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

Biosystems Engineering

Volumn 152, Issue , 2016, Pages 104-116

  Morellos, Antonios ^a   Pantazi, Xanthoula Eirini ^a   Moshou, Dimitrios ^a   Alexandridis, Thomas ^a   Whetton, Rebecca ^b   Tziotzios, Georgios ^a   Wiebensohn, Jens ^c   Bill, Ralf ^c   Mouazen, Abdul M ^b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b CRANFIELD UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF ROSTOCK   (Germany)

Author keywords

Chemometrics; Data mining; Soil properties; VIS NIR spectroscopy

Indexed keywords

DATA MINING; FORECASTING; INFRARED DEVICES; MACHINE LEARNING; MEAN SQUARE ERROR; MOISTURE; MOISTURE DETERMINATION; NEAR INFRARED SPECTROSCOPY; NITROGEN; ORGANIC CARBON; SOIL SURVEYS; SOILS; SUPPORT VECTOR MACHINES;

CHEMOMETRICS; LEAST SQUARES SUPPORT VECTOR MACHINES; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); PRINCIPAL COMPONENT REGRESSION; ROOT-MEAN-SQUARE ERROR OF PREDICTIONS; SOIL PROPERTY; VIS-NIR SPECTROSCOPY; VISIBLE AND NEAR INFRARED;

LEAST SQUARES APPROXIMATIONS;

EID: 84971620502     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2016.04.018     Document Type: Article

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