The Tannat grape, native to the foothills of the Pyrenees in France, is known for producing wines with intense colour, exceptional tannic structure, and remarkable aging potential. These distinctive characteristics are attributed to its unique genome, making Tannat one of the grape varieties with the highest tannins concentration.

The objective of this study was to develop a methodology capable of modeling the effect of viticultural climate on wine sensory characteristics.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

The success of grafting in viticulture is deeply influenced by the nutrient composition of both rootstock and scion
materials. Key components such as nitrogen and carbohydrates play a crucial role in graft compatibility, establishment,
and overall plant vigor [1].

Bud death due to cold damage is a recurrent and major economic issue with Vitis vinifera L. in the Northeastern U.S. winegrowing regions. Primary buds – and sometimes secondary and tertiary buds – are often damaged by fluctuating temperatures in the winter and early spring. To maintain balanced vegetative and reproductive growth of a vine, pruning practices need to be adjusted to account for bud damage. Conventional bud damage assessment requires growers to sample canes/spurs, cut nodes with a razor blade, and then visually assess bud damage. This process is laborious and becomes a major barrier for damage-compensated pruning decision-making, leading to too few live buds per vine and the associated excessive vigor and low yield that result. The overarching goal of this study was to develop an active thermographic system for non-destructive detection of bud damage in the vineyard.