DESCRIPTION:

Currently, vineyard monitoring is not cost-effective in terms of effort and resources. Traditional methods rely on manual observation and periodic field inspections, which can be time-consuming and limited in scope. Moreover, detecting diseases, assessing vine growth stages, and estimating yield require extensive data collection and analysis. 

The solution proposed by the research collaboration between the Fundació URV (FURV) and the Computer Vision Centre (CVC) leverages advanced technologies to automate vineyard monitoring. The approach combines real-time environmental data capture with high-resolution imaging to track the vineyard’s evolution over time. The FURV team focuses on continuous temporal monitoring, while the CVC team provides broader spatial coverage at key moments in the vine’s growth cycle, such as pruning, flowering, and fruiting. 

The result is an intelligent data-driven system that enables real-time analysis for early detection of potential issues, helping farmers optimize vineyard management. By integrating this data with 3D reconstructions and AI-based models, the platform can count and detect key elements like inflorescences and fruits, ultimately providing accurate yield estimations for each vineyard through the VinQ platform (https://vin-q.com/). VinQ aggregates vast amounts of field data, including historical management records, climate information, and sensor data, while offering built-in predictive models for disease risk assessment.