Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy

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dc.contributor.authorMorellos, Antonios
dc.contributor.authorPantazi, Xanthoula-Eirini
dc.contributor.authorMoshou, Dimitrios
dc.contributor.authorAlexandridis, Thomas
dc.contributor.authorWhetton, Rebecca
dc.contributor.authorTziotzios, Georgios
dc.contributor.authorWiebensohn, Jens
dc.contributor.authorBill, Ralf
dc.contributor.authorMouazen, Abdul Mounem
dc.date.accessioned2016-07-06T16:10:58Z
dc.date.available2016-07-06T16:10:58Z
dc.date.issued2016-05-24
dc.description.abstractIt is widely known that the visible and near infrared (VIS-NIR) spectroscopy has the potential of estimating soil total nitrogen (TN), organic carbon (OC) and moisture content (MC) due to the direct spectral responses these properties have in the near infrared (NIR) region. However, improving the prediction accuracy requires advanced modelling techniques, particularly when measurement is planned for fresh (wet and un-processed) soil samples. The aim of this work is to compare the predictive performance of two linear multivariate and two machine learning methods for TN, OC and MC. The two multivariate methods investigated included principal component regression (PCR) and partial least squares regression (PLSR), whereas the machine learning methods included least squares support vector machines (LS-SVM), and Cubist. A mobile, fibre type, VIS-NIR spectrophotometer was utilised to collect soil spectra (305–2200 nm) in diffuse reflectance mode from 140 wet soil samples collected from one field in Germany. The results indicate that machine learning methods are capable of tackling non-linear problems in the dataset. LS-SVMs and the Cubist method out-performed the linear multivariate methods for the prediction of all three soil properties studied. LS-SVM provided the best prediction for MC (root mean square error of prediction (RMSEP) = 0.457% and residual prediction deviation (RPD) = 2.24) and OC (RMSEP = 0.062% and RPD = 2.20), whereas the Cubist method provided the best prediction for TN (RMSEP = 0.071 and RPD = 1.96).en_UK
dc.identifier.citationAntonios Morellos, Xanthoula-Eirini Pantazi, Dimitrios Moshou, Thomas Alexandridis, Rebecca Whetton, Georgios Tziotzios, Jens Wiebensohn, Ralf Bill, Abdul M. Mouazen, Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy, Biosystems Engineering, Volume 152, December 2016, pp104-116en_UK
dc.identifier.issn1537-5110
dc.identifier.urihttp://dx.doi.org/10.1016/j.biosystemseng.2016.04.018
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/10106
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectVIS-NIR spectroscopyen_UK
dc.subjectData miningen_UK
dc.subjectChemometricsen_UK
dc.subjectSoil propertiesen_UK
dc.titleMachine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopyen_UK
dc.typeArticleen_UK

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