VineproData uses several Datasets related to vineyards, such as grape production and weather data, to develop a new module called Vineyard Finance Intelligence (VFI) using advanced data processing methods and machine learning.
The new module VFI will enable grape growers to calculate several performance indicators, allowing them to act on time by adjusting vineyard management. Moreover, the VFI module will allow grape growers to compare quality KPIs of different seasons and their performance compared to the rest of the industry.
To build and market the final product within the VineproData experiment, we will lean on 4PDIH for the initial design of the solution, EPCC for model implementation and deployment, and on Know-Center to develop a business strategy and map out new opportunities.
Build a Vineyard Finance Intelligence module to provide vineyard KPIs by using several Datasets.
Within the VineproData experiment, two innovations based on the data processing within the Experiment will be developed:
1) a data processing pipeline to generate vineyard’s financial key performance indicators
2) Vineyard Finance Intelligence module, a software module in eVineyard vineyard management software that will allow generation of various vineyard KPIs
VineproData experiment will contribute to the reduced economic impact and adjustment of customer operations. By enabling customers to react on time and adjust the quantity and/or quality of the harvested crop, grape growers will be able to reduce vineyard input costs and/or adapt their vineyard management activities.
The following KERs will be generated during the experiment execution:
1) Development of the Vineyard Finance Intelligence module
2) Enable data processing concepts and technology to be applicable to other crops
3) A video guide and dissemination activities to promote and sales the new module
- Number of real datasets which are incorporated in the newly developed VFI module.
- Number of new “Vineyard KPIs” which Vineyard Intelligence Finance module interface can present to the user.
- Number of discovered and defined (or machine-learned) relationships between the datapoints in real used datasets, required for the production of the “Vineyard KPIs”.