Rejections Based on Predictive Uncertainty Enable Reliable Routine Soil Spectroscopy

2026-06-19

AI Summary

Traditional physicochemical soil analysis is costly and time-consuming, while spectral methods combined with machine learning, though efficient, often lack the predictive reliability required for laboratory-grade accuracy. This study proposes a “reject-and-retest” framework that introduces, for the first time in soil spectroscopy, a rejection mechanism based on prediction uncertainty. By leveraging tabular foundation models such as TabPFNv2.5 and TabICLv2 to quantify uncertainty from visible–near-infrared spectra, the approach automatically triggers conventional retesting for low-confidence predictions. Experiments on the Quebec Soil Spectral Library demonstrate that this method significantly reduces analytical costs while meeting user-specified accuracy thresholds, thereby enabling an AI-driven, adaptive measurement workflow that facilitates the integration of spectral techniques into routine laboratory practice.

Abstract

Soil properties relevant to agricultural and environmental applications are conventionally measured using elaborate laboratory methods involving physical and chemical processing. While highly accurate, these conventional methods are costly and time-consuming. In contrast, optical spectroscopy paired with machine learning enables rapid and cost-effective predictions of multiple soil properties. However, spectroscopic modelling is often considered unreliable, as the predictive accuracy varies between soil properties and individual samples. To balance this trade-off between cost and reliability, we introduce reject-to-remeasure: an AI-based measurement framework that combines probabilistic modelling with uncertainty-guided rejection. In this framework, soil samples are first analysed using spectroscopy, after which predictions are rejected if their predictive uncertainty exceeds predefined quality constraints. Rejected samples are subsequently remeasured using conventional laboratory procedures. On a regional visible-near-infrared spectral soil library from Québec, we demonstrate that reject-to-remeasure with modern foundation models (TabPFNv2.5 and TabICLv2) can facilitate the integration of optical spectroscopy into routine laboratory workflows while meeting user-defined accuracy requirements and reducing measurement costs.

Problem

• soil spectroscopy
• predictive uncertainty
• measurement reliability
• accuracy requirements
• routine soil analysis

Innovation

• predictive uncertainty
• reject-to-remeasure
• soil spectroscopy
• probabilistic modelling
• foundation models