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LLM and Metamodeling for Model Extraction from Smart Agriculture Requirements

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hamza Abdelmalek & Mohammed Ait Oussouss & Abdeslam Jakimi & Rajae Gaamouche & Rachid Saadane & Abdellah Chehri, 2026. "LLM and Metamodeling for Model Extraction from Smart Agriculture Requirements," Springer Optimization and Its Applications,, Springer.
    • Handle: RePEc:spr:spochp:978-3-032-19012-3_11
    DOI: 10.1007/978-3-032-19012-3_11
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