Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
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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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 1; 2025: 3; 2026: 2.
12 papers.
- 2023Vision-based Learning for Drones: A Surveycited by 9
- 2022Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Datacited by 7
- 2023Data-Centric Digital Agriculture: A Perspectivecited by 5
- 2025An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVscited by 4
- 2024Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robotscited by 2
- 2023On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Roboticscited by 1
- 2025Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagerycited by 1
- 2021Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networkscited by 0
- 2023Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approachescited by 0
- 2025LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletonscited by 0
- 2026SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarmscited by 0
- 2026Embodiment-conditioned Generalist Control for Multirotor Aerial Robotscited by 0
- 2026Embodiment-conditioned Generalist Control for Multirotor Aerial Robots9 Jun 2026
- 2026SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms23 Feb 2026
- 2025LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons25 Oct 2025
- 2025Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery25 Feb 2025
- 2025An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVs20 Feb 2025
- 2024Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots21 Mar 2024
- 2023Vision-based Learning for Drones: A Survey8 Dec 2023
- 2023Data-Centric Digital Agriculture: A Perspective6 Dec 2023
- 2023Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches31 Jul 2023
- 2023On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics22 Mar 2023
- 2022Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data27 Aug 2022
- 2021Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks2 Feb 2021