# Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields

arXiv: https://arxiv.org/abs/2209.14003 · HTML: https://mlsanity.com/paper/2209.14003/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2103.04233/) (2021)
- [TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2109.05120/) (2021)
- [Visual Servoing-based Navigation for Monitoring Row-Crop Fields](https://mlsanity.com/paper/1909.12754/) (2019)
- [Towards Infield Navigation: leveraging simulated data for crop row detection](https://mlsanity.com/paper/2204.01811/) (2022)

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

- [Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation](https://mlsanity.com/paper/2306.05869/) (2023)

## Abstract (arXiv)

Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for all such variations. The lack of comprehensive datasets in agricultural environments limits the researchers from developing robust segmentation models to detect crop rows. We present a dataset for crop row detection with 11 field variations from Sugar Beet and Maize crops. We also present a novel crop row detection algorithm for visual servoing in crop row fields. Our algorithm can detect crop rows against varying field conditions such as curved crop rows, weed presence, discontinuities, growth stages, tramlines, shadows and light levels. Our method only uses RGB images from a front-mounted camera on a Husky robot to predict crop rows. Our method outperformed the classic colour based crop row detection baseline. Dense weed presence within inter-row space and discontinuities in crop rows were the most challenging field conditions for our crop row detection algorithm. Our method can detect the end of the crop row and navigate the robot towards the headland area when it reaches the end of the crop row.