Attention-enhanced hybrid deep learning model for robust mango leaf disease classification via ConvNeXt and vision transformer fusion
Mango is a crop of vital agronomic and commercial importance, particularly in tropical and subtropical regions. Accurate and timely identification of foliar diseases is essential for maintaining plant health and ensuring sustainable agricultural productivity. This study proposes MangoLeafCMDF-FAMNet...
Saved in:
| Main Author: | Ebru Ergün |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1638520/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Citrus Disease Classification Model Based on Improved ConvNeXt
by: Jichi Yan, et al.
Published: (2024-01-01) -
ViSwNeXtNet Deep Patch-Wise Ensemble of Vision Transformers and ConvNeXt for Robust Binary Histopathology Classification
by: Özgen Arslan Solmaz, et al.
Published: (2025-06-01) -
Enhancing Melanoma Diagnosis with Advanced Deep Learning Models Focusing on Vision Transformer, Swin Transformer, and ConvNeXt
by: Serra Aksoy, et al.
Published: (2024-08-01) -
An ESG-ConvNeXt network for steel surface defect classification based on hybrid attention mechanism
by: Ning Zhang, et al.
Published: (2025-03-01) -
Radar Waveform Recognition With ConvNeXt and Focal Loss
by: Liping Luo, et al.
Published: (2024-01-01)