Characterisation of berry shrivel in Vitis vinifera L. Cultivars in the Stellenbosch wine region

Pectins or pectic polysaccharides are one of the major components in grape skin cell wall, they contribute to physiological processes which determine the integrity and rigidity of grape skin tissue

Vineyards are home to a wide diversity of microorganisms that interact with plants and with each other.

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.

Oak wood has a significant impact on the chemical composition of wine, leading to transformations that influence its organoleptic properties, such as its aroma, structure, astringency, bitterness and color. Among the main extractible non-volatile polyphenol compounds released from oak wood, the ellagitannins are found [1].

The chemistry of wine is notably complex and is modified by ageing of the bottles. The composition of wines is the result of vine production (under the influence of vintage, climate and soils); yeast production (under the influence of juice composition and fermentation management); lactic bacteria production (under the influence of young wine composition and malolactic fermentation management); and of the ageing process either in vats, barrels or bottles or both. The composition is linked to the quality perceived by consumers but also to their origin, sometimes associated to the “terroir” concept.