  Accurately identifying lychee-picking points in unstructured orchard
environments and obtaining their coordinate locations is critical to the
success of lychee-picking robots. However, traditional two-dimensional (2D)
image-based object detection methods often struggle due to the complex
geometric structures of branches, leaves and fruits, leading to incorrect
determination of lychee picking points. In this study, we propose a
Fcaf3d-lychee network model specifically designed for the accurate localisation
of lychee picking points. Point cloud data of lychee picking points in natural
environments are acquired using Microsoft's Azure Kinect DK time-of-flight
(TOF) camera through multi-view stitching. We augment the Fully Convolutional
Anchor-Free 3D Object Detection (Fcaf3d) model with a
squeeze-and-excitation(SE) module, which exploits human visual attention
mechanisms for improved feature extraction of lychee picking points. The
trained network model is evaluated on a test set of lychee-picking locations
and achieves an impressive F1 score of 88.57%, significantly outperforming
existing models. Subsequent three-dimensional (3D) position detection of
picking points in real lychee orchard environments yields high accuracy, even
under varying degrees of occlusion. Localisation errors of lychee picking
points are within 1.5 cm in all directions, demonstrating the robustness and
generality of the model.
