Publication2024
A Hybrid Approach to Rice Leaf Disease Detection Using Transfer Learning and SVM
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- 2024 12th International Conference on Intelligent Systems and Embedded Design Ised 2024
- Published
- 1 January 2024
- DOI
- 10.1109/ised63599.2024.10956632
- Scopus ID
- 105003903068
- Authors
- 3
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
2 End hunger, achieve food security and improved nutrition and promote sustainable agriculture
Transfer learning and SVM hybrid approach for rice leaf disease detection improves agricultural crop health management.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep learning application to crop disease detection advances AI research capacity in agricultural technology.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
AI-enabled crop disease detection supports agriculture and rural economy through improved productivity.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “A Hybrid Approach to Rice Leaf Disease Detection Using Transfer Learning and SVM”. Kortex.