Various machine-learning models have been extensively applied to predict soil properties using infrared spectroscopy. Beyond the interpretability and transparency of these models, there is an ongoing discussion on the reliability of the prediction of soil properties generated from soil spectra. In this review, we contribute to this discussion by advocating for the integration of soil knowledge into machine-learning models. By doing so, researchers can delve deeper into the underlying soil constituents, ultimately enhancing prediction accuracy. Our review explores the soil information present in spectral data, the fallacy of model interpretability, methods to incorporate soil knowledge into machine-learning techniques, and the ways in which machine learning and soil spectroscopy can assist soil science. The combination of machine learning and domain knowledge is recommended to develop more meaningful models for predicting soil properties within the field of soil science.

中文翻译：

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将土壤知识纳入土壤光谱土壤特性的机器学习预测中

各种机器学习模型已被广泛应用于利用红外光谱预测土壤特性。除了这些模型的可解释性和透明度之外，关于土壤光谱生成的土壤特性预测的可靠性的讨论仍在继续。在这篇综述中，我们通过倡导将土壤知识整合到机器学习模型中来为这一讨论做出贡献。通过这样做，研究人员可以更深入地研究潜在的土壤成分，最终提高预测的准确性。我们的综述探讨了光谱数据中存在的土壤信息、模型可解释性的谬误、将土壤知识纳入机器学习技术的方法，以及机器学习和土壤光谱学如何协助土壤科学。建议将机器学习和领域知识相结合，开发更有意义的模型来预测土壤科学领域的土壤特性。