SinGAN-CBAM: a multi-scale GAN with attention for few-shot plant disease image generation
This study validates the effectiveness of dual-dimensional attention mechanisms in enhancing the quality of agricultural few-shot image generation, providing a high-quality data augmentation solution for intelligent disease identification with promising practical applications.
Authors
- Canyu Wang (ORCID: https://orcid.org/0000-0002-5178-3776)
- Mengyao Wu (ORCID: https://orcid.org/0000-0001-9000-4459)
- Jianping Yang (ORCID: https://orcid.org/0000-0001-5431-3457)
- Zhiyong Lv
- Yadong Li (ORCID: https://orcid.org/0000-0002-7166-3261)
- Li Chen (ORCID: https://orcid.org/0000-0001-7836-4849)
- Xinrui Wang (ORCID: https://orcid.org/0000-0003-0139-3144)
- Rongbiao Ji
Institutions
- Yunnan Agricultural University (CN)
- Qujing Normal University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-11-14
- DOI
- https://doi.org/10.3389/fpls.2025.1703529
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00