UAV-based Data Collection in Large Scale Agricultural Fields
Riferimenti esterni Prof. Simone Silvestri - University of Kentucky, USA
Gruppi di ricerca DAUIN - GR-03 - COMPUTER NETWORKS GROUP - NETGROUP
Descrizione The Internet of Agricultural Things (IoAT) is an increasingly important opportunity to improve farmersí 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.
Scadenza validita proposta 04/05/2024 PROPONI LA TUA CANDIDATURA