Sustainable fertilisation of the vineyard in Galicia (Spain)

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. 

In recent years, a pool of terpenoids possibly implicated in minty odours and in the appreciable refreshing sensation, has been identified in long aged red Bordeaux wines (Lisanti et al., 2021, Picard et al., 2016; Picard et al., 2017). These compounds were found to play a key role in the so-called “ageing bouquet”, that can be defined as “the homogeneous, harmonious flavour resulting from the complex transformation process in wine during bottle storage” (Picard et al., 2015). Moreover the minty-fresh sensory dimension in fine aged red wines plays an important role in typicity judgement by wine professionals (Picard et al., 2015).

The ability, and the underlying mechanisms of grapevines to cope with and adapt to recurring water constraints, are the focuses of this study.

Major factors involved in wine quality and typicity are soil type, climatic conditions, plant material (rootstock and cultivar), vineyard management practices and winemaking conditions.

Remote sensing technology captures spectral data beyond the visible range, making it useful for monitoring plant stress. Vis-NIR (Visible-Near Infrared) spectroscopy (400-1000 nm) is commonly used to indirectly assess plant status during drought. One example is the widespread use of normalized difference vegetation index (NDVI) that is strongly linked to green biomass. However, a knowledge gap exists regarding the applicability of this method to all the drought conditions and if it is a direct correlation to the water status of the plant.