LLM and Metamodeling for Model Extraction from Smart Agriculture Requirements

Hamza Abdelmalek (), Mohammed Ait Oussouss (), Abdeslam Jakimi (), Rajae Gaamouche (), Rachid Saadane () and Abdellah Chehri ()
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Hamza Abdelmalek: UMI-Meknes, GL-ISI Team, Faculty of Science and Technology Errachidia
Mohammed Ait Oussouss: UMI-Meknes, GL-ISI Team, Faculty of Science and Technology Errachidia
Abdeslam Jakimi: UMI-Meknes, GL-ISI Team, Faculty of Science and Technology Errachidia
Rajae Gaamouche: EMSI Rabat, SmartiLab Laboratory
Rachid Saadane: SIRC-LAGES, Hassania School of Public Works, Electrical Engineering Department
Abdellah Chehri: Royal Military College of Canada, Department of Mathematics and Computer Science

A chapter in Generative AI and Optimization Techniques for Sustainable Water Management, 2026, pp 183-193 from Springer

Abstract: Abstract Smart agriculture demands software that connects sensing, control, and governance across heterogeneous assets. However, turning informal requirements into formal models for this software remains difficult, particularly deriving platform-independent models (PIM) in model-driven architecture that can be transformed into platform-specific models and code. In this paper, we automate the extraction of a PIM from requirements for smart irrigation. Our contribution is a metamodel, along with a multi-stage pipeline that constructs a PIM using large language models. In a case study, the pipeline completed model construction in 28 min, compared to two hours for the manual baseline, resulting in a 76.96% time savings and a 4.34 × productivity gain.

Keywords: Large language models; Model-driven architecture; Model-driven engineering; Requirements engineering; Smart agriculture (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-032-19012-3_11

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DOI: 10.1007/978-3-032-19012-3_11

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