Guyot or pergola for dehydration of Rondinella grape

AIM AND METHODS: Corvina and Corvinone are the two main grape varieties used in the production of Valpolicella, Recioto and Amarone, top-quality red wines in north-eastern Italy. This work aimed at determining the aroma composition of Corvina and Corvinone experimental wines and identify the main aroma compounds contributing to the aroma characteristics of Corvina and Corvinone monovarietal wines. Five Corvina and five Corvinone wines were studied, the grapes coming from five different vineyards in Valpolicella. Volatile compounds were extracted by SPE and identified and quantified by gas chromatography-mass spectrometry (GC-MS), whereas their aroma impact was determined by gas chromatography- olfactometry (GC-O).RESULTS: Based on the GC-MS-O analysis, 95 odor zones were detected, from which 68 compounds were successfully identified. Using the criterion of a value higher than 30% in modified frequency (MF %), 51 compounds were selected and grouped according to odor similarity. Compounds with values below 30% were discarded.

A foresight study was carried out by a group of experts from INRAE, universities, INAO and FranceAgriMer from 2014 as part of the multidisciplinary “laccave” project intended to anticipate climate change in the French wine industry. The initial objective was to initiate an interdisciplinary dialogue between researchers and to feed their questions in a more systemic way. The scenario development method made it possible to build possible futures for the wine sector in the face of climate change. It began by drafting four adaptation strategies, combining different possible intensities of innovation and relocation of the vineyard.

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.

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions. 

Polyphenolic compounds are considered to have a major impact on the quality of red wines. Sensory impact, such as astringency and bitterness, stems directly from tannin composition. Thenceforth, quick analytical measurement of phenolic compounds appears to be a real challenge for winemaking monitoring and process control. Many methods were developed to analyzed polyphenols in wine, but they are time-consuming and require chemistry skills and equipment, not suitable for a rapid routine analysis. A reliable and rapid method to obtain this kind of measurement is Fourier Transform Infrared (FTIR) spectroscopy.