AI-powered detection of pumpkin leaf diseases using DualFusion-CBAM-stochastic for yield protection and precision agriculture
The results confirm that the synergistic integration of dual-backbone fusion, attention-guided refinement, and stochastic-depth regularization significantly enhances classification performance, feature interpretability, and model stability under diverse visual conditions. These findings advance automated pumpkin leaf disease diagnosis and provide a strong methodological foundation for future research in agricultural image analysis.
Authors
- M. S. T. Abbas
- Upinder Kaur (ORCID: https://orcid.org/0000-0002-5638-474X)
- Rubee Singh (ORCID: https://orcid.org/0000-0002-5406-2210)
- Nitika Kapoor (ORCID: https://orcid.org/0000-0002-3705-7524)
- Amel Ksibi
- Nitin Kumar (ORCID: https://orcid.org/0009-0001-2370-3492)
- Mudassir Khan
- Jaibir Singh
- Ruchika Bhuria
Institutions
- Chandigarh University (IN)
- Princess Nourah bint Abdulrahman University (SA)
- Lovely Professional University (IN)
- Galgotias University (IN)
- Jadara University (JO)
- Graphic Era University (IN)
- Chitkara University (IN)
- King Khalid University (SA)
- Punjab Engineering College (IN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2026-01-30
- DOI
- https://doi.org/10.3389/fpls.2025.1717226
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00