Prediction of soil Sand and clay contents via visible and near-infrared (Vis-NIR) spectroscopy

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dc.contributor.authorMouazen, Abdul M.
dc.contributor.authorKim, P.
dc.contributor.authorAnalide, C.
dc.contributor.buuauthorTümsavaş, Zeynal
dc.contributor.buuauthorTekin, Yücel
dc.contributor.buuauthorUlusoy, Yahya
dc.contributor.departmentUludağ Üniversitesi/Fen Bilimleri Enstitüsü/Toprak Bilimi ve Bitki Besleme.tr_TR
dc.contributor.departmentUludağ Üniversitesi/Teknik Bilimler Meslek Yüksek Okulu/Makine ve Metal Teknolojileri/Tarım Makineleri.tr_TR
dc.contributor.researcheridECX-5291-2022tr_TR
dc.contributor.researcheridECV-1720-2022tr_TR
dc.contributor.researcheridAAG-6056-2021tr_TR
dc.date.accessioned2023-09-08T11:05:48Z
dc.date.available2023-09-08T11:05:48Z
dc.date.issued2017
dc.descriptionBu çalışma, 21-22, Ağustus 2017 tarihlerinde Seul[Güney Kore]’de düzenlenen 13. International Conference on Intelligent Environments (IE) Kongresi‘nde bildiri olarak sunulmuştur.tr_TR
dc.description.abstractVisible and near infrared (Vis-NIR) spectroscopy is a non-destructive analytical method that can be used to complement, enhance or potentially replace conventional methods of soil analysis. The aim of this research was to predict the particle size distribution (PSD) of soils using a Vis-NIR) spectrophotometry in one irrigate field having a vertisol clay texture in the Karacabey district of Bursa Province, Turkey. A total of 86 soil samples collected from the study area were subjected to optical scanning in the laboratory with a portable, fiber-type Vis-NIR spectrophotometer (AgroSpec, tec5 Technology for Spectroscopy, Germany). Before the partial least square regression (PLSR) analysis, the entire reflectance spectra were randomly split into calibration (80%) and validation (20%) sets. A leave-one-out cross-validation PLSR analysis was carried out using the calibration set with Unscrambler (R) software, whereas the model prediction ability was tested using the validation (prediction) set. Models developed were used to predict sand and clay content using on-line collected spectra from the field. Results showed an "excellent" laboratory prediction performance for both sand (R-2 = 0.81, RMSEP = 3.84% and RPD = 2.32 in cross-validation; R-2 = 0.90, RMSEP = 2.91% and RPD = 2.99 in the prediction set) and clay (R-2 = 0.86, RMSEP = 3.4% and RPD = 2.66 in cross validation; R-2 = 0.92, RMSEP = 2.67% and RPD = 3.14 in the prediction set). Modelling of silt did not result in any meaningful correlations. Less accurate on-line predictions were recorded compared to the laboratory results, although the on-line predictions were very good (RPD = 2.24-2.31). On-line predicted maps showed reasonable spatial similarity to corresponding laboratory measured maps. This study proved that soil sand and clay content can be successfully measured and mapped using Vis-NIR spectroscopy under both laboratory and on-line scanning conditions.en_US
dc.identifier.citationTümsavaş, Z. vd. (2017). ''Prediction of soil Sand and clay contents via visible and near-infrared (Vis-NIR) spectroscopy''. ed, C. Anelide ve P. Kim. Ambient Intelligence and Smart Environments, Intelligent Environments 2017, 22, 29-38.en_US
dc.identifier.endpage38tr_TR
dc.identifier.issn1875-4163
dc.identifier.issn978-1-61499-796-2978-1-61499-795-5
dc.identifier.startpage29tr_TR
dc.identifier.urihttps://doi.org/10.3233/978-1-61499-796-2-29
dc.identifier.urihttps://ebooks.iospress.nl/publication/47211
dc.identifier.urihttp://hdl.handle.net/11452/33800
dc.identifier.volume22tr_TR
dc.identifier.wos000449170800006tr_TR
dc.indexed.wosCPCISen_US
dc.language.isoenen_US
dc.publisherIos Pressen_US
dc.relation.collaborationYurt dışıtr_TR
dc.relation.journalAmbient Intelligence and Smart Environments, Intelligent Environments 2017en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararasıtr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer scienceen_US
dc.subjectPLS regression analysisen_US
dc.subjectSanden_US
dc.subjectClayen_US
dc.subjectVis-NIR spectroscopyen_US
dc.subjectDiffuse-reflectance spectroscopyen_US
dc.subjectArtificial neural-networken_US
dc.subjectMoisture-contenten_US
dc.subjectOrganic-carbonen_US
dc.subjectLeast-squaresen_US
dc.subjectQualityen_US
dc.subjectPhen_US
dc.subjectSpectraen_US
dc.subject.wosComputer science, artificial intelligenceen_US
dc.subject.wosComputer science, interdisciplinary applicationsen_US
dc.titlePrediction of soil Sand and clay contents via visible and near-infrared (Vis-NIR) spectroscopyen_US
dc.typeBook

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