Atypical ageing defect in Pinot Blanc wines: influence of the grapevine production management.

Un projet multidisciplinaire sur l’effet du sol et du climat sur la qualité du vin a débuté en Afrique du Sud il y a 5 ans. Des mesures sont effectuées sous culture sèche dans des vignes de Sauvignon Blanc dans six localités différentes, cinq dans le district de Stellenbosch et une à Durbanville.

Grapevine downy mildew is a disease of foliage caused by Oomycete Plasmopara viticola an endoparasite that develops inside grapevine organs and can infect virtually every green organ. Downy mildew is one of the most destructive diseases in wine-growing regions, drastically reducing yield and fruit quality. Traditional manual disease detection relies on farm experts. Human field scouting has been widely used for monitoring the disease progress, however, is costly, laborious, subjective, and often imprecise.

The use of next-generation sequencing (NGS) has helped understand microbial genetics in oenology. Current studies mainly focus on barcoded amplicon NGS but not shotgun sequencing, which is useful for functional analyses. Since the high percentage of grapevine DNA conceals the microbial DNA in must, the majority of sequencing data is wasted in bioinformatic analyses. Here we present capture depletion of grapevine whole genome DNA.

Color is a key quality trait for grape berry and the producing wines. Berry color of red genotypes is mainly determined by the quantity and composition of anthocyanins accumulated in the skin and/or pulp. Both genetic and environmental factors could influence the quantity and composition of anthocyanins, while the underlying mechanisms are not fully clear. To explore the mechanisms underlying the diversity of anthocyanin accumulation in grape berry, we compared two grapevine genotypes showing distinct sensitivities to ABA-induced anthocyanin biosynthesis, where one genotype showed minor responses to exogenous ABA application while the other showed significant increase in anthocyanins after exogenous ABA application.

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions.