Dual Fusion Net: A Transformer-Based Hybrid Dual model Architecture for Highly Accurate Chili Leaf Disease Classification

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ID: 312673
2025
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Abstract
Chili leaf diseases significantly impact agricultural productivity, demanding advanced and reliable AI-based detection systems for timely intervention. This research introduces Dual Fusion Net, a Transformer-enhanced hybrid dual-model architecture that integrates InceptionV3 and DenseNet121 to achieve highly accurate chili leaf disease classification. The parallel CNN backbones extract multi-scale and densely connected deep features, while a Transformer-based fusion module learns global contextual relationships across disease patterns. Experimental results demonstrate that Dual Fusion Net outperforms single-model baselines and recent state-of-the-art chili disease classification frameworks. The proposed method achieved an overall accuracy of 98.36%, surpassing standalone InceptionV3 (94.8%) and DenseNet121 (95.6%) as well as recent published models based on EfficientNet, MobileNet, and CNN–Transformer hybrids. Visualization using Grad-CAM and attention maps validates the enhanced interpretability enabled by the Transformer fusion mechanism. The contributions of this experiment is to development of a novel dual-backbone CNN–Transformer fusion architecture, a significant improvement in classification accuracy over cutting-edge baseline models, and real-time inference capability suitable for smart agriculture systems. In future the model is lightweight Edge-AI optimization, transformer efficiency enhancement, and federated learning integration for scalable and privacy-preserving agricultural disease monitoring.
Reference Key
imported_1777055946_69ebb8ca4b713 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Junaid Shakeel
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
10.56979/1001/2025/1136
URL
https://jcbi.org/index.php/Main/article/view/1136
Keywords Keywords not found

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