# Towards agricultural autonomy: crop row detection under varying field conditions using deep learning

arXiv: https://arxiv.org/abs/2109.08247 · HTML: https://mlsanity.com/paper/2109.08247/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [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)

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

- [Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection](https://mlsanity.com/paper/2304.04333/) (2023)
- [Towards Autonomous Visual Navigation in Arable Fields](https://mlsanity.com/paper/2109.11936/) (2021)
- [Using UAS Imagery and Computer Vision to Support Site-Specific Weed Control in Corn](https://mlsanity.com/paper/2206.01734/) (2022)
- [DSER: Spectral Epipolar Representation for Efficient Light Field Depth Estimation](https://mlsanity.com/paper/2508.08900/) (2025)

## Abstract (arXiv)

This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories encountered under various field conditions was used for testing. The effect on these conditions on the angular accuracy of crop row detection was compared. A deep convolutional encoder decoder network is implemented to predict crop row masks using RGB input images. The predicted mask is then sent to a post processing algorithm to extract the crop rows. The deep learning model was found to be robust against shadows and growth stages of the crop while the performance was reduced under direct sunlight, increasing weed density, tramlines and discontinuities in crop rows when evaluated with the novel metric.