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Soil Physics

Accurate Dye Tracer Concentration Estimations Using Image Analysis

Dep. of Water Resources Engineering, Lund Univ., Box 118, SE-221 00 Lund, Sweden

^* Corresponding author (magnus.persson{at}tvrl.lth.se)

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 a color scale. Correction for inhomogeneous illumination results in a 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.

Abbreviations: CCD, charge coupled device • HSI, hue, saturation, and intensity • HSV, hue, saturation, and value • NN, neural network • RGB, red, green, and blue • RMSE, root mean square error

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