Buchumschlag

Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Amara, Jihen, König-Ries, Birgitta, Samuel, Sheeba
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Computer Vision and Pattern Recognition Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Enhancing Explainability in Multimodal Large Language Models Using Ontological Context
von: Amara, Jihen, et al.
Veröffentlicht: (2024)

Leaf-Based Plant Disease Detection and Explainable AI
von: Sagar, Saurav, et al.
Veröffentlicht: (2023)

Evaluation of State-of-the-Art Deep Learning Techniques for Plant Disease and Pest Detection
von: Banerjee, Saptarshi, et al.
Veröffentlicht: (2025)

Automatic Fused Multimodal Deep Learning for Plant Identification
von: Lapkovskis, Alfreds, et al.
Veröffentlicht: (2024)

YOLOv8-Based Deep Learning Model for Automated Poultry Disease Detection and Health Monitoring paper
von: Sabbella, Akhil Saketh Reddy, et al.
Veröffentlicht: (2025)

AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification
von: Koli, Salma Hoque Talukdar, et al.
Veröffentlicht: (2026)

Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection
von: Mir, Aqib Nazir, et al.
Veröffentlicht: (2024)

Spoof Trace Discovery for Deep Learning Based Explainable Face Anti-Spoofing
von: Zhang, Haoyuan, et al.
Veröffentlicht: (2024)

Towards Counterfactual and Contrastive Explainability and Transparency of DCNN Image Classifiers
von: Tariq, Syed Ali, et al.
Veröffentlicht: (2025)

MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction
von: Feng, Yan, et al.
Veröffentlicht: (2024)

Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies
von: Anaissi, Ali, et al.
Veröffentlicht: (2026)

Explainable Visual Anomaly Detection via Concept Bottleneck Models
von: Stropeni, Arianna, et al.
Veröffentlicht: (2025)

Advancements in Crop Analysis through Deep Learning and Explainable AI
von: Khan, Hamza
Veröffentlicht: (2025)

A Clinically Interpretable Deep CNN Framework for Early Chronic Kidney Disease Prediction Using Grad-CAM-Based Explainable AI
von: Ayub, Anas Bin, et al.
Veröffentlicht: (2025)

XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
von: Bie, Yequan, et al.
Veröffentlicht: (2024)

Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices
von: Moges, Weloday Fikadu, et al.
Veröffentlicht: (2026)

Explainable Deep Learning in Medical Imaging: Brain Tumor and Pneumonia Detection
von: Erukude, Sai Teja, et al.
Veröffentlicht: (2025)

FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy
von: Reyes-Amezcua, Ivan, et al.
Veröffentlicht: (2026)

LeafLife: An Explainable Deep Learning Framework with Robustness for Grape Leaf Disease Recognition
von: Alam, B. M. Shahria, et al.
Veröffentlicht: (2026)

PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
von: Pham, Thang M., et al.
Veröffentlicht: (2024)

Towards Automated Solar Panel Integrity: Hybrid Deep Feature Extraction for Advanced Surface Defect Identification
von: Asif, Muhammad Junaid, et al.
Veröffentlicht: (2026)

A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
von: Astolfi, Giacomo, et al.
Veröffentlicht: (2026)

Explainable Deep Learning for Pediatric Pneumonia Detection in Chest X-Ray Images
von: Khadidos, Adil O., et al.
Veröffentlicht: (2026)

Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation
von: Attard, Jonathan, et al.
Veröffentlicht: (2025)

TeaLeafVision: An Explainable and Robust Deep Learning Framework for Tea Leaf Disease Classification
von: Ahamed, Rafi, et al.
Veröffentlicht: (2026)

Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification
von: Richter, David J.
Veröffentlicht: (2026)

Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications
von: Kommineni, Vamsi Krishna, et al.
Veröffentlicht: (2024)

Understanding the Limitations of Diffusion Concept Algebra Through Food
von: Zeng, E. Zhixuan, et al.
Veröffentlicht: (2024)

CGCE: Classifier-Guided Concept Erasure in Generative Models
von: Nguyen, Viet, et al.
Veröffentlicht: (2025)

LCE: A Framework for Explainability of DNNs for Ultrasound Image Based on Concept Discovery
von: Kong, Weiji, et al.
Veröffentlicht: (2024)

Your VAR Model is Secretly an Efficient and Explainable Generative Classifier
von: Chen, Yi-Chung, et al.
Veröffentlicht: (2025)

MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
von: Bie, Yequan, et al.
Veröffentlicht: (2024)

A Concept-Based Explainability Framework for Large Multimodal Models
von: Parekh, Jayneel, et al.
Veröffentlicht: (2024)

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving
von: Theodoridis, Nikos, et al.
Veröffentlicht: (2026)

Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation
von: McMillan, Carl, et al.
Veröffentlicht: (2023)

Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease
von: Machnio, Julia, et al.
Veröffentlicht: (2025)

MedNet-PVS: A MedNeXt-Based Deep Learning Model for Automated Segmentation of Perivascular Spaces
von: Low, Zhen Xuen Brandon, et al.
Veröffentlicht: (2025)

BarkXAI: A Lightweight Post-Hoc Explainable Method for Tree Species Classification with Quantifiable Concepts
von: Huang, Yunmei, et al.
Veröffentlicht: (2025)

Content Bias in Deep Learning Image Age Approximation: A new Approach Towards better Explainability
von: Jöchl, Robert, et al.
Veröffentlicht: (2023)

Rethinking Plant Disease Diagnosis: Bridging the Academic-Practical Gap with Vision Transformers and Zero-Shot Learning
von: Benabbas, Wassim, et al.
Veröffentlicht: (2025)