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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/67046

    Título
    Incorporating meteorological data and pesticide information to forecast crop yields using machine learning
    Autor
    Hoque, Jiabul
    Islam, Saiful
    Uddin, Jia
    Samad, Abdus
    Sainz de Abajo, BeatrizAutoridad UVA Orcid
    Ramírez Vargas, Débora Libertad
    Ashraf, Imran
    Año del Documento
    2024
    Editorial
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC.
    Descripción
    Producción Científica
    Documento Fuente
    IEEE Access, Marzo 2024, vol. 12, p. 47768-47786.
    Résumé
    The agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information for sustainable agricultural practices. This research presents a novel crop yield prediction system utilizing a year’s worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model’s performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination (R2) of 99.99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future regarding food availability and robustness to climate change.
    Materias (normalizadas)
    Deep learning models
    Palabras Clave
    Agriculture
    Crop yield prediction
    Machine Learning
    Deep learning
    ISSN
    2169-3536
    Revisión por pares
    SI
    DOI
    10.1109/ACCESS.2024.3383309
    Patrocinador
    European University of Atlantic
    Version del Editor
    https://ieeexplore.ieee.org/document/10485420
    Propietario de los Derechos
    "© Todos los derechos reservados". Propietario de los derechos: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC.
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/67046
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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    • DEP71 - Artículos de revista [358]
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    Published FINAL paper.pdf
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    Attribution-NonCommercial-NoDerivatives 4.0 InternacionalExcepté là où spécifié autrement, la license de ce document est décrite en tant que Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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