A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture
Oumaima Afif,
Leonardo Franceschelli,
Eleonora Iaccheri,
Simone Trovarello,
Alessandra Di Florio Di Renzo,
Luigi Ragni,
Alessandra Costanzo,
Marco Tartagni
Affiliations
Oumaima Afif
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy
Leonardo Franceschelli
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy
Eleonora Iaccheri
Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy
Simone Trovarello
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy
Alessandra Di Florio Di Renzo
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy
Luigi Ragni
Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy
Alessandra Costanzo
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy
Marco Tartagni
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy
This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.
