Evidence map›Paper›PMID 39409239›Full record

ArticleSensors (Basel, Switzerland)2024

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Oumaima AfifDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.ORCID 0009-0002-2091-3862
Leonardo FranceschelliDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.ORCID 0000-0003-0093-5431
Eleonora IaccheriDepartment of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy.
Simone TrovarelloDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.ORCID 0000-0001-7821-0620
Alessandra Di Florio Di RenzoDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.ORCID 0009-0007-0683-9631
Luigi RagniDepartment of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy.
Alessandra CostanzoDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.
Marco TartagniDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.

Funding

European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA 425 E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 - D.D. 1032 426 17/06/2022 CN00000022
6 · The paper itself

Abstract

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.

Indexed as

machine learningmicrowave sensorsNanoVNAprecision agricultureRaspberry Pi

Identifiers

PMID39409239
PMCPMC11478617

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.