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Color-Aware Two-Branch DCNN for Efficient Plant Disease Classificat

  • Datos identificativos

    Identificador: imarina:9280248
    Autores:
    Schuler JPSRomani SAbdel-Nasser MRashwan HPuig D
    Resumen:
    Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.
  • Otros:

    Autor según el artículo: Schuler JPS; Romani S; Abdel-Nasser M; Rashwan H; Puig D
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Romaní Also, Santiago / Schwarz Schuler, Joao Paulo
    Palabras clave: Plant disease Neural networks Multipath Deep learning Dcnn Cnn Cie lab Artificial intelligence
    Resumen: Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.
    Áreas temáticas: Theoretical computer science General computer science Decision sciences (miscellaneous) Control and systems engineering Computer science (miscellaneous) Computer science (all) Computational mathematics Artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat joaopaulo.schwarz@estudiants.urv.cat santiago.romani@urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0002-7582-0711 0000-0001-6673-9615 0000-0002-0562-4205
    Fecha de alta del registro: 2024-09-21
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://mendel-journal.org/index.php/mendel/article/view/176
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Mendel. 28 (1): 55-62
    Referencia de l'ítem segons les normes APA: Schuler JPS; Romani S; Abdel-Nasser M; Rashwan H; Puig D (2022). Color-Aware Two-Branch DCNN for Efficient Plant Disease Classificat. Mendel, 28(1), 55-62. DOI: 10.13164/mendel.2022.1.055
    DOI del artículo: 10.13164/mendel.2022.1.055
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Artificial Intelligence,Computational Mathematics,Computer Science (Miscellaneous),Control and Systems Engineering,Decision Sciences (Miscellaneous),Theoretical Computer Science
    Plant disease
    Neural networks
    Multipath
    Deep learning
    Dcnn
    Cnn
    Cie lab
    Artificial intelligence
    Theoretical computer science
    General computer science
    Decision sciences (miscellaneous)
    Control and systems engineering
    Computer science (miscellaneous)
    Computer science (all)
    Computational mathematics
    Artificial intelligence
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