| KEYWORD |
UAV-based Data Collection in Large Scale Agricultural Fields
Thesis abroad
Reference persons GUIDO MARCHETTO, ALESSIO SACCO
External reference persons Prof. Simone Silvestri - University of Kentucky, USA
Research Groups DAUIN - GR-03 - COMPUTER NETWORKS GROUP - NETGROUP
Description The Internet of Agricultural Things (IoAT) is an increasingly important opportunity to improve farmers’ awareness and monitoring capabilities for decision making. As an example, IoAT data could provide information on irrigation, use of fertilizers, detection of illnesses, and general crop health. However, the large scale and a lack of internet connectivity of agricultural fields makes it inconvenient, and often impossible, to form a connected network of IoAT devices. Unmanned Aerial Vehicles are a valid alternative for this purpose, although their limited capabilities make it challenging to collect data accurately and comprehensively. In this thesis, we will develop machine learning based solutions, and specifically feature selection approaches paired with trajectory planning algorithms, to improve the accuracy of collecting data using UAVs in large-scale agricultural fields.
Deadline 04/04/2025
PROPONI LA TUA CANDIDATURA