  Robotic harvesting of fruits in orchards is a challenging task, since high
density and overlapping of fruits and branches can heavily impact the success
rate of robotic harvesting. Therefore, the vision system is demanded to provide
comprehensive information of the working environment to guide the manipulator
and gripping system to successful detach the target fruits. In this study, a
deep learning based one-stage detector DaSNet-V2 is developed to perform the
multi-task vision sensing in the working environment of apple orchards.
DaSNet-V2 combines the detection and instance segmentation of fruits and
semantic segmentation of branch into a single network architecture. Meanwhile,
a light-weight backbone network LW-net is utilised in the DaSNet-V2 model to
improve the computational efficiency of the model. In the experiment, DaSNet-V2
is tested and evaluated on the RGB-D images of the orchard. From the experiment
results, DaSNet-V2 with lightweight backbone achieves 0.844, 0.858, and 0.795
on the F 1 score of the detection, and mean intersection of union on the
instance segmentation of fruits and semantic segmentation of branches,
respectively. To provide a direct-viewing of the working environment in
orchards, the obtained sensing results are illustrated by 3D visualisation .
The robustness and efficiency of the DaSNet-V2 in detection and segmentation
are validated by the experiments in the real-environment of apple orchard.
