000150746 001__ 150746
000150746 005__ 20251017144649.0
000150746 0247_ $$2doi$$a10.1016/j.compag.2025.110018
000150746 0248_ $$2sideral$$a142648
000150746 037__ $$aART-2025-142648
000150746 041__ $$aeng
000150746 100__ $$0(orcid)0000-0003-0998-2939$$aLacueva-Pérez, Francisco José
000150746 245__ $$aDeveloping machine learning models from multisourced real-world datasets to enhance smart-farming practices
000150746 260__ $$c2025
000150746 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150746 5203_ $$aSmart farming technologies empower farmers to achieve sustainability by enabling data-driven decision-making. For instance, smart farming can contribute to farm sustainability through optimized pest control. By utilizing pest risk prediction models, farmers can conserve resources and minimize environmental impact by avoiding unnecessary treatments. However, effective crop pest control relies on timely treatments at specific phenological stages. Therefore, the ability to accurately predict phenological development becomes a crucial factor in increasing farm sustainability.
This paper describes the design, development and evaluation of a set of Machine Learning models that predict the phenology of grapevines. The models were trained on multisourced data that combine 9 different datasets with different temporal and spatial resolutions. The authors evaluated and compared different machine learning algorithms to predict 9 different phenological stages of grapevines. The models that performed best also included data derived from Sentinel-2 images, which suggests that multispectral satellite images could be used to monitor and predict woody plant phenology. A key contribution of our proposal is the combination of multiple data sources and a fine-grained prediction aimed at distinguishing among 9 phenological states.
000150746 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T17-23R$$9info:eu-repo/grantAgreement/ES/DGA/T64-23R$$9info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101086461/EU/Smart Farm and Agri-environmental Big Data Space/AgriDataValue
000150746 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000150746 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000150746 700__ $$0(orcid)0000-0003-2755-5500$$aHoyo-Alonso, Rafael del
000150746 700__ $$aLabata-Leazaún, Gorka
000150746 700__ $$0(orcid)0000-0003-2980-5454$$aBarriuso-Vargas, Juan José$$uUniversidad de Zaragoza
000150746 700__ $$0(orcid)0000-0002-7073-219X$$aIlarri-Artigas, Sergio$$uUniversidad de Zaragoza
000150746 7102_ $$15011$$2705$$aUniversidad de Zaragoza$$bDpto. CC.Agrar.y Medio Natural$$cÁrea Producción Vegetal
000150746 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000150746 773__ $$g231 (2025), 110018 [21 pp.]$$pComput. electron. agric.$$tComputers and Electronics in Agriculture$$x0168-1699
000150746 8564_ $$s6708639$$uhttps://zaguan.unizar.es/record/150746/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-02-03
000150746 8564_ $$s1923211$$uhttps://zaguan.unizar.es/record/150746/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-02-03
000150746 909CO $$ooai:zaguan.unizar.es:150746$$particulos$$pdriver
000150746 951__ $$a2025-10-17-14:35:30
000150746 980__ $$aARTICLE