An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications

TL;DR

This paper presents a model-agnostic method transforming regression into classification to improve uncertainty estimation in soil property prediction, especially under data scarcity, demonstrating superior performance in agricultural datasets.

Contribution

The paper introduces a novel, data-efficient approach for uncertainty estimation in pedometrics by converting regression tasks into classification problems, enabling the use of advanced machine learning algorithms.

Findings

Better uncertainty estimates than traditional pedometric models

Effective in data-scarce soil studies

Validated on German agricultural field datasets

Abstract

This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics and the practice of digital soil mapping. For addressing the typical challenge of data scarcity in soil studies, we present an improved technique for uncertainty estimation. This method is based on the transformation of regression tasks into classification problems, which not only allows for the production of reliable uncertainty estimates but also enables the application of established machine learning algorithms with competitive performance that have not yet been utilized in pedometrics. Empirical results from datasets collected from two German agricultural fields showcase the practical application of the proposed methodology. Our results and findings suggest that the proposed approach has the potential to provide better uncertainty estimation than the models commonly used in pedometrics.