Using an unmanned ground vehicle to scout vineyards for non-intrusive estimation of canopy features and grape yield - Lopes, C.M. (2017)
This paper aims to present some results of the EU VINBOT (Autonomous cloud-computing vineyard robot to optimize yield management and wine quality) project focused on vineyard yield estimation. A ground truth evaluation trial was set up in an experimental vineyard with two plots of the white varieties ‘Alvarinho’ and ‘Arinto’, trained on a vertical shoot positioning system and spur pruned. For each varietal plot, six smart points were selected with 10 contiguous vines each. During the ripening period of the 2016 season the vines were manually assessed for canopy dimensions and yield and then scanned by the VINBOT sensor head composed with a 2D laser rangefinder, a Kinect v2 camera and a set of robot navigation sensors. Ground truth data was used to compare with the canopy data estimated by the rangefinder and with the output of the image analysis algorithms. Regarding canopy features (height, volume and exposed leaf area), in general an acceptable fit between actual and estimated values was observed with canopy height showing the best agreement. The regression analysis between actual and estimated values of canopy features showed a significant linear relationship for all the features however the lower values of the R2 indicate a weak relationship. Regarding the yield, despite the significant R2 (0.31) showed by the regression analysis between actual and estimated values, the equation of the fitted line indicate that the VINBOT algorithms underestimated the yield by an additive factor. Our results showed that canopy features can be estimated by the VINBOT platform with an acceptable accuracy. However, the underestimation of actual yield, caused mainly by bunch occlusion, deserves further research to improve the algorithms accuracy