The role of œnology in the enhancement of terroir expression

Wine quality depends on grape biochemical constituents, including sugars, organic acids, amino acids, and bound and free aroma compounds, which are influenced by vineyard location and environmental factors such as temperature and precipitation [1].

Vine diseases have a strong impact on vineyards sustainability, which in turns leads to strong economic consequences. Among those diseases, Flavescence dorée spreads quickly and is incurable, which led in France to the setup of a mandatory pest control implying the systematic use of pesticides and the prospection and uprooting of every infected plants. Remote sensing could be a very powerful tool to optimize prospection as it allows to produce quickly accurate maps over large areas. Recent studies have shown that high spatial resolution (10cm/pixel) multispectral images acquired from UAVs allow to map Flavescence dorée in vineyards using leaves discolorations [e.g. Albetis et al., Remote Sensing, 2017].

Pruning is one essential vineyard management activity whose main purpose is to regulate plant growth and vigour, modulating berry size, and consequently, wine quality. In Chilecito, La Rioja Province, Argentina, Torrontés Riojano stands as the only autochthonous variety for winemaking, yielding golden and aromatic berries and distinctive muscatel-tasting wines. This white cultivar, resulting from the natural cross between Moscatel de Alejandría x Criolla Chica, is traditionally trained in “parral” (horizontal trellis system), aimed to manage vigorous canopies. This project constitutes the first study on the influence of pruning intensity on Torrontés Riojano growth habit and berry quality.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages.