Repositori institucional URV
| Autor/es de la URV: | Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Romaní Also, Santiago |
| Autor según el artículo: | Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec |
| 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 |
| Año de publicación de la revista: | 2021 |
| Tipo de publicación: | Proceedings Paper |
| 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 |
| Referencia al articulo segun fuente origial: | Frontiers In Artificial Intelligence And Applications. 339 375-382 |
| 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. |
| DOI del artículo: | 10.3233/FAIA210157 |
| Enlace a la fuente original: | https://ebooks.iospress.nl/doi/10.3233/FAIA210157 |
| Versión del articulo depositado: | info:eu-repo/semantics/publishedVersion |
| Acceso a la licencia de uso: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departamento: | Enginyeria Informàtica i Matemàtiques |
| URL Documento de licencia: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Áreas temáticas: | Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii |
| Palabras clave: | Classification Cnn Computer vision Dcnn Deep learnin Deep learning Plant leaf disease Plant village |
| Entidad: | Universitat Rovira i Virgili |
| Fecha de alta del registro: | 2024-10-12 |
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| DocumentPrincipal | DocumentPrincipal | application/pdf |
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