# Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

arXiv: https://arxiv.org/abs/1911.03098 · HTML: https://mlsanity.com/paper/1911.03098/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 13 of 13)

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- [Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots](https://mlsanity.com/paper/2403.14056/) (2024)
- [On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics](https://mlsanity.com/paper/2303.12499/) (2023)
- [Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery](https://mlsanity.com/paper/2502.18320/) (2025)
- [Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures](https://mlsanity.com/paper/2602.15533/) (2026)
- [Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks](https://mlsanity.com/paper/2102.01460/) (2021)
- [Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches](https://mlsanity.com/paper/2308.00159/) (2023)
- [LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons](https://mlsanity.com/paper/2510.22164/) (2025)
- [SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms](https://mlsanity.com/paper/2603.00120/) (2026)
- [Embodiment-conditioned Generalist Control for Multirotor Aerial Robots](https://mlsanity.com/paper/2606.10857/) (2026)

## Abstract (arXiv)

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention.