The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 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 corpora. 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 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), and (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.

中文翻译：

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机器学习和土壤科学：借助机器学习工具进行的回顾

机器学习（ML）技术在各个科学领域中的应用已迅速增加，尤其是在最近十年中。可以从远程和近端有效获取的土壤数据的可用性不断提高，以及可免费获得的开放源代码算法，导致加速采用ML技术来分析土壤数据。鉴于出版物数量众多，要手动审查所有有关ML在土壤科学中的应用的论文而不缩小特定研究问题中ML应用的叙述是不可能的。本文旨在对ML技术在土壤科学中的应用进行全面的综述，并借助ML算法​​（潜在的狄利克雷分配）在大量文本语料库中查找模式。目的是深入了解ML在土壤科学中的应用出版物，并讨论该主题中的研究空白。我们发现（a）土壤科学中ML方法的使用在增加，主要集中在发达国家，（b）审阅的出版物可以分为12个主题，即遥感，土壤有机碳，水，污染，方法（集合），侵蚀和母体材料，方法（NN，神经网络，SVM，支持向量机），光谱学，建模（类），作物，物理和建模（连续），以及（c）高级ML方法通常表现更好比简单的方法要好，因为它们具有捕获非线性关系的能力。从这些发现中，我们发现了研究方面的差距，尤其是在避免过度拟合方面应采取的预防措施（简约），ML模型的可解释性是应用高级ML方法以提高我们对土壤的了解和理解时要考虑的重要方面。我们预见，大量的研究将集中在后一个主题上。