RFID-based Soil Moisture Sensor for Smart Agriculture

In this work, we present an RFID-based indirect soil moisture sensor based on the application of Machine Learning. More specifically, we suggest an unsupervised approach that does not require information about the real height and moisture levels. This approach can be of great interest in practical a...

Descripción completa

Detalles Bibliográficos
Autores: Martínez Benelmeki, Nedal|||0009-0003-1955-5006, Diaz Machado, Elvis|||0000-0002-6583-8547, del Rio Toledano, Javier, Morell, Antoni|||0000-0003-2249-8594, Lopez Vicario, Jose|||0000-0002-3574-4697
Tipo de recurso: capítulo de libro
Fecha de publicación:2025
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:dnet:uabarcelona_::dd9550e0307706d5d70371939ab03e83
Acceso en línea:https://ddd.uab.cat/record/328259
Access Level:acceso abierto
Palabra clave:Radio frequency identification (RFID)
Agriculture 4.0
Moisture Sensing
RF
Bayesian Machine Learning
id ES_394da3fd44ad1d9e7ae1c3b1cee656f2
oai_identifier_str oai:dnet:uabarcelona_::dd9550e0307706d5d70371939ab03e83
network_acronym_str ES
network_name_str España
repository_id_str
spelling RFID-based Soil Moisture Sensor for Smart Agriculturea Gaussian Mixture Model ApproachMartínez Benelmeki, Nedal|||0009-0003-1955-5006Diaz Machado, Elvis|||0000-0002-6583-8547del Rio Toledano, JavierMorell, Antoni|||0000-0003-2249-8594Lopez Vicario, Jose|||0000-0002-3574-4697Radio frequency identification (RFID)Agriculture 4.0Moisture SensingRFBayesian Machine LearningIn this work, we present an RFID-based indirect soil moisture sensor based on the application of Machine Learning. More specifically, we suggest an unsupervised approach that does not require information about the real height and moisture levels. This approach can be of great interest in practical agricultural deployments, where the careful deployment of tags at specific depths within the soil is challenging. It allows an estimation of the posterior probability of moisture, based on the available Received Signal Strength Indicator (RSSI) and phase. The suggested method enables the RFID system to operate as a sensor by probabilistically quantifying measurement uncertainty, which is a key distinction from existing ethodologies. In this paper, we focus on two differentiated moisture cases to show the validity of our approach. Future research will extend the proposed methodology to a wider set of moisture levels.URSI 22025-01-0120252025-01-01Capítol de llibrehttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://ddd.uab.cat/record/328259reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2022-139929NB-I00Generalitat de Catalunya https://doi.org/10.13039/501100002809 2021/SGR-00197open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:dnet:uabarcelona_::dd9550e0307706d5d70371939ab03e832026-06-06T12:50:31Z
dc.title.none.fl_str_mv RFID-based Soil Moisture Sensor for Smart Agriculture
a Gaussian Mixture Model Approach
title RFID-based Soil Moisture Sensor for Smart Agriculture
spellingShingle RFID-based Soil Moisture Sensor for Smart Agriculture
Martínez Benelmeki, Nedal|||0009-0003-1955-5006
Radio frequency identification (RFID)
Agriculture 4.0
Moisture Sensing
RF
Bayesian Machine Learning
title_short RFID-based Soil Moisture Sensor for Smart Agriculture
title_full RFID-based Soil Moisture Sensor for Smart Agriculture
title_fullStr RFID-based Soil Moisture Sensor for Smart Agriculture
title_full_unstemmed RFID-based Soil Moisture Sensor for Smart Agriculture
title_sort RFID-based Soil Moisture Sensor for Smart Agriculture
dc.creator.none.fl_str_mv Martínez Benelmeki, Nedal|||0009-0003-1955-5006
Diaz Machado, Elvis|||0000-0002-6583-8547
del Rio Toledano, Javier
Morell, Antoni|||0000-0003-2249-8594
Lopez Vicario, Jose|||0000-0002-3574-4697
author Martínez Benelmeki, Nedal|||0009-0003-1955-5006
author_facet Martínez Benelmeki, Nedal|||0009-0003-1955-5006
Diaz Machado, Elvis|||0000-0002-6583-8547
del Rio Toledano, Javier
Morell, Antoni|||0000-0003-2249-8594
Lopez Vicario, Jose|||0000-0002-3574-4697
author_role author
author2 Diaz Machado, Elvis|||0000-0002-6583-8547
del Rio Toledano, Javier
Morell, Antoni|||0000-0003-2249-8594
Lopez Vicario, Jose|||0000-0002-3574-4697
author2_role author
author
author
author
dc.subject.none.fl_str_mv Radio frequency identification (RFID)
Agriculture 4.0
Moisture Sensing
RF
Bayesian Machine Learning
topic Radio frequency identification (RFID)
Agriculture 4.0
Moisture Sensing
RF
Bayesian Machine Learning
description In this work, we present an RFID-based indirect soil moisture sensor based on the application of Machine Learning. More specifically, we suggest an unsupervised approach that does not require information about the real height and moisture levels. This approach can be of great interest in practical agricultural deployments, where the careful deployment of tags at specific depths within the soil is challenging. It allows an estimation of the posterior probability of moisture, based on the available Received Signal Strength Indicator (RSSI) and phase. The suggested method enables the RFID system to operate as a sensor by probabilistically quantifying measurement uncertainty, which is a key distinction from existing ethodologies. In this paper, we focus on two differentiated moisture cases to show the validity of our approach. Future research will extend the proposed methodology to a wider set of moisture levels.
publishDate 2025
dc.date.none.fl_str_mv 2
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv Capítol de llibre
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/328259
url https://ddd.uab.cat/record/328259
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2022-139929NB-I00
Generalitat de Catalunya https://doi.org/10.13039/501100002809 2021/SGR-00197
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv URSI
publisher.none.fl_str_mv URSI
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406156111216640
score 15.228081

Similares en LA Referencia