A probabilistic graphical model for describing the grape berry maturity

• Dynamic Bayesian networks for coupling heterogeneous data and expertise knowledge.
• The modeling of grape berry maturity over the time tainted with uncertainty.
• Prediction of sugar, acidity and anthocyanin concentrations over the maturity.

Grape berry maturation depends on complex and coupled physiological and biochemical reactions which are climate dependant. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert prediction. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a robust mathematical model able (1) to capitalize the heterogeneous fragmented available knowledge including data and expertise by means of probabilistic graphical approaches; and (2) to predict sugar, acidity and anthocyanin concentrations over the maturity.