A Case Study of eXplainable AI in Smart Agriculture. Lessons Learned for Information Systems Engineers
Document Type
Conference Proceeding
Source of Publication
Lecture Notes in Business Information Processing
Publication Date
6-7-2026
Abstract
While Vision-Language Models (VLMs) show promise in smart agriculture, they often function as black boxes, lacking the domain-specific transparency needed for farmer trust. Current models suffer from hallucinations or fail to provide actionable context. This paper addresses these limitations by proposing a novel integration of the BLIP-2 multimodal model with the Olive Leaf Disease Ontology. Following design science principles, our approach grounds AI-generated explanations in a structured knowledge graph to ensure semantic consistency. Results demonstrate that combining VLMs with ontologies effectively eliminates hallucinations and provides transparent, practically valuable insights into disease causes and treatments for agricultural decision support.
DOI Link
ISBN
[9783032279965]
ISSN
Publisher
Springer Nature Switzerland
Volume
587 LNBIP
First Page
28
Last Page
36
Disciplines
Computer Sciences
Keywords
eXplainable AI, Knowledge graphs, Ontologies, Sustainable agriculture, Vision–language models
Scopus ID
Recommended Citation
Grati, Rima; Fattouch, Najla; Boukadi, Khouloud; De Luzi, Francesca; Mecella, Massimo; and Seffah, Ahmed, "A Case Study of eXplainable AI in Smart Agriculture. Lessons Learned for Information Systems Engineers" (2026). All Works. 8035.
https://zuscholars.zu.ac.ae/works/8035
Indexed in Scopus
yes
Open Access
no