  In (grapevine) breeding programs and research, periodic phenotyping and
multi-year monitoring of different grapevine traits, like growth or yield, is
needed especially in the field. This demand imply objective, precise and
automated methods using sensors and adaptive software. This work presents a
proof-of-concept analyzing RGB images of different growth stages of grapevines
with the aim to detect and quantify promising plant organs which are related to
yield. The input images are segmented by a Fully Convolutional Neural Network
(FCN) into object and background pixels. The objects are plant organs like
young shoots, pedicels, flower buds or grapes, which are principally suitable
for yield estimation. In the ground truth of the training images, each object
is separately annotated as a connected segment of object pixels, which enables
end-to-end learning of the object features. Based on the CNN-based
segmentation, the number of objects is determined by detecting and counting
connected components of object pixels using region labeling. In an evaluation
on six different data sets, the system achieves an IoU of up to 87.3% for the
segmentation and an F1 score of up to 88.6% for the object detection.
