Amandeep Kaur

From Handcrafted Features to Transformers: A Hybrid CNN–Vision Transformer Framework for Cherry Leaf Disease Detection

This study proposes a hybrid technique that integrates attention-weighted exponential pooling (AWEP) with CNN and Vision Transformer (ViT) to enhance feature representation and significantly improve classification performance and highlights that ViT improves embedding separability through t‑distributed stochastic neighbor embedding (t-SNE), thereby reducing overfitting and producing fewer misclassifications in visually similar classes.