The integration of advanced technologies in agriculture is revolutionizing crop monitoring and management practices, enhancing efficiency and sustainability. The aim of this work is to examine the combined use of Internet of Things sensor technology, Unmanned Aerial Vehicle drones, digital twins and metrology principles applied to precision agriculture and smart farms and to create a theoretical framework that combines all the new advanced technologies. Internet of Things sensors enable real-Time monitoring of environmental and agronomic parameters while Unmanned Aerial Vehicle drones provide highresolution data for crop analysis and optimized interventions. Metrology ensures the reliability of collected data through calibration and validation processes. This paper explores both the recent and state-of-The-Art of these technologies assembling them into a novel framework, underlining key challenges and future perspectives for a more intelligent and data-driven agriculture.
Reconstructing Agricultural Environments: Smart Farm Development Through Metrology, AI and Remote Sensing
Gagliardi M.;Lamonaca F.;Maurmo D.;Ruga T.;Zumpano E.
2025-01-01
Abstract
The integration of advanced technologies in agriculture is revolutionizing crop monitoring and management practices, enhancing efficiency and sustainability. The aim of this work is to examine the combined use of Internet of Things sensor technology, Unmanned Aerial Vehicle drones, digital twins and metrology principles applied to precision agriculture and smart farms and to create a theoretical framework that combines all the new advanced technologies. Internet of Things sensors enable real-Time monitoring of environmental and agronomic parameters while Unmanned Aerial Vehicle drones provide highresolution data for crop analysis and optimized interventions. Metrology ensures the reliability of collected data through calibration and validation processes. This paper explores both the recent and state-of-The-Art of these technologies assembling them into a novel framework, underlining key challenges and future perspectives for a more intelligent and data-driven agriculture.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


