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Machine learning analysis of soil data is also used to draw conclusions on the controls of the distribution of the soil. Several well-known ML applications in soils science include the prediction of soil types and properties via digital soil mapping (DSM) or pedotransfer functions and analysis of infrared spectral data to infer soil properties. 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. Soil science research, in particular, pedometrics, has used statistical models to “learn” or understand from data how soil is distributed in space and time ( McBratney et al., 2019). The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. We foresee that a large number of studies will focus on the latter topic. (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous),Īnd (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships.įrom 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 found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. 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 corpora. 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.
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