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dc.contributorDas, Bhabani Sankaren_US
dc.contributorWani, Suhasen_US
dc.contributorSahrawat, Kanwar Lalen_US
dc.contributorGupta, Arobindaen_US
dc.creatorSarathjith, MCen_US
dc.date.accessioned2017-02-08T23:20:01Z
dc.date.available2017-02-08T23:20:01Z
dc.identifierhttp://oar.icrisat.org/id/eprint/9480; http://www.currentscience.ac.in/Volumes/110/06/1031.pdfen_US
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/cIB16HWken_US
dc.identifier.citationMC 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.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/5607
dc.description.abstractDiffuse 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.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherIndian Academy of Sciencesen_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceCurrent Science;110,(2016) Pagination 1031,1037en_US
dc.subjectdiffuse reflectance spectroscopyen_US
dc.subjectdiscrete wavelet transformationen_US
dc.subjectpartial-least-squares regressionen_US
dc.subjectsoil nutrient contentsen_US
dc.subjectsupport vector regressionen_US
dc.titleComparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopyen_US
dc.typeJournal Articleen_US
dcterms.available2016-03-25en_US
dcterms.extent1031-1037en_US
cg.subject.agrovocsoilen_US
cg.contributor.centerInternational Crops Research Institute for the Semi-Arid Tropics - ICRISATen_US
cg.contributor.centerIndian Institute of Technology Kharagpur - IITKen_US
cg.contributor.crpCRP on Dryland Systems - DSen_US
cg.contributor.funderNot Applicableen_US
cg.coverage.regionSouthern Asiaen_US
cg.coverage.countryINen_US
cg.contactK.SAHRAWAT@CGIAR.ORGen_US
cg.isijournalISI journalen_US
dc.identifier.statusOpen accessen_US
mel.impact-factor0.967en_US
cg.issn0011-3891en_US
cg.journalCurrent Scienceen_US
cg.issue6en_US
cg.volume110en_US


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