  Image-based yield detection in agriculture could raiseharvest efficiency and
cultivation performance of farms. Following this goal, this research focuses on
improving instance segmentation of field crops under varying environmental
conditions. Five data sets of cabbage plants were recorded under varying
lighting outdoor conditions. The images were acquired using a commercial mono
camera. Additionally, depth information was generated out of the image stream
with Structure-from-Motion (SfM). A Mask R-CNN was used to detect and segment
the cabbage heads. The influence of depth information and different colour
space representations were analysed. The results showed that depth combined
with colour information leads to a segmentation accuracy increase of 7.1%. By
describing colour information by colour spaces using light and saturation
information combined with depth information, additional segmentation
improvements of 16.5% could be reached. The CIELAB colour space combined with a
depth information layer showed the best results achieving a mean average
precision of 75.
