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Discussion papers
https://doi.org/10.5194/soil-2019-57
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-2019-57
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: review article 03 Sep 2019

Submitted as: review article | 03 Sep 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal SOIL (SOIL).

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

José Padarian, Budiman Minasny, and Alex B. McBratney José Padarian et al.
  • Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, New South Wales, Australia

Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (Latent Dirichlet Allocation) to find patterns in a large collection of text corpus. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that: a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, b) the reviewed publication can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, SVM), spectroscopy, modelling (classes), crops, physical and modelling (continuous), c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular: about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

José Padarian et al.
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Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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José Padarian et al.
José Padarian et al.
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Latest update: 09 Dec 2019
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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.
The application of machine learning (ML) has shown an accelerated adoption in soil sciences. It...
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