Browsing by Author "Kuang, Boyan"

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    Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content
    (Elsevier, 2015-03) Kuang, Boyan; Mouazen, Abdul M.; Tekin, Yucel; Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu.; J-3560-2012; 15064756600
    Soil organic carbon (OC), pH and clay content (CC) can be measured with on-line visible and near infrared spectroscopy (vis-NIRS), whose calibration method may considerably affect the measurement accuracy. The aim of this study was to compare artificial neural network (ANN) and partial least squares regression (PLSR) for the calibration of a visible and near infrared (vis-NIR) spectrophotometer for the on-line measurement of OC, pH and CC in two fields in a Danish farm. An on-line sensor platform equipped with a mobile, fiber type, vis-NIR spectrophotometer (AgroSpec from tec5 Technology for Spectroscopy, Germany), with a measurement range of 305-2200 nm was used to acquire soil spectra in diffuse reflectance mode. Both ANN and PLSR calibration models of OC, pH and CC were validated with independent validation sets. Comparison and full-point maps were developed using ArcGIS software (ESRI, USA). Results of the on-line independent validation showed that ANN outperformed PLSR in both fields. For example, residual prediction deviation (RPD) values for on-line independent validation in Field 1 were improved from 1.93 to 2.28, for OC, from 2.08 to 2.31 for pH and from 1.98 to 2.15 for CC, after ANN analyses as compared to PLSR, whereas root mean square error (RMSEP) values decreased from 1.48 to 1.25%, for OC, from 0.13 to 0.12 for pH and from 1.05 to 0.96% for CC. The comparison maps showed better spatial similarities between laboratory and ANN predicted maps (higher kappa values), as compared to PLSR predicted maps. In most cases, more detailed full-point maps were developed with ANN, although the size of spots with high concentration of PLSR maps matches the measured maps better. Therefore, it was recommended to adopt the ANN for on-line prediction of DC, pH and CC.
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    Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations
    (MDPI, 2013-07-31) Kuang, Boyan; Mouazen, Abdul M.; Tekin, Yücel; Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu.; J-3560-2012; 15064756600
    This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R-2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R-2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.