Ensemble Deep Learning-based Semantic Segmentation and Classification of Leaves Images for Agricultural Apple and Wheat Diseases’ Detection / Naryméne Kebiche  

Public ISBD Titre : Ensemble Deep Learning-based Semantic Segmentation and Classification of Leaves Images for Agricultural Apple and Wheat Diseases’ Detection Type de document : texte imprimé Auteurs : Naryméne Kebiche, Auteur ; Faiçal Ammisaid, Auteur ; Abdelouahab Moussaoui, Directeur de thèse Année de publication : 2023 Importance : 1 vol (165 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Plant disease detection Classification Index. décimale : 004 Informatique Résumé : Providing food for the world’s rapidly expanding population is a challenging task with Plant diseases reducing food production due to a demand and supply mismatch, thus,Early detection and appropriate pest control methods are vital to increase crop yield. This master’s thesis proposes reliable approaches for early detection and diagnosis of agricultural plant diseases through the analysis of leaf images. The method uses ensemble deep learning-based semantic segmentation and classification techniques by including transfer learning of CNN-based architectures, a focus on U-Net Attention Mechanisms integration also adapting CNN’s and ViT (Segformer) based encoders, examination of Vision transformers, lastly utilizing ensemble learning to help enhancing models prediction’s confidence. The study also highlights the limited availability of datasets for supervised semantic segmentation in the field of agriculture, resulting in the creation of a new wheat segmentation dataset we conducted Evaluation on public and new datasets demonstrates the method’s efficacy with interesting semantic segmentation scores. Notably, the models achieved up to 77.95% (IoU) and 87.58% (Dice score) for binary semantic segmentation and 98.66% (IoU) and 97.35% (Dice score) for multi-class segmentation on the ATLDSD dataset. Additionally, the models reached 96.30% (IoU) and 98.11% (Dice score) on the newly annotated wheat dataset. Côte titre : MAI/0699 En ligne : https://drive.google.com/file/d/1lxucKFuuP7fc1NLnHviaxI_tE28yAx43/view?usp=drive [...] Format de la ressource électronique : pdf Ensemble Deep Learning-based Semantic Segmentation and Classification of Leaves Images for Agricultural Apple and Wheat Diseases’ Detection [texte imprimé] / Naryméne Kebiche, Auteur ; Faiçal Ammisaid, Auteur ; Abdelouahab Moussaoui, Directeur de thèse . - 2023 . - 1 vol (165 f .) ; 29cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Plant disease detection Classification Index. décimale : 004 Informatique Résumé : Providing food for the world’s rapidly expanding population is a challenging task with Plant diseases reducing food production due to a demand and supply mismatch, thus,Early detection and appropriate pest control methods are vital to increase crop yield. This master’s thesis proposes reliable approaches for early detection and diagnosis of agricultural plant diseases through the analysis of leaf images. The method uses ensemble deep learning-based semantic segmentation and classification techniques by including transfer learning of CNN-based architectures, a focus on U-Net Attention Mechanisms integration also adapting CNN’s and ViT (Segformer) based encoders, examination of Vision transformers, lastly utilizing ensemble learning to help enhancing models prediction’s confidence. The study also highlights the limited availability of datasets for supervised semantic segmentation in the field of agriculture, resulting in the creation of a new wheat segmentation dataset we conducted Evaluation on public and new datasets demonstrates the method’s efficacy with interesting semantic segmentation scores. Notably, the models achieved up to 77.95% (IoU) and 87.58% (Dice score) for binary semantic segmentation and 98.66% (IoU) and 97.35% (Dice score) for multi-class segmentation on the ATLDSD dataset. Additionally, the models reached 96.30% (IoU) and 98.11% (Dice score) on the newly annotated wheat dataset. Côte titre : MAI/0699 En ligne : https://drive.google.com/file/d/1lxucKFuuP7fc1NLnHviaxI_tE28yAx43/view?usp=drive [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0699	MAI/0699	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

---

1 (1 - 1 / 1)