Combination of NIR multispectral information acquired from a ground moving vehicle with AI methods to assess the vine water status in a Tempranillo (Vitis vinifera L.) commercial vineyard

A wide range of olfactory descriptors ranging from fresh and jammy fruit notes to cooked and oxidized fruit notes could describe the fruity aroma of red wines [1]. The fruity character of a wine is mainly related to the grape variety selected, to the terroir and the vinification process applied for its conception. In white wines, some volatile compounds confer directly their aroma to the wine while the question of “key” compound is more complex in red wines. According to many studies performed over the past decades, some fruity ethyl esters are directly involved in the fruity perception of red wines while others, present at subthreshold concentrations, participate indirectly to the fruity expression via perceptive interactions [2].

The aim of this work was to study the trapping capacity of four polyaniline polymers towards phenolic compounds in wine-like model solutions. METHODS: The model wine solution was composed of 12% (v/v) and 4 g/L of tartaric acid adjusted to pH = 3.6. A series of centrifuge tubes (15 mL) were filled with 10 mL of model solution enriched with 50 mg/L of five phenolic compounds (i.e., Gallic acid, caffeic acid, (+)-catechin, (-)-epicatechin, and rutin), and treated with different doses of PANI polymer (i.e., 0, 2, 4 and 8 g/L). After the addition of the polymer, the samples were stirred using a platform shaker at room temperature (20 ºC) for 2, 8, 16 and 24 h. All treatments included three replications.

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.

Global climate change affects regional climates and hold implications for wine growing regions worldwide. Although winegrowers are constantly adapting to internal and external factors, it seems relevant to develop tools, which will allow them to better define actual and future agro-climatic potentials. Within this context, we develop a modelling approach, able to simulate the impact of environmental conditions and constraints on vine behaviour and to highlight potential adaptation strategies according to different climate change scenarios. Our modeling approach, named SEVE (Simulating Environmental impacts on Viticultural Ecosystems), provides a generic modeling framework for simulating grapevine growth and berry ripening under different conditions and constraints (slope, aspect, soil type, climate variability…) as well as production strategies and adaptation rules according to climate change scenarios. Each activity is represented by an autonomous agent able to react and adapt its reaction to the variability of environmental constraints. Using this model, we have recently analyzed the evolution of vineyards’ exposure to climatic risks (frost, pathogen risk, heat wave) and the adaptation strategies potentially implemented by the winegrowers. This approach, implemented for two climate change scenarios, has been initiated in France on traditional (Loire Valley) and emerging (Brittany) vineyards. The objective is to identify the time horizons of adaptations and new opportunities in these two regions. Carried out in collaboration with wine growers, this approach aims to better understand the variability of climate change impacts at local scale in the medium and long term.

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.