Girault-Bogue Gnanguenon-Guesse is one of the #DigitAg co-funded PhDs
Modelling and viewing relations between agrienvironmental time courses and product quality using a parsimonious Bayesian approach
- Start Date: December 2017
- University: MUSE Montpellier University of Excellence / Montpellier SupAgro
- PhD School: ED 166 I2S – Information, Structures, Systèmes
- Field(s): Biostatistics
- Doctoral Thesis Advisor: Nadine Hilgert (INRA Mistea)
- Co-supervisors : Bénédicte Fontez (Montpellier SupAgro MISTEA) et Thierry Simonneau (INRA LEPSE)
- Funding: #DigitAg – INRA
- #DigitAg: Confunded PhD – Axis 5 & 6 – Challenges 1, 5 & 7
Keywords: Factor models, Latent factor models, parsimonious approach, Bayesian inference, Application in Agronomy
Traditional knowledge plays an important role in agricultural practices. For instance, in the vine and wine food chain, decisions taken in vineyards mainly rely on expert knowledge-based approaches.
Confronted with new challenges, stakeholders in agricultural production chains need advanced quantitative-based decision support tools. The aims of this PhD are i) to propose a knowledge discovery method to deal with big data from time courses, ii) to explain and predict product quality. Data integration should deal with high resolution data from sensors or agronomic models, low resolution observations and expert knowledge. It requires taking into account the reliability of all sources and data uncertainties. This calls for a coupling between informatics and data analysis, and constitutes the core of the PhD. The main application concerns the vine and wine food chain, in close relation with industrial partners (consulting professionals: ITK, Fruition Sciences, technical institute IFV) and public research laboratories (Joint Units LEPSE and SPO).
Contact: girault-bogues.gnanguenon-guesse [AT] inra.fr – Tél : +33 (0)499612595