# Vision-based Vineyard Navigation Solution with Automatic Annotation

arXiv: https://arxiv.org/abs/2303.14347 · HTML: https://mlsanity.com/paper/2303.14347/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [Visual Servoing-based Navigation for Monitoring Row-Crop Fields](https://mlsanity.com/paper/1909.12754/) (2019)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows](https://mlsanity.com/paper/2107.00700/) (2021)
- [Towards Autonomous Visual Navigation in Arable Fields](https://mlsanity.com/paper/2109.11936/) (2021)
- [High Precision Control of Tracked Field Robots in the Presence of Unknown Traction Coefficients](https://mlsanity.com/paper/2103.11294/) (2021)
- [Semantic Feature Matching for Robust Mapping in Agriculture](https://mlsanity.com/paper/2107.04178/) (2021)

## Built on by (most cited first, 4 of 4)

- [Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning](https://mlsanity.com/paper/2601.18714/) (2026)
- [A Vision-Based Navigation System for Arable Fields](https://mlsanity.com/paper/2309.11989/) (2023)
- [Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation](https://mlsanity.com/paper/2509.18342/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)

## Abstract (arXiv)

Autonomous navigation is the key to achieving the full automation of agricultural research and production management (e.g., disease management and yield prediction) using agricultural robots. In this paper, we introduced a vision-based autonomous navigation framework for agriculture robots in trellised cropping systems such as vineyards. To achieve this, we proposed a novel learning-based method to estimate the path traversibility heatmap directly from an RGB-D image and subsequently convert the heatmap to a preferred traversal path. An automatic annotation pipeline was developed to form a training dataset by projecting RTK GPS paths collected during the first setup in a vineyard in corresponding RGB-D images as ground-truth path annotations, allowing a fast model training and fine-tuning without costly human annotation. The trained path detection model was used to develop a full navigation framework consisting of row tracking and row switching modules, enabling a robot to traverse within a crop row and transit between crop rows to cover an entire vineyard autonomously. Extensive field trials were conducted in three different vineyards to demonstrate that the developed path detection model and navigation framework provided a cost-effective, accurate, and robust autonomous navigation solution in the vineyard and could be generalized to unseen vineyards with stable performance.