Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards
This work addresses the problem of improving robotic harvesting efficiency for apple farmers by providing a multi-task vision system, though it is incremental with a lightweight backbone.
The study tackled the challenge of robotic harvesting in apple orchards by developing DaSNet-V2, a deep learning model for fruit detection, instance segmentation, and branch segmentation, achieving F1 scores of 0.844 and 0.858 for detection and instance segmentation, and a mean IoU of 0.795 for branch segmentation.
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.