Document Type

Article

Source of Publication

Smart Agricultural Technology

Publication Date

12-1-2025

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 highlight the potential of combining advanced ML techniques with semantic reasoning to improve the interpretability and adoption of Artificial Intelligence in agriculture.

ISSN

2772-3755

Publisher

Elsevier BV

Volume

12

Disciplines

Computer Sciences

Keywords

LLM, Machine learning, Ontology, Soil moisture, XAI

Scopus ID

105011092015

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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