Occluded Branches Tree

Fruit tree pruning and fruit thinning require a powerful vision system that can provide high resolution segmentation of the fruit trees and their branches. However, recent works only consider the dormant season, where there are minimal occlusions on the branches or fit a polynomial curve to reconstruct branch shape and hence, losing information about branch thickness. In this work, we apply

In this work, the branch reconstruction focuses only on reconstructing the main branches o the trunk where apples are likely to grow during the thinning season. To detect the branches of the tree, a branch of deep learning, semantic segmentation is often applied.

Given RGB-D input of trees with partially occluded branches, the models estimate depth values of only the wooden parts of the tree. A large photorealistic simulation dataset comprising around 44 K images of nine different tree species is generated, on which the models are trained.

This project presents HOB-CNNv2, a regression deep learning vision model fine-tuned for detecting continuous tree branches under substantial natural occlusions experienced during summer. Building upon our preceding work detailed in the HOB-CNN paper HOB-CNN Hallucination of Occluded Branches with

Bark-included junctions in trees are considered a defect as the bark weakens the union between the branches. To more accurately assess this weakening effect, 241 bifurcations from young specimens of hazel Corylus avellana L., of which 106 had bark inclusions, were harvested and subjected to rupture tests. Three-point bending of the smaller branches acted as a benchmark for the relative

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.

Distribution of occluded branches according to tree height the whole dataset with respect to the number of occluded branches NOB and mean diameter of occluded branches MDOB. Plots were made

On nine ashes and nine oaks, six branches were pruned from each tree. On three trees of each species the branch stubs were 2 inches long, on three trees the stubs were 1 inch long, and on three trees the pruning cuts were through the branch collar almost flush with the tree trunk.

Superior Performance in Extreme Conditions The HOB-CNNv2 showcases remarkable accuracy in detecting tree branches even in heavily and extremely occluded conditions, standing distinctly ahead of the prevailing U-Net semantic segmentation model in evaluating branch position and thickness. Pioneering Solution for Extreme Occlusions To the best of our knowledge, HOB-CNNv2 is the inaugural vision

The model is tested under two occlusion conditions, heavily occluded and extremely occluded. The experimental results show that HOB-CNNv2 can accurately detect tree branches in both occlusion conditions and outperforms the state-of-the-art semantic segmentation model, U-Net, in terms of branch position and thickness accuracy.