Titre : |
Vision Transformer Based Deep Learning Models for Plant Disease Detection and Diagnosis |
Type de document : |
texte imprimé |
Auteurs : |
Rayene Amina Boukabouya, Auteur ; Zakaria Bouzour, Auteur ; Abdelouahab Moussaoui, Directeur de thèse |
Année de publication : |
2022 |
Langues : |
Français (fre) |
Catégories : |
Thèses & Mémoires:Informatique
|
Mots-clés : |
Deep learning
Vision Transformers |
Index. décimale : |
004 Informatique |
Résumé : |
ne particular use of deep learning is classification. In this thesis, we want to
Compare multiple classification models based on classical neural networks and
vision Transformers to classify and identify different plant diseases at early
stages with high accuracy that outperform SOTA models. Plant health is one of the
most crucial things in a natural cycle, it needs to be preserved to maintain the life
of the individuals. The diagnosed at late stages, there is almost no chance to reverse
agricultural crops, which means the increase of lesions of hunger food in the world and
the farmers will lose many dollars, loss their time and their hard work. We propose
deep convolutional neural network architectures and vision transformers and tuned on
tomato and grape dataset, base model CNN, InceptionV3, VGG-16, CNN with Attention
and ViT. we compare our models for the tomatoes leaf and, base CNN, VGG16, Efficient3,
and InceptionV3 Vit for the second Dataset. The recent vision transformers model gives
the performance more than the previously published works using the same data for the
Tomatoes leaf, where we obtained an accuracy up to 99.7% and for the grape we achieved
98%.
This technology might help speed up the classification and treatment of treatable
illnesses, allowing for early treatment and better agricultural results |
Côte titre : |
MAI/0589 |
En ligne : |
https://drive.google.com/file/d/1luRodvYBcwPD52z6rtYFhSGLjHj0yzFN/view?usp=share [...] |
Format de la ressource électronique : |
pdf |
Vision Transformer Based Deep Learning Models for Plant Disease Detection and Diagnosis [texte imprimé] / Rayene Amina Boukabouya, Auteur ; Zakaria Bouzour, Auteur ; Abdelouahab Moussaoui, Directeur de thèse . - 2022. Langues : Français ( fre)
Catégories : |
Thèses & Mémoires:Informatique
|
Mots-clés : |
Deep learning
Vision Transformers |
Index. décimale : |
004 Informatique |
Résumé : |
ne particular use of deep learning is classification. In this thesis, we want to
Compare multiple classification models based on classical neural networks and
vision Transformers to classify and identify different plant diseases at early
stages with high accuracy that outperform SOTA models. Plant health is one of the
most crucial things in a natural cycle, it needs to be preserved to maintain the life
of the individuals. The diagnosed at late stages, there is almost no chance to reverse
agricultural crops, which means the increase of lesions of hunger food in the world and
the farmers will lose many dollars, loss their time and their hard work. We propose
deep convolutional neural network architectures and vision transformers and tuned on
tomato and grape dataset, base model CNN, InceptionV3, VGG-16, CNN with Attention
and ViT. we compare our models for the tomatoes leaf and, base CNN, VGG16, Efficient3,
and InceptionV3 Vit for the second Dataset. The recent vision transformers model gives
the performance more than the previously published works using the same data for the
Tomatoes leaf, where we obtained an accuracy up to 99.7% and for the grape we achieved
98%.
This technology might help speed up the classification and treatment of treatable
illnesses, allowing for early treatment and better agricultural results |
Côte titre : |
MAI/0589 |
En ligne : |
https://drive.google.com/file/d/1luRodvYBcwPD52z6rtYFhSGLjHj0yzFN/view?usp=share [...] |
Format de la ressource électronique : |
pdf |
|