Evaluation of machine learning approaches to predict soil organic matter and pH using vis-NIR spectra

Sensors (Switzerland)

Volumn 19, Issue 2, 2019, Pages

  Yang, Meihua ^a,b   Xu, Dongyun ^a   Chen, Songchao ^c   Li, Hongyi ^d   Shi, Zhou ^a,e  

^a ZHEJIANG UNIVERSITY   (China)

^b Yuzhang Normal University   (China)

^c Centre d'Orléans   (France)

^d JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^e BEIJING HOSPITAL   (China)

Author keywords

Machine learning approaches; Paddy soil; PH; Soil organic matter; Vis NIR spectra

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOGEOCHEMISTRY; BIOLOGICAL MATERIALS; FORECASTING; GENETIC ALGORITHMS; INFRARED DEVICES; LANDFORMS; LEAST SQUARES APPROXIMATIONS; MEAN SQUARE ERROR; ORGANIC COMPOUNDS; PH; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

COEFFICIENT OF DETERMINATION; LAND MANAGEMENT PRACTICES; LEAST SQUARES SUPPORT VECTOR MACHINES; MACHINE LEARNING APPROACHES; PADDY SOILS; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); SOIL ORGANIC MATTERS; VIS-NIR SPECTRA;

SOILS;

EID: 85060046768     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s19020263     Document Type: Article

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