[PhD student] Teiki Raihauti

[PhD student] Teiki Raihauti: Semantic and modular representation of crop models

Thesis topic cofunded by #DigitAg

Semantic and modular representation of crop models

T Raihauti

I'm a PhD student at the Laboratoire d'Écophysiologie des Plantes Sous Stress Environnementaux (LEPSE-INRAE), and at the AGAP institute (CIRAD). My background is in computer science, having completed a bachelor's degree in Computer Science at the Montpellier Faculty of Science, as well as a master's degree in IMAGINA, with a focus on Images, Games and Intelligent Agents. I then started working as a research engineer at INRAe in the Eco&Sols unit, and then at LEPSE, which gave me a taste for research and the desire to pursue a thesis.
Crop2ML is a system for developing crop model components and facilitating the exchange of these components between modeling platforms.
This work was carried out during CyrilleMidingoyi's thesis, and the aim of my thesis is to overcome certain limitations of this system, such as the absence of semantics to facilitate their composition, as well as to facilitate the modularity of models at process level.
Crop models are widely used in agriculture, but in different formalisms, languages and structures, making it difficult to share components. "The Agricultural Model Exchange Initiative (AMEI) has been studying this subject for several years, and my work will enable better knowledge sharing in crop models, a key point in agronomic research.

  • Starting date : 1st October 2023
  • University: Institut Agro Montpellier
  • PhD student: GAIA
  • Scientific field: BIDAP-Agronomic sciences
  • Thesis management: Pierre Martre, UMR Lepse, Inrae
  • Thesis supervisors: Pierre Martre, UMR Lepse, Inrae et Christophe Pradal, UMR Agap, Cirad
  • Funding: #DigitAg – Région Occitanie
  • #DigitAg : Cofunded thesis – Axe 6 : Modélisation et simulation (systèmes de production agricole), Axe 4 : Système d’information, stockage et transfert de données, Axe 5 : Fouille de données, analyse de données, extraction de connaissances, Challenge 1 : Le challenge agroécologique, Challenge 2 : Le phénotypage rapide, Challenge 3 : La protection des cultures, Challenge 5 : Les services de conseil agricole, Challenge 8 : Développement agricole au Sud

Keywords: Modeling, Semantic, Crop model, Metalanguage, Modularity

Abstract: The use of crop models to predict the performance and environmental impact of crops is widespread at all levels of the value chain. Their use to reduce the use of inputs, to adapt agriculture to climate change, to diversify agro-systems, to preserve biodiversity, and thus to meet the objectives of the Green Deal, leads to constantly review their formalisms and to model new processes.
We have recently developed the Crop Modelling Meta Language (Crop2ML) model representation and transformation system, which allows the development of model components in accordance with FAIR principles. A current limitation of Crop2ML is the lack of semantics to search for components and facilitate their composition in operational modeling solutions.
The SemCrop project aims to address this limitation. The operational objectives concern the interoperability of the modeling tools and the links with the information systems collecting very large data sets. A modularity of the models at the process level is aimed at allowing a better integration at different scales, facilitating the link with the data, and the feedback between data and models (digital twins).
By proposing a modular modeling system, SemCrop will bring an original contribution to address the challenges of ecological, climate, and digital transitions. It will provide #DigitAg and regional AgTech companies (ITK, SMAG, FruitionSciences,...) with innovative tools to develop digital solutions for agriculture. SemCrop will increase the international scope of #DigitAg's research via its insertion in the AMEI (Agriculture Model Exchange Initiative) initiative coordinated by the supervisors.

Contact : teiki.raihauti [AT] inrae.fr
Réseau social: LinkedIn

Modification date: 04 April 2024 | Publication date: 22 January 2024 | By: GL