Exploring zoxamide sensitivity in Plasmopara viticola populations: implications for fungicide management in precision agriculture

Oenococcus oeni is the predominant lactic acid bacteria species in wine and cider, where it performs the malolactic fermentation (MLF) (Lonvaud-Funel, 1999). The O. oeni strains analyzed to date form four major genetic lineages named phylogroups A, B, C and D (Lorentzen et al., 2019). Most of the strains isolated from wine, cider, or kombucha belong to phylogroups A, B+C, and D, respectively, although B and C strains were also detected in wine (Campbell-Sills et al., 2015; Coton et al., 2017; Lorentzen et al., 2019;

The aim of this study was to determine how artificial shading influenced berry development and anthocyanin accumulation in ‘Cabernet sauvignon’. Opaque polypropylene boxes were applied to grape bunches over three different developmental stages.

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.

Grapevine downy mildew (GDM), caused by the obligate biotroph oomycete Plasmopara viticola, is one of the most destructive diseases in viticulture.

The zoning of grapevine in a hilly area should consider the variability of the environmental characteristics due to topography. Since soil and climate data are usually available as point data