RTCB: an integrated deep learning model for garlic leaf disease identification
The proposed approach provides a scalable technical reference for the engineering application of automatic disease monitoring and control in intelligent agriculture. The current strategy is conducive to the deployment of edge computing equipment and has extensive significance and application potential in plant leaf disease detection.
Authors
- Laixiang Xu (ORCID: https://orcid.org/0000-0001-5412-7689)
- Mohammad Nazir Ahmad (ORCID: https://orcid.org/0000-0003-3639-1157)
- Xueli Zheng (ORCID: https://orcid.org/0000-0001-8394-0310)
- J.A.L. van Kan (ORCID: https://orcid.org/0000-0003-3563-1550)
- Jia Liu (ORCID: https://orcid.org/0000-0001-8424-7972)
- Xiao‐Ming Chen (ORCID: https://orcid.org/0000-0002-3353-7918)
- Junmin Zhao
Institutions
- Asia Pacific University of Technology & Innovation (MY)
- Henan University of Urban Construction (CN)
- Henan Institute of Technology (CN)
- Xinxiang University (CN)
- University of Kuala Lumpur (MY)
- National University of Malaysia (MY)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-10-16
- DOI
- https://doi.org/10.3389/fpls.2025.1687300
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Strong
- National Natural Science Foundation of China
- Henan University