Implementation of integrated and innovative Precision Agriculture management strategies to reduce the occurrence of ochratoxins along the vine value chain products: grapes, raisins/currants and wine

OchraVine Control is a Horizon 2020 project aiming at the effective control of ochratoxins in grape products (grapes, raisins/currants and wine) using precision agriculture methods. Ochratoxins are carcinogenic mycotoxins produced by species of various Aspergillus and Penicillium genera.

The project will work on the development of a smart model for the integrated management and will allow prediction and monitoring at pre- and post-harvest level to control Aspergillus infection and OTA contamination in vine cultivation by combining epidemiological data, biological and chemical management strategies, post-harvest technologies and precision agriculture tools.

The project will work towards strengthening the links between research and innovation with entrepreneurship and increased competitiveness, productivity and outreach of the participating companies to international markets. The project will contribute to the facilitation of technology transfer and the cooperation between universities and enterprises. It will also contribute to the direct and effective channeling of available resources to the promotion of research activities and the implementation of innovation in SMEs.

NEUROPUBLIC has significant experience in the development of DSSs, as well as in IoT stations for collecting atmospheric and soil measurements from the field to support precision agriculture services. NP will work on pre-harvest methods that reduce OTA production and also on DSS design and implementation.

In addition, in the context of the project, NEUROPUBLIC will receive secondees from 2 universities to exchange knowledge and work together in the analysis and modelling of environmental data coming from field sensors at canopy level (NP’s GAIAtron stations) and in applying PA techniques on early insect prediction and risk estimation.

You can find more information about the project at

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 778219