GRAPEVINE was a three-year project that aimed to adapt a set of phenological, disease and meteorological models to improve prevention and control of grape diseases in viticulture.
Grapevine: reducing the environmental impact by optimising the use of phytosanitary products and increasing biodiversity
GRAPEVINE (High performance computing services for prevention and control of pests in fruit crops) was a three-year project funded under the grant agreement 863463 that aimed to adapt a set of phenological, disease and meteorological models to the field of viticulture, with the ultimate goal of training a predictive model based on Machine Learning techniques to improve the prevention and control of grape diseases in the wine industry.
GRAPEVINE aimed to improve current pest prediction and control systems in the wine sector, thanks to Big Data and Artificial Intelligence technologies. One of its challenges was the improvement of existing Machine Learning predictive models to offer a more effective response to the treatment of pests and reduce the amount of fungicide and its number of treatments. The developed services were tested to forecast grapevine diseases in Aragon (Spain) and the Protected Denomination of Origin region of Goumenissa (Greece). Implementing these services required the execution of complex computational tasks, mainly the high-resolution calculation of meteorological models for forecasting the evolution of grape diseases, which are resource-intensive tasks. As these resources are quickly consumed, many High-Performance Computing (HPC) resources are needed.
The implementation required a modular and cross-border solution with components that can be distributed and coordinated by an HPC orchestration solution to become an intelligent decision support tool for grapevine farmers adaptable to other regions. A large amount of existing data was used to adapt the models to specific grape varieties, phenological specificities, climatic conditions, and the training of Deep Learning algorithms.
Additionally, the amount of data managed required the computational activities to be close to the data source to execute efficient data transfers and collection of results.
EGI-ACE has been instrumental in the success of the GRAPEVINE project. Their allocation of High-Performance Computing resources came at a critical juncture, enabling us to continue our weather simulations tasks when our initial resources were exhausted. The EGI-ACE's commitment to fostering research and development proved invaluable, offering a lifeline that allowed us to complete our work effectively and efficiently. Without their support, achieving our project objectives would have been a much more daunting, if not impossible, task. EGI-ACE's contribution has been a cornerstone of GRAPEVINE's success.
Rafael del Hoyo Alonso, GRAPEVINE project coordinator