  The berry size is one of the most important fruit traits in grapevine
breeding. Non-invasive, image-based phenotyping promises a fast and precise
method for the monitoring of the grapevine berry size. In the present study an
automated image analyzing framework was developed in order to estimate the size
of grapevine berries from images in a high-throughput manner. The framework
includes (i) the detection of circular structures which are potentially berries
and (ii) the classification of these into the class 'berry' or 'non-berry' by
utilizing a conditional random field. The approach used the concept of a
one-class classification, since only the target class 'berry' is of interest
and needs to be modeled. Moreover, the classification was carried out by using
an automated active learning approach, i.e no user interaction is required
during the classification process and in addition, the process adapts
automatically to changing image conditions, e.g. illumination or berry color.
The framework was tested on three datasets consisting in total of 139 images.
The images were taken in an experimental vineyard at different stages of
grapevine growth according to the BBCH scale. The mean berry size of a plant
estimated by the framework correlates with the manually measured berry size by
$0.88$.
