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@article{204291,
        author = {Dr.R.Mythili and Mr.S.Appandai Rajan},
        title = {A Plant Leaf Disease Image Classification Method Integrating Capsule Networks and Transformer Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2323-2331},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204291},
        abstract = {Plant leaf diseases pose a serious threat to agricultural productivity and food security. This paper presents a hybrid image classification framework for detecting plant leaf diseases by integrating Capsule Networks and Transformer models with established architectures such as CNN, AlexNet, and VGG16. Capsule networks contribute to preserving spatial hierarchies and relationships of disease features on leaves, improving robustness in recognizing disease patterns despite variations in orientation and damage. Transformer models, known for their self-attention mechanisms, enhance the extraction of global contextual information and long-range dependencies. The hybrid approach leverages the strengths of capsules in capturing local spatial features and transformers in modelling global contextual relationships, leading to superior accuracy and robustness in disease classification. Evaluations on benchmark plant leaf disease datasets demonstrate that the proposed framework outperforms existing methods, highlighting its potential for real-time disease monitoring and precision agriculture applications.},
        keywords = {Agriculture, AlexNet, Capsule Networks, CNN, Plant leaf Disease, Transformer Models, VGG16.},
        month = {June},
        }