A hybrid convolution and attention-based framework with visual explanation for fruit disease identification

Abstract The objective of this study is to create a highly accurate and interpretable deep learning (DL) model for the multi-class classification of fruit using convolutional and transformer architectures. The classification performance can be enhanced by making sure that the used technique is expla...

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Principais autores:	Raja Kothandaraman, Saravanan Srinivasan, Sandeep Kumar Mathivanan, Usha Moorthy
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Nature Portfolio 2026-03-01
coleção:	Scientific Reports
Assuntos:	Fruit disease Multi-class fruit classification DL Transformer architecture and Grad-CAM
Acesso em linha:	https://doi.org/10.1038/s41598-026-42135-5
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