# Towards Autonomous Visual Navigation in Arable Fields

arXiv: https://arxiv.org/abs/2109.11936 · HTML: https://mlsanity.com/paper/2109.11936/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Visual Servoing-based Navigation for Monitoring Row-Crop Fields](https://mlsanity.com/paper/1909.12754/) (2019)
- [Towards agricultural autonomy: crop row detection under varying field conditions using deep learning](https://mlsanity.com/paper/2109.08247/) (2021)
- [From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields](https://mlsanity.com/paper/1709.04751/) (2017)

## Built on by (most cited first, 6 of 6)

- [PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics](https://mlsanity.com/paper/2309.05339/) (2023)
- [Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection](https://mlsanity.com/paper/2304.04333/) (2023)
- [Data-Centric Digital Agriculture: A Perspective](https://mlsanity.com/paper/2312.03437/) (2023)
- [Vision-based Vineyard Navigation Solution with Automatic Annotation](https://mlsanity.com/paper/2303.14347/) (2023)
- [Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots](https://mlsanity.com/paper/2209.04278/) (2022)
- [DropClick: Semi-Automated One-Click Segmentation for Agricultural Robotic Data](https://mlsanity.com/paper/2609.03680/) (2026)

## Abstract (arXiv)

Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technology is expensive and also prone to failure (e.g. through lack of coverage). As such, autonomous navigation through sensors that can interpret their environment (such as cameras) is important to achieve the goal of autonomy in agriculture. In this paper, we introduce a purely vision-based navigation scheme that is able to reliably guide the robot through row-crop fields without manual intervention. Independent of any global localization or mapping, this approach is able to accurately follow the crop-rows and switch between the rows, only using onboard cameras. With the help of a novel crop-row detection and a novel crop-row switching technique, our navigation scheme can be deployed in a wide range of fields with different canopy types in various growth stages with limited parameter tuning, creating a crop agnostic navigation approach. We have extensively evaluated our approach in three different fields under various illumination conditions using our agricultural robotic platform (BonnBot-I). For navigation, our approach is evaluated on five crop types and achieves an average navigation accuracy of 3.82cm relative to manual teleoperation.