Harnessing artificial intelligence for sustainable rice leaf disease classification
The proposed AI-driven system provides reliable and scalable rice leaf disease detection, supporting timely intervention to reduce yield loss. By strengthening rice production and promoting sustainable practices, the model contributes to SDG 2 by advancing global food security. This research highlights AI's transformative role in agriculture, fostering mechanization, ecological stability, and resilience in food systems.
Authors
- Hrudaya Kumar Tripathy (ORCID: https://orcid.org/0000-0002-4960-6228)
- Saurav Mallik (ORCID: https://orcid.org/0000-0003-4107-6784)
- Vazeerudeen Abdul Hameed (ORCID: https://orcid.org/0000-0002-7328-0686)
- Muhammad Ehsan Rana (ORCID: https://orcid.org/0000-0001-5828-4796)
- Ian Kiew Yi Eng
Institutions
- Harvard University (US)
- University of Arizona (US)
- Asia Pacific University of Technology & Innovation (MY)
- KIIT University (IN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-09-25
- DOI
- https://doi.org/10.3389/fpls.2025.1594329
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75