Enhancing decision support in crop production: Analyzing conformal prediction for uncertainty quantification
Year: 2025
Type: article
Abstract: Assessing the confidence of machine learning models is increasingly important for enhancing decision support in digital agriculture. Addressing this challenge involves identifying and classifying sources of uncertainty and applying a suitable quantification method. To fully realize the potential of uncertainty quantification in digital agriculture, it is necessary to explore and evaluate different techniques, demonstrating their effectiveness in diverse scenarios. In this paper, we focus on the conformal prediction (CP) framework, specifically inductive conformal prediction (ICP), as a non-parametric tool for uncertainty quantification. Conformal prediction has gained a foothold for its multiple advantages, including being model-agnostic – requiring no retraining or changes to model architecture – and computationally efficient. Moreover, CP operates under a distribution-free framework, making no assumptions about the data, and provides calibrated uncertainty estimates where empirical errors match pre-defined theoretical levels. These properties make it particularly well-suited for agricultural tasks, where datasets often exhibit variability across regions and lack reliable ground truth labels. Through ablation studies, we comprehensively analyze ICP’s performance as a post-hoc approach for ResNet-18 and ViT-B/16, demonstrating that both achieve the required pre-defined error levels. We compare ICP performance against other methods in diverse agricultural tasks, including out-of-distribution detection and covariate shift. Our experiments highlight CP’s unique benefits, such as validity, efficiency, and low computational overhead, making it a promising approach for developing more reliable agricultural machine learning systems.
Authors: Mohamed Farag, Ahmed Emam, Johannes Leonhardt, Ribana Roscher
Institutions: University of Bonn, Forschungszentrum Jülich
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $3,870
Funder: Deutsche Forschungsgemeinschaft
Awards: SFB 1502: Regionaler Klimawandel: Die Rolle von Landnutzung und Wassermanagement, FOR 5351: KIFOR Automatisierung und künstliche Intelligenz zur Überwachung und Entscheidungsfindung bei Gartenbaukulturen (AID4Crops), EXC 2070: PhenoRob Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion, MapInWild: Kartierung und Interpretation von Wildnis aus dem Weltraum
OpenAlex ID: W4411099832