HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees
This work addresses a domain-specific challenge in agricultural robotics for automating tasks like pruning and harvesting, but it is incremental as it builds on existing regression-based deep learning methods.
The paper tackled the problem of predicting occluded branch positions in 2D fruit trees for orchard automation, proposing HOB-CNN, which outperformed state-of-the-art baselines and showed robustness and generalization across different tree types and occlusion levels.
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.