Details of the project on GRAPEVINE vineyards

Grapevine (https://grapevine-project.eu/) is the European initiative that aims to improve current pest prediction and control systems in the wine sector, thanks to Big Data and Artificial Intelligence technologies. The GRAPEVINE project aims to promote the use of Open Data and High Performance Computing (HPC) infrastructures for the development of a predictive model based on machine learning techniques. The project seeks to improve the prevention and control of vine diseases, such as: the Mildiu, with the aim of reducing the amount of fungicide used in the prevention of these diseases, as well as the number of preventive treatments carried out by producers in the wine sector; in this way, introducing the criterion of sustainability in agricultural production and offering consumers higher quality and safer products. In this way the model that has been developed in the project to control different diseases of the vine, allowing: — Use the existing infrastructure of both open data and HPC to address the challenges that arise in the agricultural sector. Provide an open information system that can be used by wine producers to perform more effective treatments against vine diseases. — Provide a reusable service that can potentially be used in any region of the world that faces similar problems. The GRAPEVINE project provides a set of data containing information related to Aragon, and information on vineyards that are made available and open. The data include several meteorological and vegetation indices measured in specific fields, meteorological data. Images captured with hyperspectral cameras, from different vineyard fields. In addition, different disease predictions are found in the data. Data are available regarding the information on vineyards in Aragon provided by different weather stations, which are associated with the PAC code. Each record in the dataset is identified by a unique “id” code, and includes a “code” field representing the PAC code of the field to which the data refers. The “date” field corresponds to the date the data was taken, and there are several other fields that provide information on the minimum and maximum values of NDVI, as well as the mean, standard deviation and median NDVI values of the pixels of the Copernicus image corresponding to the field. The data set also includes information on the image’s “tessels”, the “station” (where the data was collected), the “location” of the station, the “year” and the “day” to which the data belongs, as well as the values of temperature and solar radiation. There are also fields for precipitation and wind speed in different directions. The dataset also provides information on the “potentialDormancyDay” for the vine, the “station” to which the data belongs, the accumulated radiation and precipitation, and various crop indexes such as GDD (Growing Degree Day) and cooling index, which provide information on temperature conditions that are optimal for vine growth.