# Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments

arXiv: https://arxiv.org/abs/1908.01885 · HTML: https://mlsanity.com/paper/1908.01885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot](https://mlsanity.com/paper/2410.14790/) (2024)

## Abstract (arXiv)

3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach. The Deep-3DMTS approach is shown to have performance equivalent to the standard 3DMTS baseline in guiding the end effector of a robotic arm to improve the view of occluded fruit (sweet peppers): end effector final position within 11.4 mm of the baseline; and an increase in fruit size in the image by a factor of 17.8 compared to the baseline of 16.8 (avg.).