The major challenge in a vineyard is disease detection of the grapevine
The detection of the malformations in the grape usually is done visually. Unless these diseases are detected in time, they can produce massive damage to up to 70-80 per cent of the entire wine production in a year. Thus, early detection of the malformations in the plant is crucial for the entire yearly wine production. Get in touch with us
our Solutions
We provide wineyard automation
Videos
Vineyard automation in action
Canopy segmentation
Leaf detection in wine yards
Abstract
Some early-stage research works are focusing on aerial observation of the vineyard, however, they lack the close proximity observation which is usually available from ground-level robots. This project proposal aims to demonstrate the potential of using proximity aerial sensing with commercial UAVs.
Adopting our custom UAV image based detection methods, this project would demonstrate the real benefits of the paradigm shift towards the use of autonomous robots in the era of Agriculture 4.0. We target TRL6 to TRL7 level for this project.
The impact of the GrapeGuard project is multifold: environmental, social , economic (reduces losses) and scientific (omnidirectional heterogeneous image fusion and detection). This will be ensured by the interdisciplinary project team formed by experts in the machine learning and robotics fields together with winemakers.
We build wineyard automation tools with the help of advanced AI
One of the major challenges in the vineyards is related to vine diseases detection (VDD) including - which affect the grapevine in different phases of its maturity. The detection of the malformations in the vineyards is done by visual inspection. Unless these diseases are detected in time, they can produce massive damages of up to 70-80 percent of the entire wine production in a year. Thus, the early-stage detection of the malformations in the plant is crucial for the entire yearly wine production.Some early-stage research works are focusing on aerial observation of the vineyard, however, they lack the close proximity observation which is usually available from ground-level robots. This project proposal aims to demonstrate the potential of using proximity aerial sensing with commercial UAVs.
Adopting our custom UAV image based detection methods, this project would demonstrate the real benefits of the paradigm shift towards the use of autonomous robots in the era of Agriculture 4.0. We target TRL6 to TRL7 level for this project.
The impact of the GrapeGuard project is multifold: environmental, social , economic (reduces losses) and scientific (omnidirectional heterogeneous image fusion and detection). This will be ensured by the interdisciplinary project team formed by experts in the machine learning and robotics fields together with winemakers.
Our Objectives
Project Objectives.
01
AI based vineyard disease detection
AI based detection of the vineyard detection from proximity sensing with UAV.
02
Disease mapping
Making a desktop and mobile visualization of the disease maps recorded during the flights.
03
Validation
Validation of the proposed setup in real life scenarios at client side.
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