Key odorants of french syrah wines from the northern rhone valley

Wine quality and safety are the main concerns of consumers and health agencies. Biogenic amines and polyamines, depending on their concentration and on individuals, in wine can constitute a potential public health concern due to their physiological and toxicological effects

In vineyard management, the block is considered today as the technical work unit. However, considerable variability can exist inside a block with regard to physiological parameters, such as vigour, particularly because of soil heterogeneity. To represent this variability spatially, many measurements have to be taken, which is costly in both time and money. High resolution remote sensing appears to be an efficient tool for mapping intra-block heterogeneity.

New pathogen resistant varieties allow an efficient and greatly reduced use of fungicides. These new varieties promise, therefore, an enormous potential to reach the European Green Deal aim of a 50% reduction of pesticides in EU agriculture by 2030.

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 Valpolicella hilly area, north of Verona, is highly vocated for viticulture but its vineyards are sometimes characterized by very different soil and microclimate conditions which can greatly affect their oenological potential.