  Orchard automation has attracted the attention of researchers recently due to
the shortage of global labor force. To automate tasks in orchards such as
pruning, thinning, and harvesting, a detailed understanding of the tree
structure is required. However, occlusions from foliage and fruits can make it
challenging to predict the position of occluded trunks and branches. This work
proposes a regression-based deep learning model, Hallucination of Occluded
Branch Convolutional Neural Network (HOB-CNN), for tree branch position
prediction in varying occluded conditions. We formulate tree branch position
prediction as a regression problem towards the horizontal locations of the
branch along the vertical direction or vice versa. We present comparative
experiments on Y-shaped trees with two state-of-the-art baselines, representing
common approaches to the problem. Experiments show that HOB-CNN outperform the
baselines at predicting branch position and shows robustness against varying
levels of occlusion. We further validated HOB-CNN against two different types
of 2D trees, and HOB-CNN shows generalization across different trees and
robustness under different occluded conditions.
