Hyperspectral imaging and machine learning for monitoring grapevine physiology

Phenolic compounds are among the most important quality factors of wines. They contribute to the organoleptic characteristics of wine such as colour, astringency, and bitterness. Although tannins found in wine can come from microbial and oak sources, the main sources of polyphenols are skin and seed from grapes. Yet, the link between grape seed phenolic content and wine composition, or even the link between seed maturity stage and wine composition are poorly studied. This work describes and explains the seed tannins kinetics release in wine, but also the impact of seed maturity stage on seed tannins extractability.

The physical factors that influence the grape ripening include elevation, slope, aspect, potential global radiation, sun hours and soil type of the vineyards.

Downy mildew (Plasmopara viticola) is a fungal pathogen that causes a destructive disease in grapevines (Vitis vinifera).

AIM. Wine yeast lees constitute a winemaking by-product that, unlike grape skins and seeds, are not sufficiently exploited to add value to the winemaking sector, as their treatment and disposal generally represents a cost for wineries [1].

the phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data. Our approach applies to vast areas because it solely relies on open and satellite data having global coverage without requiring any in-field data from weather stations or other sensors making the approach extensible to other areas.