Peer-reviewedCC BY 4.0Scientific Reports
Hard attention mechanism integrated with VGG16 for high-precision tomato leaf disease classification
Related
- LeafXNet: A Lightweight Explainable Model With Integrated Attention Mechanism for Fruit Leaf Disease Detection
- Attention-integrated hybrid CNN–vision transformer framework for robust multi-class cervical cytology classification using liquid-based cytology images
- A multi-attention mechanism-improved ResNet18 approach for lithology identification and classification
- Traffic surveillance video-based a unified road accident detection, classification, and vehicle tracking framework using an optimized spatio-temporal attention mechanism
- MangoSwin-attention: a swin transformer with attention pooling for generalizable mango leaf disease classification
BibTeX
@article{laatiri2026hard,
title = {{Hard attention mechanism integrated with VGG16 for high-precision tomato leaf disease classification}},
author = {Youssef Laatiri and Mohamed Ali Mahjoub},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-79107-8},
url = {https://www.nature.com/articles/s41598-026-79107-8},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Hard attention mechanism integrated with VGG16 for high-precision tomato leaf disease classification
AU - Youssef Laatiri
AU - Mohamed Ali Mahjoub
PY - 2026
DA - 2026/11/01
JO - Scientific Reports
DO - 10.1038/s41598-026-79107-8
UR - https://www.nature.com/articles/s41598-026-79107-8
SN - 2045-2322
ER -