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Orchard automation has attracted the attention of researchers recently due tothe shortage of global labor force. To automate tasks in orchards such aspruning, thinning, and harvesting, a detailed understanding of the treestructure is required. However, occlusions from foliage and fruits can make itchallenging to predict the position of occluded trunks and branches. This workproposes a regression-based deep learning model, Hallucination of OccludedBranch Convolutional Neural Network (HOB-CNN), for tree branch positionprediction in varying occluded conditions. We formulate tree branch positionprediction as a regression problem towards the horizontal locations of thebranch along the vertical direction or vice versa. We present comparativeexperiments on Y-shaped trees with two state-of-the-art baselines, representingcommon approaches to the problem. Experiments show that HOB-CNN outperform thebaselines at predicting branch position and shows robustness against varyinglevels of occlusion. We further validated HOB-CNN against two different typesof 2D trees, and HOB-CNN shows generalization across different trees androbustness under different occluded conditions.

HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees