in the camera,... (More)
In this paper, the accuracy of dye tracer concentration estimations using image analysis is examined. The variability before and after application of different image correction methods was investigated in three experiments using a digital camera. In each of these experiments, one correction was applied and the remaining variability after correction was calculated as the SD of uniformly colored patches on color scale. Correction for inhomogeneous illumination results in relatively small remaining variability. The variability after correction for different color temperatures (or white point) was larger if the correction was made for image files directly from the camera. However, when using the raw data from the image sensor in the camera, the remaining variability was significantly reduced. A calibration experiment was also conducted, in which photographs of calibration samples of dye-stained soil were taken. Between 72 and 260 samples were prepared for each of three soils. The samples had dye concentrations from 0 to 1.5 g L-1. Effects of exposure settings and calibration model were investigated. The exposure settings only affected the results significantly in one soil which became dark when the dye concentration was high. Overexposure made the image lighter and the root mean square error (RMSE) of the concentration estimate decreased for this soil. By applying a neural network (NN) model, the RMSE of the dye concentration estimates could be as low as 0.0747 to 0.0944 g L-1. Reasonable accuracy (0.10-0.13 g L-1) could also be achieved with a polynomial calibration relationship derived from around 20 soil samples. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Soil Science Society of America Journal
volume
69
issue
4
pages
967 - 975
publisher
Soil Science Society of Americ
external identifiers
  • wos:000230760300001
  • scopus:22744446112
ISSN
0361-5995
DOI
10.2136/sssaj2004.0186
language
English
LU publication?
yes
id
267bc3c9-2f88-42a1-9a63-b309ee5b9b4c (old id 229875)
date added to LUP
2007-08-06 08:38:45
date last changed
2017-10-01 04:37:20
@article{267bc3c9-2f88-42a1-9a63-b309ee5b9b4c,
  abstract     = {In this paper, the accuracy of dye tracer concentration estimations using image analysis is examined. The variability before and after application of different image correction methods was investigated in three experiments using a digital camera. In each of these experiments, one correction was applied and the remaining variability after correction was calculated as the SD of uniformly colored patches on color scale. Correction for inhomogeneous illumination results in relatively small remaining variability. The variability after correction for different color temperatures (or white point) was larger if the correction was made for image files directly from the camera. However, when using the raw data from the image sensor in the camera, the remaining variability was significantly reduced. A calibration experiment was also conducted, in which photographs of calibration samples of dye-stained soil were taken. Between 72 and 260 samples were prepared for each of three soils. The samples had dye concentrations from 0 to 1.5 g L-1. Effects of exposure settings and calibration model were investigated. The exposure settings only affected the results significantly in one soil which became dark when the dye concentration was high. Overexposure made the image lighter and the root mean square error (RMSE) of the concentration estimate decreased for this soil. By applying a neural network (NN) model, the RMSE of the dye concentration estimates could be as low as 0.0747 to 0.0944 g L-1. Reasonable accuracy (0.10-0.13 g L-1) could also be achieved with a polynomial calibration relationship derived from around 20 soil samples.},
  author       = {Persson, Magnus},
  issn         = {0361-5995},
  language     = {eng},
  number       = {4},
  pages        = {967--975},
  publisher    = {Soil Science Society of Americ},
  series       = {Soil Science Society of America Journal},
  title        = {Accurate dye tracer concentration estimations using image analysis},
  url          = {http://dx.doi.org/10.2136/sssaj2004.0186},
  volume       = {69},
  year         = {2005},
}