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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/52628

    Título
    A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties
    Autor
    Pourdarbani, Razieh
    Sabzi, Sajad
    Kalantari, Davood
    Hernández Hernández, José Luis
    Arribas Sánchez, Juan IgnacioAutoridad UVA Orcid
    Año del Documento
    2020
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Foods, 2020, vol. 9, n. 2, 113
    Resumen
    Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.
    Palabras Clave
    Chickpeas
    Garbanzos
    Computer vision
    Visión artificial
    Image processing
    Procesamiento de imágenes
    ISSN
    2304-8158
    Revisión por pares
    SI
    DOI
    10.3390/foods9020113
    Patrocinador
    Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP)
    Version del Editor
    https://www.mdpi.com/2304-8158/9/2/113
    Propietario de los Derechos
    © 2020 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/52628
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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    • DEP71 - Artículos de revista [358]
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