Articles | Volume 6, issue 1
SOIL, 6, 35–52, 2020
https://doi.org/10.5194/soil-6-35-2020
SOIL, 6, 35–52, 2020
https://doi.org/10.5194/soil-6-35-2020

Review article 06 Feb 2020

Review article | 06 Feb 2020

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

José Padarian et al.

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Cited articles

Ahmad, S., Kalra, A., and Stephen, H.: Estimating soil moisture using remote sensing data: A machine learning approach, Adv. Water Resour., 33, 69–80, 2010. a
Ahmed, O., Habbani, F. I., Mustafa, A., Mohamed, E., Salih, A., and Seedig, F.: Quality assessment statistic evaluation of X-ray fluorescence via NIST and IAEA standard reference materials, World Journal of Nuclear Science and Technology, 7, 121–128, 2017. a
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A.: Network dissection: Quantifying interpretability of deep visual representations, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6541–6549, 2017. a, b
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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.