# Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows

arXiv: https://arxiv.org/abs/2107.00700 · HTML: https://mlsanity.com/paper/2107.00700/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)](https://mlsanity.com/paper/2004.12880/) (2020)
- [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)
- [UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture](https://mlsanity.com/paper/2004.14421/) (2020)

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

- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation](https://mlsanity.com/paper/2209.03355/) (2022)
- [Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation](https://mlsanity.com/paper/2304.01029/) (2023)
- [Vision-based Vineyard Navigation Solution with Automatic Annotation](https://mlsanity.com/paper/2303.14347/) (2023)
- [A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops](https://mlsanity.com/paper/2112.03816/) (2021)
- [Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation](https://mlsanity.com/paper/2306.15517/) (2023)
- [Spannotation: Enhancing Semantic Segmentation for Autonomous Navigation with Efficient Image Annotation](https://mlsanity.com/paper/2402.18084/) (2024)
- [CA-Cut: Crop-Aligned Cutout for Data Augmentation to Learn More Robust Under-Canopy Navigation](https://mlsanity.com/paper/2507.17727/) (2025)
- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)

## Abstract (arXiv)

Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology.