A Novel Hybrid Deep Learning Validation Model for Real-Time and Synthetic Image Inputs in Capsicum Plant Disease Diagnosis

The diagnosis of plant diseases in Capsicum species remains a critical challenge in precision agriculture due to variability in environmental conditions and limited availability of high-quality datasets. The traditional convolutional neural network (CNN) has been demonstrated to have satisfactory performance, but it cannot capture robust performance in both real-time and synthetic images. This study introduces a novel hybrid deep learning validation model based on Convolutional neural networks (CNN) combined with Capsule networks (CapsNet) and Vision Transformer (ViT) backbone. The framework is intended to be able to validate multi-source image inputs and improve the reliability of the classification in natural and synthetic image environments. In contrast to previous plant disease detection models, which can only be trained on real-time plant images, the proposed CNN–ViT–CapsNet framework features a dual-domain validation process that is able to classify both real-time and the GAN-generated synthetic Capsicum leaf images. CNN-based local feature extraction, ViT-based global contextual learning, and CapsNet-based spatial validation ensure robustness against illumination, orientation, and background conditions.

@article{Vikhe2026,
  title     = {A Novel Hybrid Deep Learning Validation Model for Real-Time and Synthetic Image Inputs in Capsicum Plant Disease Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Prashant Vikhe and Baisa Gunjal},
  doi       = {10.14569/IJACSA.2026.0170633},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170633}
}