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Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy

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Title Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy
 
Creator Sarathjith, MC
 
Contributor Das, Bhabani Sankar
Wani, Suhas
Sahrawat, Kanwar Lal
Gupta, Arobinda
 
Subject diffuse reflectance spectroscopy
discrete wavelet transformation
partial-least-squares regression
soil nutrient contents
support vector regression
 
Description Diffuse reflectance spectroscopy (DRS) operating in wavelength range of 350–2500 nm is emerging as a rapid and noninvasive
approach for estimating soil nutrient content. The success of the DRS approach relies on the ability of the data mining algorithms to
extract appropriate spectral features while accounting for nonlinearity
and complexity of the reflectance spectra. There is no
comparative assessment of spectral algorithms for estimating nutrient content of Indian soils. We compare the performance of
partialleastsquares
regression (PLSR), support vector regression (SVR), discrete wavelet transformation (DWT) and their combinations
(DWT–PLSR and DWT–SVR) to estimate soil nutrient content. The DRS models were generated for extractable phosphorus (P),
potassium (K), sulphur (S), boron (B), zinc (Zn), iron (Fe) and aluminium (Al) content in Vertisols and Alfisols and were compared using
residual prediction deviation (RPD) of validation dataset. The best DRS models yielded accurate predictions for P (RPD = 2.27), Fe
(RPD = 2.91) in Vertisols and Fe (RPD = 2.43) in Alfisols, while B (RPD = 1.63), Zn (RPD = 1.49) in Vertisols and K (RPD = 1.89), Zn
(RPD = 1.41) in Alfisols were predicted with moderate accuracy. The DWT–SVR outperformed all other approaches in case of P, K and
Fe in Vertisols and P, K and Zn in Alfisols; whereas the PLSR approach was better for B, Zn and Al in Vertisols and B, Fe and Al in
Alfisols. The DWT–SVR approach yielded parsimonious DRS models with similar or better prediction accuracy than PLSR approach.
Hence, the DWT–SVR may be considered as a suitable data mining approach for estimating soil nutrients in Alfisols and Vertisols of
India.
 
Date 2016-03-25
2017-02-08T23:20:01Z
2017-02-08T23:20:01Z
 
Type Journal Article
 
Identifier http://oar.icrisat.org/id/eprint/9480; http://www.currentscience.ac.in/Volumes/110/06/1031.pdf
https://mel.cgiar.org/reporting/download/hash/cIB16HWk
MC Sarathjith, Bhabani Sankar Das, Suhas Wani, Kanwar Lal Sahrawat, Arobinda Gupta. (25/3/2016). Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy. Current Science, 110(6), pp. 1031-1037.
https://hdl.handle.net/20.500.11766/5607
Open access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher Indian Academy of Sciences
 
Source Current Science;110,(2016) Pagination 1031,1037