AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting

Technological advances are providing farmers with valuable data about their crops. However, to improve resource use efficiency in agriculture, it is necessary to transform this data into practical information, applicable by farmers and/or technicians in crop management. The objective of this work wa...

Descripción completa

Detalles Bibliográficos
Autores: Puig, F., García Vila, Margarita, Soriano, Mª Auxiliadora, Rodríguez-Díaz, Juan A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/397730
Acceso en línea:http://hdl.handle.net/10261/397730
https://api.elsevier.com/content/abstract/scopus_id/105002127557
Access Level:acceso abierto
Palabra clave:Vision System
Crop Modeling
Data Assimilation
Decision Support System
Precision Irrigation
id ES_cfa5efa30994bdfebf5debd6de4d9412
oai_identifier_str oai:digital.csic.es:10261/397730
network_acronym_str ES
network_name_str España
repository_id_str
spelling AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecastingPuig, F.García Vila, MargaritaSoriano, Mª AuxiliadoraRodríguez-Díaz, Juan A.Vision SystemCrop ModelingData AssimilationDecision Support SystemPrecision IrrigationTechnological advances are providing farmers with valuable data about their crops. However, to improve resource use efficiency in agriculture, it is necessary to transform this data into practical information, applicable by farmers and/or technicians in crop management. The objective of this work was to develop a fully automated IoT platform that integrates crop images from RGB cameras with open climate data sources and crop models, to optimize irrigation strategies and enhance crop productivity under varying environmental conditions. To achieve this, the AquaCrop-IoT platform was developed, which integrates the FAO's AquaCrop model with a custom-build image capture and processing system, used to adjust the green canopy cover (CC) in real-time. Additionally, the platform incorporates weather data from in-situ weather stations, and forecasts and historical weather data from open datasets. Everything is presented in a web application that facilitates its use. The platform has been tested in a wheat crop in southern Spain throughout its growth cycle, demonstrating its potential as a decision support system for irrigation management. Dynamically updating CC values using images captured by the in-situ camera enabled the AquaCrop model to correct potential errors in crop growth estimation by including the effects of adverse factors like pests and diseases that the model cannot simulate. Furthermore, as the developed platform incorporates meteorological data daily, in real-time, it allowed the design of real-time irrigation schedules tailored to the crop in its particular environment and management. This approach improved the estimation of crop water requirements, reducing the amount of recommended irrigation water during the wheat growing season by approximately 32%.This research was funded by the María de Maeztu Unit of Excellence of the Department of Agronomy of the University of Cordoba, the Holistic management of irrigation and fertigation through digital twins project (PID2023–149376OB-C22), funded by the Spanish Ministry of Science and Innovation, and the Qualifica Project 829 QUAL21-023 IAS financed by Junta de Andalucía, Spain.Peer reviewedElsevierUniversidad de Córdoba (España)Ministerio de Ciencia e Innovación (España)Junta de AndalucíaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/397730https://api.elsevier.com/content/abstract/scopus_id/105002127557reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149376OB-C22The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.compag.2025.110372https://doi.org/10.1016/j.compag.2025.110372Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3977302026-05-22T06:33:51Z
dc.title.none.fl_str_mv AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
title AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
spellingShingle AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
Puig, F.
Vision System
Crop Modeling
Data Assimilation
Decision Support System
Precision Irrigation
title_short AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
title_full AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
title_fullStr AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
title_full_unstemmed AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
title_sort AquaCrop-IoT: A smart irrigation platform integrating real-time images and weather forecasting
dc.creator.none.fl_str_mv Puig, F.
García Vila, Margarita
Soriano, Mª Auxiliadora
Rodríguez-Díaz, Juan A.
author Puig, F.
author_facet Puig, F.
García Vila, Margarita
Soriano, Mª Auxiliadora
Rodríguez-Díaz, Juan A.
author_role author
author2 García Vila, Margarita
Soriano, Mª Auxiliadora
Rodríguez-Díaz, Juan A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad de Córdoba (España)
Ministerio de Ciencia e Innovación (España)
Junta de Andalucía
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Vision System
Crop Modeling
Data Assimilation
Decision Support System
Precision Irrigation
topic Vision System
Crop Modeling
Data Assimilation
Decision Support System
Precision Irrigation
description Technological advances are providing farmers with valuable data about their crops. However, to improve resource use efficiency in agriculture, it is necessary to transform this data into practical information, applicable by farmers and/or technicians in crop management. The objective of this work was to develop a fully automated IoT platform that integrates crop images from RGB cameras with open climate data sources and crop models, to optimize irrigation strategies and enhance crop productivity under varying environmental conditions. To achieve this, the AquaCrop-IoT platform was developed, which integrates the FAO's AquaCrop model with a custom-build image capture and processing system, used to adjust the green canopy cover (CC) in real-time. Additionally, the platform incorporates weather data from in-situ weather stations, and forecasts and historical weather data from open datasets. Everything is presented in a web application that facilitates its use. The platform has been tested in a wheat crop in southern Spain throughout its growth cycle, demonstrating its potential as a decision support system for irrigation management. Dynamically updating CC values using images captured by the in-situ camera enabled the AquaCrop model to correct potential errors in crop growth estimation by including the effects of adverse factors like pests and diseases that the model cannot simulate. Furthermore, as the developed platform incorporates meteorological data daily, in real-time, it allowed the design of real-time irrigation schedules tailored to the crop in its particular environment and management. This approach improved the estimation of crop water requirements, reducing the amount of recommended irrigation water during the wheat growing season by approximately 32%.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/397730
https://api.elsevier.com/content/abstract/scopus_id/105002127557
url http://hdl.handle.net/10261/397730
https://api.elsevier.com/content/abstract/scopus_id/105002127557
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149376OB-C22
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.compag.2025.110372
https://doi.org/10.1016/j.compag.2025.110372
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420102275825664
score 15,811543

Similares en LA Referencia