Oospore germination dynamics and disease forecasting model for a precision management of downy mildew 

During red wine vinification, maceration allows the must, and consequently the wine, to be enriched with several compounds that contribute to the creation of the typical organoleptic characteristics of red wines. Among these, extraction of polyphenols (PPs) during maceration is a major process of enological interest.
The purpose of this study was the evaluate the suitability of a rapid analytical approach based in linear sweep voltammetry to monitor PPs extraction during vinification.

This study examines, from the perspective of the employees at Bosque de Matasnos—a demonstrative winery participating in the LIFE Climawin project—the socioeconomic impact and potential contributions of the initiative to the wine sector and the sustainable development of the Ribera del Duero region in Spain.

Grape downy mildew, caused by plasmopara viticola, is one of the main diseases affecting viticulture worldwide and its control usually relies on frequent sprays with agrochemicals. Grapevine varieties resistant to p. Viticola represent an effective solution to control downy mildew and reduce the environmental impact of viticulture. Loci of resistance to p. Viticola (Rpv) have been introgressed from wild vitis species and some of them, like Rpv1, Rpv3.1 and Rpv10, are currently the most utilized genetic resources in grape breeding.

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.

Precision viticulture aims to optimize vineyard management by monitoring and responding to variability within vine plots. this work presents a comprehensive study on the application of hyperspectral imaging (hsi) technology for monitoring purposes in precision viticulture. authors explore the deployment of hsi sensors on various platforms including laboratory settings, terrestrial vehicles, and unmanned aerial vehicles, facilitating the collection of high-resolution data across extensive vineyard areas.