Titre : |
Identification of Plant-Leaf Diseases Based Generative : Attention CNN and Transfer-Learning Approaches |
Type de document : |
texte imprimé |
Auteurs : |
Abdeldjalil Chougui, Auteur ; Achraf Moussaoui, Auteur ; Moussaou,iAbdelouahab, Directeur de thèse |
Année de publication : |
2022 |
Importance : |
1 vol (118 f .) |
Format : |
29cm |
Langues : |
Français (fre) |
Catégories : |
Thèses & Mémoires:Informatique
|
Mots-clés : |
plant diseases
classification |
Index. décimale : |
004 Informatique |
Résumé : |
etecting plant diseases is usually difficult without an experts knowledge. In this thesis
we want to propose a new classification model based on deep learning that will be able
to classify and identify different plant-leaf diseases with high accuracy that outperform
the state of the art approaches, previous works and the diagnosis of experts in pathology. Using
only training images, CNN can automatically extract features for classification, and achieve high
classification performance. We used two datasets in this study, PlantVillage dataset containing
54,303 healthy and unhealthy leaf images divided into 38 categories by species and disease, and
Tomato dataset containing 11,000 healthy and unhealthy tomato leaf images with nine diseases
to train the models. We propose a deep convolutional neural network architecture, with and
without attention mechanism, and we tuned 9 pretrained models that have been trained on large
dataset such as Inception, Xception, MobileNet, DenseNet, VGG-16, VGG-19, EfficientNet B3,
EfficientNet B5 and ResNET, we also tuned 4 ViT models that was powered by keras(vit b32),
google (base patch 16), microsoft(BeiT) and facebook (DeiT). And we used YoLo V5 for plant leaf
detection. Our porposed model obtained an accuracy up to 97.74%. The pretrained models gave
an accuracy up to 99.86%. YoLo obtained a result of 65.4% recall, and a result of 65.3% precision.
This study may aid in detecting the plant leaf diseases and improve life conditions to plants
which will improve quality of humans life.
Keywords |
Côte titre : |
MAI/0606 |
En ligne : |
https://drive.google.com/file/d/1orsNJnxbCFUZ10QdQQZxfzUcBjFd_miL/view?usp=share [...] |
Format de la ressource électronique : |
pdf |
Identification of Plant-Leaf Diseases Based Generative : Attention CNN and Transfer-Learning Approaches [texte imprimé] / Abdeldjalil Chougui, Auteur ; Achraf Moussaoui, Auteur ; Moussaou,iAbdelouahab, Directeur de thèse . - 2022 . - 1 vol (118 f .) ; 29cm. Langues : Français ( fre)
Catégories : |
Thèses & Mémoires:Informatique
|
Mots-clés : |
plant diseases
classification |
Index. décimale : |
004 Informatique |
Résumé : |
etecting plant diseases is usually difficult without an experts knowledge. In this thesis
we want to propose a new classification model based on deep learning that will be able
to classify and identify different plant-leaf diseases with high accuracy that outperform
the state of the art approaches, previous works and the diagnosis of experts in pathology. Using
only training images, CNN can automatically extract features for classification, and achieve high
classification performance. We used two datasets in this study, PlantVillage dataset containing
54,303 healthy and unhealthy leaf images divided into 38 categories by species and disease, and
Tomato dataset containing 11,000 healthy and unhealthy tomato leaf images with nine diseases
to train the models. We propose a deep convolutional neural network architecture, with and
without attention mechanism, and we tuned 9 pretrained models that have been trained on large
dataset such as Inception, Xception, MobileNet, DenseNet, VGG-16, VGG-19, EfficientNet B3,
EfficientNet B5 and ResNET, we also tuned 4 ViT models that was powered by keras(vit b32),
google (base patch 16), microsoft(BeiT) and facebook (DeiT). And we used YoLo V5 for plant leaf
detection. Our porposed model obtained an accuracy up to 97.74%. The pretrained models gave
an accuracy up to 99.86%. YoLo obtained a result of 65.4% recall, and a result of 65.3% precision.
This study may aid in detecting the plant leaf diseases and improve life conditions to plants
which will improve quality of humans life.
Keywords |
Côte titre : |
MAI/0606 |
En ligne : |
https://drive.google.com/file/d/1orsNJnxbCFUZ10QdQQZxfzUcBjFd_miL/view?usp=share [...] |
Format de la ressource électronique : |
pdf |
|