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

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.departmentTeknik Bilimler Meslek Yüksekokulu
dc.contributor.departmentFen Bilimleri Enstitüsü
dc.contributor.departmentMakine ve Metal Teknolojileri
dc.contributor.departmentToprak Bilimi ve Bitki Besleme
dc.contributor.departmentTarım Makineleri
dc.contributor.researcheridECX-5291-2022
dc.contributor.researcheridECV-1720-2022
dc.contributor.researcheridAAG-6056-2021
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.
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.
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.
dc.identifier.endpage38
dc.identifier.issn1875-4163
dc.identifier.issn978-1-61499-796-2978-1-61499-795-5
dc.identifier.startpage29
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.volume22
dc.identifier.wos000449170800006
dc.indexed.wosCPCIS
dc.language.isoen
dc.publisherIos Press
dc.relation.collaborationYurt dışı
dc.relation.journalAmbient Intelligence and Smart Environments, Intelligent Environments 2017
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputer science
dc.subjectPLS regression analysis
dc.subjectSand
dc.subjectClay
dc.subjectVis-NIR spectroscopy
dc.subjectDiffuse-reflectance spectroscopy
dc.subjectArtificial neural-network
dc.subjectMoisture-content
dc.subjectOrganic-carbon
dc.subjectLeast-squares
dc.subjectQuality
dc.subjectPh
dc.subjectSpectra
dc.subject.wosComputer science, artificial intelligence
dc.subject.wosComputer science, interdisciplinary applications
dc.titlePrediction of soil Sand and clay contents via visible and near-infrared (Vis-NIR) spectroscopy
dc.typeBook
dspace.entity.typePublication
local.contributor.departmentFen Bilimleri Enstitüsü/Toprak Bilimi ve Bitki Besleme
local.contributor.departmentTeknik Bilimler Meslek Yüksekokulu/Makine ve Metal Teknolojileri/Tarım Makineleri
local.indexed.atWOS

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