A Multi-Model Ensemble Approach for Intelligent and Transparent Plant Disease Detection: A Review
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Plant diseases significantly affect agricultural productivity and food security worldwide. Recent advances in deep learning have enabled automated plant disease detection systems with high classification accuracy. Among these approaches, convolutional neural networks (CNNs), ensemble learning techniques, and explainable artificial intelligence (XAI) methods have emerged as promising solutions. This review presents a comprehensive analysis of recent developments in plant disease detection from 2022 to 2026, focusing on deep learning architectures, ensemble models, benchmark datasets, and explainability techniques. Various publicly available datasets, including PlantVillage, PlantDoc, Plant Pathology 2021, and PlantCLEF, are systematically compared. Furthermore, performance benchmarks of CNN-based models, Vision Transformers (ViTs), hybrid architectures, and ensemble frameworks are reviewed. Research gaps, challenges, and future research directions are identified to guide the development of reliable, interpretable, and field-deployable plant disease detection systems.
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