Towards a better understanding of the root system diversity and plasticityin young grafted vines using 2D imaging and 3D modelling tools

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.

The growing demand for non-alcoholic beverages, driven by health-conscious consumers and shifting social norms, has positioned dealcoholized wines as a promising alternative in the global beverage industry (Akhtar et al., 2025, in press; Kakroo, 2024).

Compactness is a complex trait of V. vinifera L. and is defined ultimately by the portion of free space within the bunch which is not occupied by the berries. A high degree of compactness results in poor ventilation and consequently a higher susceptibility to fungal diseases, diminishing the quality of the fruit. The easiness to conceptualize the trait and its importance arguably contrasts with the difficulty to measure and quantify it. However, recent technical advancements have allowed to study this attribute more accurately over the last decade. Our main objective was to explore the underlying genetics determining bunch compactness by applying updated phenotyping methods in a collection of V. vinifera L. cultivars with a wide genetic diversity.

Astringency is a crucial sensory attribute typically described as the drying and/or puckering sensation occurring after the consumption of tannin-rich foods and beverages. In this study, thirty-seven red wines from different varieties, origins and styles were evaluated, analyzing both chemical and sensory features. Principal Component Analysis was used for dimensionality-reduction and for correlating selected chemical parameters against astringency. The results showed that tannin content was the most important chemical parameter influencing overall astringency but more clearly the dryness sub-quality, followed by pH, titratable acidity and alcohol content.

The yeast residue left over after wine-making, known as wine yeast lees, is a source of various compounds that are of interest for wine and food industry. In winemaking, yeast-derived glycocompounds and proteins represent an example of circular economy approach since they have been proven to reduce the need for bentonite and animal-based fining agents. This leads to a reduced environmental impact in the stabilization and fining processes in winemaking. (de Iseppi et al., 2020, 2021).