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

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.

Automated Iterative Training of Convolutional Neural Networks for Tree Skeleton Segmentation

Training of convolutional neural networks for semantic segmentation requires accurate pixel-wise labeling. Depending on the application this can require large amounts of human effort. The human-in-the-loop method reduces labeling effort but still requires human intervention for a selection of images. This paper describes a new iterative training method: Automating-the-loop. Automating-the-loop aims to replicate the human adjustment in human-in-the-loop, with an automated process. Thereby, removing human intervention during the iterative process and drastically reducing labeling effort. Using the application of segmented apple tree detection, we compare human-in-the-loop, Self Training Loop, Filtered-Self Training Loop (semi-supervised learning) and our proposed method automating-the-loop. These methods are used to train U-Net, a deep learning based convolutional neural network. The results are presented and analyzed on both traditional performance metrics and a new metric, Horizontal Scan. It is shown that the new method of automating-the-loop greatly reduces the labeling effort while generating a network with comparable performance to both human-in-the-loop and completely manual labeling.