Sugarcane leaf disease classification using deep neural network approach
The study demonstrates the effectiveness of DL models, particularly EfficientNet-B7 and DenseNet201, for fast, accurate, and automatic disease detection in sugarcane leaves. These systems offer a significant improvement over traditional manual methods, enabling farmers and agricultural managers to make timely and informed decisions, ultimately reducing crop loss and enhancing overall sugarcane yield. This work highlights the transformative potential of DL in agriculture.
Authors
- Hariharan Rajadurai
- Sandeep Kumar Mathivanan (ORCID: https://orcid.org/0000-0001-8572-1197)
- S. M. Prabin
- Mohd Asif Shah (ORCID: https://orcid.org/0000-0001-6164-0915)
- Saravanan Srinivasan (ORCID: https://orcid.org/0000-0001-7884-3532)
- Suresh kulandaivelu
Institutions
- Lovely Professional University (IN)
- Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN)
- Galgotias University (IN)
- Kardan University (AF)
- Barkatullah University (IN)
- Chitkara University (IN)
- Vellore Institute of Technology University (IN)
Publication Details
- Journal
- BMC Plant Biology
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1186/s12870-025-06289-0
- Citations
- 18
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 31.50