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Reliable Deep Learning Plant Leaf Disease Classification Light-Chroma Separated BranchesBased on

  • Datos identificativos

    Identificador: imarina:9385566
    Autores:
    Schwarz Schuler, Joao PauloRomani, SantiagoAbdel-Nasser, MohamedRashwan, HatemPuig, Domenec
    Resumen:
    The Food and Agriculture Organization (FAO) estimated that plant diseases cost the world economy $220 billion in 2019. In this paper, we propose a lightweight Deep Convolutional Neural Network (DCNN) for automatic and reliable plant leaf diseases classification. The proposed method starts by converting input images of plant leaves from RGB to CIE LAB coordinates. Then, L and AB channels go into separate branches along with the first three layers of a modified Inception V3 architecture. This approach saves from 1/3 to 1/2 of the parameters in the separated branches. It also provides better classification reliability when perturbing the original RGB images with several types of noise (salt and pepper, blurring, motion blurring and occlusions). These types of noise simulate common image variability found in the natural environment. We hypothesize that the filters in the AB branch provide better resistance to these types of variability due to their relatively low frequency in the image-space domain.
  • Otros:

    Autor según el artículo: Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec
    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
    Palabras clave: Classification Cnn Computer vision Dcnn Deep learnin Deep learning Plant leaf disease Plant village
    Resumen: The Food and Agriculture Organization (FAO) estimated that plant diseases cost the world economy $220 billion in 2019. In this paper, we propose a lightweight Deep Convolutional Neural Network (DCNN) for automatic and reliable plant leaf diseases classification. The proposed method starts by converting input images of plant leaves from RGB to CIE LAB coordinates. Then, L and AB channels go into separate branches along with the first three layers of a modified Inception V3 architecture. This approach saves from 1/3 to 1/2 of the parameters in the separated branches. It also provides better classification reliability when perturbing the original RGB images with several types of noise (salt and pepper, blurring, motion blurring and occlusions). These types of noise simulate common image variability found in the natural environment. We hypothesize that the filters in the AB branch provide better resistance to these types of variability due to their relatively low frequency in the image-space domain.
    Áreas temáticas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: domenec.puig@urv.cat santiago.romani@urv.cat hatem.abdellatif@urv.cat mohamed.abdelnasser@urv.cat
    Identificador del autor: 0000-0002-0562-4205 0000-0001-6673-9615 0000-0001-5421-1637 0000-0002-1074-2441
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://ebooks.iospress.nl/doi/10.3233/FAIA210157
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 339 375-382
    Referencia de l'ítem segons les normes APA: Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec (2021). Reliable Deep Learning Plant Leaf Disease Classification Light-Chroma Separated BranchesBased on. Amsterdam: IOS Press
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3233/FAIA210157
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    Artificial Intelligence
    Classification
    Cnn
    Computer vision
    Dcnn
    Deep learnin
    Deep learning
    Plant leaf disease
    Plant village
    Artificial intelligence
    Ciências agrárias i
    Comunicació i informació
    Engenharias iii
    Engenharias iv
    General o multidisciplinar
    Información y documentación
    Interdisciplinar
    Medicina ii
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