Work

LLM-driven semantic explanations for soil moisture prediction models

Year: 2025

Type: article

Abstract: Efficient soil moisture prediction is crucial for sustainable agricultural practices, especially in the face of climate change and increasing water scarcity. However, the adoption of machine learning (ML) models in this context is frequently limited by their lack of interpretability, particularly among non-expert users such as farmers. This study proposes a novel approach to soil moisture prediction that combines high predictive performance with enhanced explainability. We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. The ontology formalizes essential agricultural concepts and their interrelationships, enabling semantically rich explanations to bridge the gap between sophisticated model results and practical decision-making. Our approach is exemplified by a prototype system that provides both predictions and user-friendly explanations. The findings highlights the potential of combining advanced ML techniques with semantic reasoning to improve the interpretability and adoption of Artificial Intelligence in agriculture.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101174

Authors: Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

Institutions: Faculty of Economics and Management of Sfax, Zayed University, University of Gabès, Sapienza University of Rome

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,770