A Decision Support System Coupling Fuzzy Logic and Probabilistic Graphical Approaches for the Agri-Food Industry: Prediction of Grape Berry Maturity

Abstract

Agri-food is one of the most important sectors of the industry and a major contributor to the global warming potential in Europe. Sustainability issues pose a huge challenge for this sector. In this context, a big issue is to be able to predict the multiscale dynamics of those systems using computing science. A robust predictive mathematical tool is implemented for this sector and applied to the wine industry being easily able to be generalized to other applications. Grape berry maturation relies on complex and coupled physicochemical and biochemical reactions which are climate dependent. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert predictions. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a decision support system so called FGRAPEDBN able to (1) capitalize the heterogeneous fragmented knowledge available including data and expertise and (2) predict the sugar (resp. the acidity) concentrations with a relevant RMSE of 7 g/l (resp. 0.44 g/l and 0.11 g/kg). FGRAPEDBN is based on a coupling between a probabilistic graphical approach and a fuzzy expert system.

Metagenomic Approaches to Investigate the Contribution of the Vineyard Environment to the Quality of Wine Fermentation: Potentials and Difficulties

“…In spring vine fertilized flowers start to develop a seed and a grape berry to protect it. Grape growth and maturation occur in the following months, with a duration changing according to the climate (Perrot et al, 2015 ). While growing and ripening, grapes are exposed to microbes originating from the surrounding environment, and the microbial communities on grape skins are subjected to dynamic changes due to environmental factors and anthropogenic interventions.…”

Hyperspectral signatures and reflectance models related to the ripening index in four grape varieties

“…According to Perrot et al (2015) established that the optimum measure of sugar content for the grape harvest is between 21 to 23 °Bx, from our results obtained, only the Merlot variety was at its optimum harvest point.…”

Artificial Intelligence and Big Data Analytics in Vineyards: A Review

“…Berry maturity is measured as sugar concentration that increases rapidly, and acidity concentration, that decreases along with pH levels as berry mature. This ES attains high predictive accuracy (i.e., a root-mean-squarederror (RMSE) of 7 g/l (i.e., 0.44 g/l or 0.11 g/kg) [20]. The coupling of ES to AI (i.e., ML and DL models/algorithms) in viticulture, or agriculture in general, is still unexplored and in its infancy.…”