Machine learning and soil sciences: a review aided by machine learning tools

Padarian, José; Minasny, Budiman; McBratney, Alex B.

doi:https://doi.org/10.5194/soil-6-35-2020

Articles | Volume 6, issue 1

https://doi.org/10.5194/soil-6-35-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/soil-6-35-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6, issue 1

Review article

| Highlight paper

|

06 Feb 2020

Review article | Highlight paper |

| 06 Feb 2020

Machine learning and soil sciences: a review aided by machine learning tools

José Padarian, Budiman Minasny, and Alex B. McBratney

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Final revised paper (published on 06 Feb 2020)
Preprint (discussion started on 03 Sep 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of Soil 2019-57', Anonymous Referee #1, 20 Sep 2019
- AC1: 'Response to RC1', José Padarian, 27 Nov 2019
RC2: 'my comments', Anonymous Referee #2, 17 Oct 2019
- AC2: 'Response to RC2', José Padarian, 28 Nov 2019
SC1: 'some comment', Wei Shangguan, 25 Oct 2019
- AC3: 'Response to SC1', José Padarian, 30 Nov 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by editor) (30 Nov 2019) by Olivier Evrard

AR by José Padarian on behalf of the Authors (07 Dec 2019) Author's response Manuscript

ED: Publish as is (06 Jan 2020) by Olivier Evrard

ED: Publish as is (07 Jan 2020) by John Quinton (Executive editor)

AR by José Padarian on behalf of the Authors (14 Jan 2020)

Short summary

The application of machine learning (ML) has shown an accelerated adoption in soil sciences. It is a difficult task to manually review all papers on the application of ML. This paper aims to provide a review of the application of ML aided by topic modelling in order to find patterns in a large collection of publications. The objective is to gain insight into the applications and to discuss research gaps. We found 12 main topics and that ML methods usually perform better than traditional ones.