A comparative analysis of single- and dual-backbone deep learning architectures with explainable AI for cherry leaf disease classification

Abstract Accurate differentiation of visually similar cherry leaf diseases remains a major challenge in precision agriculture due to overlapping symptom patterns and environmental variability. This study presents a comprehensive deep learning–based framework for multi-class cherry leaf disease class...

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Principais autores:	Hüseyin Tayyip Altay, Özge Demir, Fatih Ekinci, Mehmet Serdar Güzel, Eda Kumru, Ilgaz Akata, Koray Acıcı, Mustafa Sevindik
Format:	Artigo
Jezik:	Inglês
Izdano:	Nature Portfolio 2026-04-01
Serija:	Scientific Reports
Teme:	Cherry leaf disease classification Deep learning Explainable artificial intelligence Grad-CAM Precision agriculture
Online dostop:	https://doi.org/10.1038/s41598-026-50104-1
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