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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 aims to improve the prevention and control of vine diseases, such as mildew, with the objective of reducing the amount of fungicide used in the prevention of these diseases, as well as the number of preventive treatments carried out by wine producers, thus 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 to control downy mildew, as well as other vine diseases, allows:
Different data sets are used, from multispectral images from Copernicus-Sentinel-2, to observations of the evolution of vineyard phenology and the presence of diseases and pests from Red FARA (Red de Vigilancia Fitosanitaria de Aragón), supported by CSCV, and completed with data provided by UNIZAR; The project also includes the meteorological stations managed by SARGA of the SIAR network and the 14 stations deployed during the project, as well as those of AEMET close to or located in the appellations of origin. Also, weather forecasts based on Agroapps models.
ITA has integrated data from different sources and has developed predictive models of vine phenology and the diseases and pests that can affect it using Machine Learning algorithms. For the training of the models ITA is using its own resources, as well as those provided by CESGA.
