  Yield and its prediction is one of the most important tasks in grapevine
breeding purposes and vineyard management. Commonly, this trait is estimated
manually right before harvest by extrapolation, which mostly is
labor-intensive, destructive and inaccurate. In the present study an automated
image-based workflow was developed quantifying inflorescences and single
flowers in unprepared field images of grapevines, i.e. no artificial background
or light was applied. It is a novel approach for non-invasive, inexpensive and
objective phenotyping with high-throughput.
  First, image regions depicting inflorescences were identified and localized.
This was done by segmenting the images into the classes "inflorescence" and
"non-inflorescence" using a Fully Convolutional Network (FCN). Efficient image
segmentation hereby is the most challenging step regarding the small geometry
and dense distribution of flowers (several hundred flowers per inflorescence),
similar color of all plant organs in the fore- and background as well as the
circumstance that only approximately 5% of an image show inflorescences. The
trained FCN achieved a mean Intersection Over Union (IOU) of 87.6% on the test
data set. Finally, individual flowers were extracted from the
"inflorescence"-areas using Circular Hough Transform. The flower extraction
achieved a recall of 80.3% and a precision of 70.7% using the segmentation
derived by the trained FCN model.
  Summarized, the presented approach is a promising strategy in order to
predict yield potential automatically in the earliest stage of grapevine
development which is applicable for objective monitoring and evaluations of
breeding material, genetic repositories or commercial vineyards.
