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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/48443

    Título
    Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
    Autor
    Sabzi, Sajad
    Pourdarbani, Razieh
    Rohban, Mohammad H.
    García Mateos, Ginés
    Arribas Sánchez, Juan IgnacioAutoridad UVA Orcid
    Año del Documento
    2021
    Editorial
    Elsevier
    Descripción
    Producción Científica
    Documento Fuente
    Chemometrics and Intelligent Laboratory Systems, 2021, vol. 217, 104404
    Zusammenfassung
    In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
    Palabras Clave
    Hyperspectral imaging
    Imágenes hiperespectrales
    Image processing
    Procesamiento de imágenes
    Nitrogen
    Nitrógeno
    Plants
    Plantas
    ISSN
    0169-7439
    Revisión por pares
    SI
    DOI
    10.1016/j.chemolab.2021.104404
    Patrocinador
    Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (grants RTI2018-098958-B-I00 and RTI2018-098156-B-C53)
    Version del Editor
    https://www.sciencedirect.com/science/article/pii/S0169743921001726?via%3Dihub
    Propietario de los Derechos
    © 2021 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/48443
    Tipo de versión
    info:eu-repo/semantics/submittedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Artículos de revista [358]
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    Dateien zu dieser Ressource
    Nombre:
    Estimation-nitrogen-content.pdf
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