Special Issue "3D Modelling and Mapping for Precision Agriculture"
Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 43628
Interests: precision agriculture; robotics; remote sensing; image processing; machine learning
Interests: agricultural machinery; sensors; precision agriculture
An effective precision agriculture (PA) management approach relies on accurate knowledge of the agricultural environment, with the aim of timely and properly performing site specific operations. Recent solutions for PA are based on unmanned vehicles, both ground (UGVs) and aerial (UAVs), that can profitably perform crop scouting and monitoring tasks, and even accomplish several management operations in an autonomous way.
In this context, the contribution of 3D models of crops to the improvements of PA practices is rapidly growing. Indeed, point clouds of agricultural environments can be profitably exploited to retrieve information on the crop status, geometries, field yield, and other valuable agronomical indices. In addition, 3D models are proving to be an effective input of robust control and navigation algorithms of autonomous vehicles in complex scenarios, such as the agricultural ones, allowing for enhanced obstacles and targets detection. In order to mine valuable information for agricultural purposes from 3D point clouds, however, specific computing frameworks are usually required, many of which are based on artificial intelligence (AI) and machine learning (ML) methods.
The goal of this Special Issue is to present an up-to-date overview of the recent achievements in the field of 3D modelling and mapping in agriculture, as well as to identify the obstacles still ahead. Review and research papers on, but not limited to, the following topics are welcome:
- Time of flight (ToF) and structured light (SL) technologies for PA
- Structure from motion (SfM) methods for PA
- 3D point cloud processing
- Machine learning and artificial intelligence
- Crop 3D modelling
- Field 3D mapping
- Navigation and control based on 3D point clouds
- Agricultural UAVs
- Agricultural UGVs
- Agricultural robots
Dr. Lorenzo Comba
Dr. Jordi Llorens
Dr. Alessandro Biglia
Manuscript Submission Information
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- Precision agriculture
- 3D point clouds
- Sensing for automation
- Crop monitoring
- Drones and robotics
- UAVs and UGVs
- Feature extraction
- Machine learning
- Semantic segmentation