Details

Autor(en) / Beteiligte

• Morellos, Antonios
• Pantazi, Xanthoula-Eirini
• Moshou, Dimitrios
• Alexandridis, Thomas
• Whetton, Rebecca
• Tziotzios, Georgios
• Wiebensohn, Jens
• Bill, Ralf
• Mouazen, Abdul M.

Titel

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

Ist Teil von

• Biosystems engineering, 2016-12, Vol.152, p.104-116

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• It 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). •LS-SVM, Cubist, PCR and PLSR were used for VIS-NIR prediction of TN, OC, and MC.•MC and OC are best predicted by LS-SVM and TN is best predicted by the Cubist.•Machine learning methods perform better than the multivariate regression methods.