Research*eu Results Magazine June 2017 [antikvár]

N T E R V I E w AN AUTONOMOUS ROBOT TO PREDICT FUTURE YIELDS A technology able to accurately predict future yields, without human intervention, would be a dreann conne true for vine growers across Europe. If all goes as planned, such technology should be available to them in less than two years. n a market that's Increasingly driven by quality, VINBOT (Autonomous cloud-computing vineyard robot to optimise yield management and wine quality) can provide European wine producers with a competitive edge. As better forecasts also mean better decision-making, the autonomous robot and its network of sensors can estimate grape yield and relevant canopy features, generate maps, and share such infonnation via the cloud. André Barriguinha, CEO of Agri-Ciencia and post-project dissemination/commercialisation leader of VINBOT, discusses the project's outcomes and plans for future work. ? What are the specific challenges that VINBOT aimed to respond to? André Barriguinha: VINBOT can run autonomously in the vineyard with no human intervention. It is equipped with a sensor system to allow navigation, localisation and data acquisition. It uses a single camera that collects shots from the canopy. Once it's done, it uses algorithms to identify the grapes and bunches and estimate future yields. This is something that's currently not available on the market There Is no existing yield estimation device, so vine growers must resort to manual processes which are very time consuming and not very accurate. Thanks to VINBOT, vine growers will have a new tool to make these estimates as early as possible. * How come there was no such solution before VINBOT? Because it's very difficult to have a fully-autonomous robot that can both navigate inside the vineyard and estimate the yield. VINBOT is bringing just that autonomous navigation thanks to an on-board GPS receiver and 20 rangefinder; an HMl to define a set of waypoints and the characteristics of the acquisition mission; vine measurement components; a cloud-based software that processes the robot's sensor data to extract relevant information and produce yield maps; and a web application for the end user to access these maps. One of the major problems we faced was trying to identify the bunches in the vineyard, mainly when they are not visible to the camera (hidden by vegetation and/or other clusters). , This is a difficulty that we are still trying to overcome by the use of models based on the 3D canopy reconstruction, obtained by the Range Finder Our results showed that can-" opy features and yield can be estimated by the VINBOT platform with an acceptable accuracy. However, the underestimation of actual yield, caused mainly by cluster occlusion, deserves further research to improve the