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    • SCIENTIFIC PRODUCTION
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    • Dpto. Ingeniería Agrícola y Forestal
    • DEP42 - Artículos de revista
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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/63485

    Título
    Big data and machine learning to improve European grapevine moth (Lobesia botrana) predictions
    Autor
    Balduque Gil, Joaquín
    Lacueva Pérez, Francisco J.
    Labata Lezaun, Gorka
    Ilarri, Sergio
    Del Hoyo Alonso, Rafael
    Sánchez Hernández, EvaAutoridad UVA Orcid
    Martín Ramos, PabloAutoridad UVA Orcid
    Barriuso Vargas, Juan José
    Año del Documento
    2023
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Plants, 2023, Vol. 12, Nº. 3, 633
    Abstract
    Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest’s flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.
    Materias (normalizadas)
    Lobesia botrana
    Grapes - Diseases and pests
    Vid - Enfermedades y plagas
    Pesticides - Application
    Control de plagas
    Big data
    Internet of things
    Internet de las cosas
    Internet - Tecnología
    Meteorology
    Weather data
    Machine learning
    Aprendizaje automático
    Artificial intelligence
    Pests - Integrated control
    Manejo integrado de plagas
    Plant Science
    Ecology
    Ecología
    Materias Unesco
    3102 Ingeniería Agrícola
    2509.01 Meteorología agrícola
    3101.09 Plaguicidas
    1203.04 Inteligencia Artificial
    ISSN
    2223-7747
    Revisión por pares
    SI
    DOI
    10.3390/plants12030633
    Patrocinador
    European Union’s Connecting Europe Facility (CEF) - (Grant INEA/CEF/ICT/A2018/1837816 GRAPEVINE project)
    Version del Editor
    https://www.mdpi.com/2223-7747/12/3/633
    Propietario de los Derechos
    © 2023 The authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/63485
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Collections
    • DEP42 - Artículos de revista [291]
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    Atribución 4.0 InternacionalExcept where otherwise noted, this item's license is described as Atribución 4.0 Internacional

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