Soil property prediction: An extreme learning machine approach

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18 Scopus citations

Abstract

In this paper, we propose a method for predicting functional properties of soil samples from a number of measurable spatial and spectral features of those samples. Our method is based on Savitzky-Golay filter for preprocessing and a relatively recent evolution of single hidden layer feed-forward network (SLFN) learning technique called extreme learning machine (ELM) for prediction. We tested our method with Africa Soil Property Prediction dataset, and observed that the results were promising.

Original languageBritish English
Title of host publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
EditorsWeng Kin Lai, Qingshan Liu, Tingwen Huang, Sabri Arik
PublisherSpringer Verlag
Pages18-27
Number of pages10
ISBN (Print)9783319265346
DOIs
StatePublished - 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9490
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing, ICONIP 2015
Country/TerritoryTurkey
CityIstanbul
Period9/11/1512/11/15

Keywords

  • Extreme learning machine (ELM)
  • Kernel-based ELM
  • Neural network
  • Prediction
  • Soil property

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