Soil texture prediction with automated deep convolutional neural networks and population-based learning-Reference-Cited by-同舟云学术

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Soil texture prediction with automated deep convolutional neural networks and population-based learning

Published:2023-08 Issue: Volume:436 Page:116521
ISSN:0016-7061
Container-title:Geoderma
language:en
Short-container-title:Geoderma

Author:

Omondiagbe Osayande Pascal^ORCID,Lilburne Linda^ORCID,Licorish Sherlock A.^ORCID,MacDonell Stephen G.^ORCID

Publisher

Elsevier BV

Subject

Soil Science

Reference57 articles.

1. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from B razil by clustering into smaller subsets or use of data mining calibration techniques;Araújo;Eur. J. Soil Sci.,2014

2. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils–Critical review and research perspectives;Bellon-Maurel;Soil Biol. Biochem.,2011

3. FCM: The fuzzy c-means clustering algorithm;Bezdek;Comput. Geosci.,1984

4. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey;Bilgili;J. Arid Environ.,2010

5. Soil survey laboratory methods manual;Burt,2004

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