Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers
Common bean production in Tanzania is threatened by diseases such as bean rust and bean anthracnose, with early detection critical for effective management. This study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robust...
Saved in:
| Main Authors: | Upendo Mwaibale, Neema Mduma, Hudson Laizer, Bonny Mgawe |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1643582/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Leveraging edge computing and deep learning for the real-time identification of bean plant pathologiesBean Plant Pathologies Dataset for Deep Learning Tasks
by: Andrew Katumba, et al.
Published: (2024-12-01) -
Exploring associations between metabolites and gene transcripts of common bean (Phaseolus vulgaris L.) in response to rust (Uromyces appendiculatus) infection
by: Penny Makhumbila, et al.
Published: (2025-05-01) -
Head and Hands Tunneling Pipeline for Enhancing Sign Language Recognition
by: Ganzorig Batnasan, et al.
Published: (2025-01-01) -
Biochemical Defense Arsenal, Genes/QTLs and Transcripts for Imparting Anthracnose Resistance in Common bean (Phaseolus vulgaris L.)
by: Safoora Shafi, et al.
Published: (2024-12-01) -
MAS-PD: Transferable Adversarial Attack Against Vision-Transformers-Based SAR Image Classification Task
by: Boshi Zheng, et al.
Published: (2025-01-01)