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Feature-Level Fusion of CNN and Vision Transformer for Tomato Leaf Disease Identification

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  • Afnan Ahmed, Sadiq Ali

    (Department of Electrical EngineeringUniversity of Engineering TechnologyPeshawar, Pakistan)

Abstract

Tomato leaf diseases pose a serious threat to crop yield and quality, necessitating timely and accurate detection for effective management. Traditional visual inspection methods are subjective, labor-intensive, and inefficient, highlighting the need for automated solutions. This study explores the use of transfer learning and fine-tuning of deep learning models, ResNet-50 and Vision Transformers (ViT), for tomato leaf disease detection. A novel hybrid model integrating ResNet-50 and ViT through feature-level fusion is proposed to enhance classification accuracy. While ResNet-50 and ViT achieved accuracies of 95.20% and 98%, respectively, the hybrid model outperformed both with 99.07% accuracy. These results demonstrate the effectiveness and scalability of the hybrid model for early disease detection, offering a promising solution to enhance crop health and agricultural productivity.

Suggested Citation

  • Afnan Ahmed, Sadiq Ali, 2025. "Feature-Level Fusion of CNN and Vision Transformer for Tomato Leaf Disease Identification," International Journal of Innovations in Science & Technology, 50sea, vol. 7(7), pages 38-49, May.
    • Handle: RePEc:abq:ijist1:v:7:y:2025:i:7:p:38-49
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