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 accuracyN/
