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Robotic harvesting of fruits in orchards is a challenging task, since highdensity and overlapping of fruits and branches can heavily impact the successrate of robotic harvesting. Therefore, the vision system is demanded to providecomprehensive information of the working environment to guide the manipulatorand gripping system to successful detach the target fruits. In this study, adeep learning based one-stage detector DaSNet-V2 is developed to perform themulti-task vision sensing in the working environment of apple orchards.DaSNet-V2 combines the detection and instance segmentation of fruits andsemantic segmentation of branch into a single network architecture. Meanwhile,a light-weight backbone network LW-net is utilised in the DaSNet-V2 model toimprove the computational efficiency of the model. In the experiment, DaSNet-V2is tested and evaluated on the RGB-D images of the orchard. From the experimentresults, DaSNet-V2 with lightweight backbone achieves 0.844, 0.858, and 0.795on the F 1 score of the detection, and mean intersection of union on theinstance segmentation of fruits and semantic segmentation of branches,respectively. To provide a direct-viewing of the working environment inorchards, the obtained sensing results are illustrated by 3D visualisation .The robustness and efficiency of the DaSNet-V2 in detection and segmentationare validated by the experiments in the real-environment of apple orchard.

Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards