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

Maddassar Jalal Amandeep Kaur
Aug 2026 · Applied Fruit Science · Vol 68 · 0 citations · 39 references

TL;DR

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.

DOI Semantic Scholar
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