Edge-computing smart irrigation controller using LoRaWAN and LSTM for predictive controlled deficit irrigation

Baseca, Carlos Cambra; Dionísio, Rogério Pais; Ribeiro, Fernando Reinaldo; Metrôlho, J.C.M.M

Publication

Edge-computing smart irrigation controller using LoRaWAN and LSTM for predictive controlled deficit irrigation

2025Research article

dc.contributor.author	Baseca, Carlos Cambra
dc.contributor.author	Dionísio, Rogério Pais
dc.contributor.author	Ribeiro, Fernando Reinaldo
dc.contributor.author	Metrôlho, J.C.M.M
dc.contributor.editor	Yu, Guanding
dc.date.accessioned	2025-11-24T16:12:46Z
dc.date.available	2025-11-24T16:12:46Z
dc.date.issued	2025
dc.description.abstract	Enhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can help save irrigation water and produce almonds more sustainably. This work presents an IoT-enabled edge computing model for smart irrigation systems focused on precision agriculture. This model combines IoT sensors, hybrid machine learning algorithms, and edge computing to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high density almond tree fields applying reductions of 35% ETc (crop evapotranspiration). By gathering and analyzing meteorological, humidity soil, and crop data, a soft ML (Machine Learning) model has been developed to enhance irrigation practices and identify crop anomalies in real-time without cloud computing. This methodology has the potential to transform agricultural practices by enabling precise and efficient water management, even in remote locations with lack of internet access. This study represents an initial step toward implementing ML algorithms for irrigation CDI strategies.	eng
dc.identifier.citation	BASECA, C. C. [et al.] (2025) - Edge-computing smart irrigation controller using LoRaWAN and LSTM for predictive controlled deficit irrigation. Sensors. 25:22, p. 7079. DOI: 10.3390/s25227079
dc.identifier.doi	10.3390/s25227079
dc.identifier.eissn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10400.11/10368
dc.language.iso	eng
dc.peerreviewed	yes
dc.publisher	MDPI
dc.relation.hasversion	https://www.mdpi.com/1424-8220/25/22/7079
dc.relation.ispartofseries	Special Issue "Wireless Sensor Networks in Industrial/Agricultural Environments"; Sensor Networks
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	wireless sensor networks
dc.subject	Internet of Things
dc.subject	smart irrigation controllers
dc.subject	edge computing in sustainable deficit irrigation
dc.subject	almonds smart farming
dc.subject	precision agriculture
dc.title	Edge-computing smart irrigation controller using LoRaWAN and LSTM for predictive controlled deficit irrigation	eng
dc.type	research article
dspace.entity.type	Publication
oaire.citation.endPage	15
oaire.citation.issue	7079
oaire.citation.startPage	1
oaire.citation.title	Sensors
oaire.citation.volume	25
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyName	PAIS DIONÍSIO
person.familyName	Reinaldo Silva Garcia Ribeiro
person.familyName	Metrôlho
person.givenName	ROGÉRIO
person.givenName	Fernando
person.givenName	José Carlos
person.identifier	30skvuAAAAAJ
person.identifier	1688084
person.identifier.ciencia-id	2F1A-414F-368B
person.identifier.ciencia-id	7B1C-D761-291D
person.identifier.ciencia-id	4B17-3AF4-7DD4
person.identifier.orcid	0000-0002-6810-2447
person.identifier.orcid	0000-0002-1225-3844
person.identifier.orcid	0000-0002-7327-2109
person.identifier.scopus-author-id	6507997502
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