Wine sector
Protection of vines with Grapevine AI Assistant
Monitoring the health status of the vine and the intelligent recognition of Flavescenza dorata
Preserve the health of vineyards to reduce waste
Monitoring the health of the vines is a complex and often ineffective process:
Diseases are detected late, causing irreparable damage to crops;
Traditional methods rely on laboratory analysis and manual sampling, with high costs and long times;
The lack of updated and centralized data hinders the monitoring of disease evolution and the adoption of targeted strategies.
Partner
The Grapevine AI Assistant project was born out of collaboration with Agrea and the National Research Council. The synergy between our technological expertise and the advanced research of CNR and Agrea has allowed us to offer innovative solutions to tackle the emerging challenges of new biological threats.
Announcement
We worked on the I-NEST call: a funding line of Spoke 7 within the PNRR and NextGenerationEU programs, focusing on innovation and digital transformation, especially in rural areas and the agritech sector.
Technologies
Machine Learning, blockchain, and Cloud Web platform
Location
Veneto Region, Italy
AI at the service of viticulture: diagnosis and prevention
We analyzed different types of vineyards to identify the main risk factors for the most widespread diseases, such as Flavescenza dorata, studying their life cycle, modes of propagation over time, and effects on productivity.
Solution
The tracking system
To monitor the health of the vines, we use a cloud-based platform where artificial intelligence, starting from the analysis of sample leaves, recognizes the phytoplasma of Flavescenza dorata and its vector, the Scaphoideus titanus.
The training of the AI occurs through a 2D and 3D reconstruction of the vineyard, obtained from drones that capture spectral and color images. These data are processed to identify the first signs of disease and to monitor the vineyard over time.
Data acquisition
I drones and ground sensors capture sample images of the grapevine leaves following routes in the vineyard
AI analysis of diseases
The AI detection of the Golden Flavescence using a trained machine learning model
3D Reconstruction
The vineyard is reproduced in a three-dimensional model to visualize its health status and compare it over time
Crop traceability
The system monitors the state of the crops by identifying critical points to prevent losses and waste
Interactive monitoring
The platform allows farmers to view in real time the status of the vines' health and the results of the AI
Cloud Storage
The data is stored on a platform to ensure traceability and security
Results
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