Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-553-2025
https://doi.org/10.5194/soil-11-553-2025
Original research article
 | 
22 Jul 2025
Original research article |  | 22 Jul 2025

Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models

Yin-Chung Huang, José Padarian, Budiman Minasny, and Alex B. McBratney

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Cited articles

Angelopoulos, A. N. and Bates, S.: A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.07511, 2022. 
Begoli, E., Bhattacharya, T., and Kusnezov, D.: The need for uncertainty quantification in machine-assisted medical decision making, Nat. Mach. Intell., 1, 20–23, https://doi.org/10.1038/s42256-018-0004-1, 2019. 
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.-M., and McBratney, A.: Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy, TrAC, Trends Anal. Chem., 29, 1073–1081, https://doi.org/10.1016/j.trac.2010.05.006, 2010. 
Bethell, D., Gerasimou, S., and Calinescu, R.: Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction, Proceedings of the AAAI Conference on Artificial Intelligence, 38, 20939–20948, https://doi.org/10.1609/aaai.v38i19.30084, 2024. 
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Short summary
Uncertainty quantification plays a crucial role in reporting machine learning models in soil spectroscopy. This study introduces Monte Carlo conformal prediction (MC-CP), a novel method for uncertainty quantification in deep-learning soil spectral models. MC-CP outperformed two established methods, providing the most reliable results. Its efficiency and robustness make it a practical choice for implementing soil spectral models in decision making.
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