150746 20251017144649.0 doi 10.1016/j.compag.2025.110018 sideral 142648 ART-2025-142648 eng Lacueva-Pérez, Francisco José (orcid)0000-0003-0998-2939 Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices 2025 Access copy available to the general public Unrestricted Smart 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. info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00 info:eu-repo/grantAgreement/ES/DGA/T17-23R info:eu-repo/grantAgreement/ES/DGA/T64-23R info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101086461/EU/Smart Farm and Agri-environmental Big Data Space/AgriDataValue info:eu-repo/semantics/embargoedAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Hoyo-Alonso, Rafael del (orcid)0000-0003-2755-5500 Labata-Leazaún, Gorka Barriuso-Vargas, Juan José Universidad de Zaragoza (orcid)0000-0003-2980-5454 Ilarri-Artigas, Sergio Universidad de Zaragoza (orcid)0000-0002-7073-219X 5011 705 Universidad de Zaragoza Dpto. CC.Agrar.y Medio Natural Área Producción Vegetal 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 231 (2025), 110018 [21 pp.] Comput. electron. agric. Computers and Electronics in Agriculture 0168-1699 6708639 http://zaguan.unizar.es/record/150746/files/texto_completo.pdf Postprint info:eu-repo/date/embargoEnd/2027-02-03 1923211 http://zaguan.unizar.es/record/150746/files/texto_completo.jpg?subformat=icon icon Postprint info:eu-repo/date/embargoEnd/2027-02-03 oai:zaguan.unizar.es:150746 articulos driver 2025-10-17-14:35:30 ARTICLE