Bu öğeden alıntı yapmak, öğeye bağlanmak için bu tanımlayıcıyı kullanınız: http://hdl.handle.net/11452/26997
Başlık: 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
Yazarlar: Kuang, Boyan
Mouazen, Abdul M.
Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu.
Tekin, Yucel
J-3560-2012
15064756600
Anahtar kelimeler: ANN
Soil
Spectroscopy
Visible and near infrared
Moisture-content
Prediction
Sensor
Calibration
Agreement
Accuracy
Spiking
Models
Matter
Scale
Agriculture
Netherlands
Calibration
Carbon
Infrared devices
Least squares approximations
Mean square error
Meteorological instruments
Near infrared spectroscopy
Neural networks
Organic carbon
Social networking (online)
Soils
Spectrophotometers
Spectroscopy
Measurement accuracy
On-line measurement
Partial least square (PLS)
Partial least squares regressions (PLSR)
Root mean square errors
Visible and near infrared
Visible and near-infrared spectroscopy
Soil surveys
Artificial neural network
Calibration
Clay soil
Comparative study
Concentration (composition)
Least squares method
Mapping
Methodology
Near infrared
pH
Soil analysis
Soil organic matter
Yayın Tarihi: Mar-2015
Yayıncı: Elsevier
Atıf: Kuang, B. vd. (2015). "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". Soil and Tillage Research, 146(Part B), 243-252.
Özet: 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.
URI: https://doi.org/10.1016/j.still.2014.11.002
https://www.sciencedirect.com/science/article/pii/S0167198714002475
http://hdl.handle.net/11452/26997
ISSN: 0167-1987
Koleksiyonlarda Görünür:Scopus
Web of Science

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