# Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection

arXiv: https://arxiv.org/abs/2304.04333 · HTML: https://mlsanity.com/paper/2304.04333/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Deep Hough Transform for Semantic Line Detection](https://mlsanity.com/paper/2003.04676/) (2020)
- [Visual Servoing-based Navigation for Monitoring Row-Crop Fields](https://mlsanity.com/paper/1909.12754/) (2019)
- [Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy](https://mlsanity.com/paper/2005.12815/) (2020)
- [Towards Autonomous Visual Navigation in Arable Fields](https://mlsanity.com/paper/2109.11936/) (2021)
- [Towards agricultural autonomy: crop row detection under varying field conditions using deep learning](https://mlsanity.com/paper/2109.08247/) (2021)
- [Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching](https://mlsanity.com/paper/2203.15285/) (2022)

## Built on by (most cited first, 9 of 9)

- [NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2506.23468/) (2025)
- [BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation](https://mlsanity.com/paper/2306.08075/) (2023)
- [MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation](https://mlsanity.com/paper/2311.18331/) (2023)
- [Unified Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2311.13254/) (2023)
- [Hierarchical end-to-end autonomous navigation through few-shot waypoint detection](https://mlsanity.com/paper/2409.14633/) (2024)
- [Learning Flow-Guided Registration for RGB-Event Semantic Segmentation](https://mlsanity.com/paper/2505.01548/) (2025)
- [AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot](https://mlsanity.com/paper/2508.18694/) (2025)
- [Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024](https://mlsanity.com/paper/2406.00587/) (2024)
- [Selective Cotton Boll Localization for Robotic Harvesting: Evaluation of Deep Learning Vision Models Under Field Conditions](https://mlsanity.com/paper/2609.19592/) (2026)

## Abstract (arXiv)

The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propose Agronav, an end-to-end vision-based autonomous navigation framework, which outputs the centerline from the input image by sequentially processing it through semantic segmentation and semantic line detection models. We also present Agroscapes, a pixel-level annotated dataset collected across six different crops, captured from varying heights and angles. This ensures that the framework trained on Agroscapes is generalizable across both ground and aerial robotic platforms. Codes, models and dataset will be released at hrefhttps://github.com/shivamkumarpanda/agronavgithub.com/shivamkumarpanda/agronav.