| Titre : | Plant leaves disease severity stimation |
| Auteurs : | Wissam Abid, Auteur ; Nesrine Bouadjenek, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | [S.l.] : [s.n.], 2025 |
| Format : | 1 fichier PDF (15.8 Mo) |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 89-92 |
| Langues : | Anglais |
| Index. décimale : | PN00125 |
| Tags : | Smart agriculture Disease severity Estimation Yellow rust Transformer encoder Vision transformer (ViT) Swin transformer Multi-head self-attention Feature extraction |
| Résumé : |
Smart agriculture aims to improve crop monitoring through automated and accurate analysis of plant health. A critical task in this domain is disease severity estimation, which focuses on identifying the progression stages of plant infections. In this work, we propose a deep learning-based solution using two transformer architectures: Vision Transformer (ViT) and Swin Transformer. These models are implemented, evaluated, and combined into a novel architecture that leverages ViTs global attention and Swins hierarchical local attention for fine-grained severity classification. The models are trained on Wheat Yellow Rust dataset, which includes six severity stages. Finally, results show that the combined model outperforms individual baselines, providing an effective solution for automated severity estimation. |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire |
|---|---|---|---|---|---|---|
| PN00125 | Ressources électroniques | Bibliothèque centrale | Projet Fin d'Etudes | Disponible | Electronique | Téléchargeable |
