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Collaborative project: Artificial intelligence for an innovative yield forecast in grapevine - subproject A (KI-iREPro)
Project
Project code: 28DK128A20
Contract period: 01.04.2021
- 31.10.2024
Budget: 496,938 Euro
Purpose of research: Applied research
Keywords: digital world, knowledge transfer, vine, AI Artificial Intelligence, viticulture (winery), forecast, sustainability
In viticulture quantity and quality of yield significantly affects the product quality and therefore the profitability. For vine grower yield forecasts are an important ecological factor, which can be highly influenced through weather conditions showing strong year-to-year fluctuations. Up to now yield forecasts are very errorprone leading to yearly surprises, positive or negative, in the companies. Simultaneous accurate yield forecasts offer an important base for operation-al management: (1) adjusting the desired yield level (quantity-quality- relation; quality man-agement); (2) transport logistics (transport capacity from the vineyard to the cellar. reduction of waiting time at grape delivery) and (3) cellar logistic (capacity of grape press and fermenta-tion tanks). Trans-technologically and application-oriented a machine learning method (section of Artificial Intelligence (AI)) will be used to develop a yield forecast model.
Section overview
Subjects
- Crop Production
- Viticulture
Framework programme
Funding programme
Excutive institution
Federal Research Centre for Cultivated Plants – Julius Kühn-Institut (JKI)
Funding institution
Project management agencies
Associated projects: KI-iREPro
- Collaborative project: Artificial intelligence for an innovative yield forecast in grapevine - subproject B
- Collaborative project: Artificial intelligence for an innovative yield forecast in grapevine - subproject C
- Collaborative project: Artificial intelligence for an innovative yield forecast in grapevine - subproject D
- Collaborative project: Artificial intelligence for an innovative yield forecast in grapevine - subproject E
