Publication2025
Development of an SDG-driven Convolutional Neural Network (CNN) model for multi-class classification of Tomato Leaf diseases using combined public and local datasets
- Zero Hunger
- Partnerships for the Goals
- Thriving and Sustainable Economy
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- International — held abroad
- Journal
- Journal of Integrated Science and Technology
- Volume
- 13
- Issue
- 3
- Published
- 1 January 2025
- DOI
- 10.62110/sciencein.jist.2025.v13.1063
- Scopus ID
- 85216540435
- ISSN
- 23214635
- Authors
- 6
- Open access
- Yes
- Citations
- 7
- International co-authorship
- Yes
- Co-author countries
- ET, 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
Custom CNN model achieves 99.8% accuracy in multi-class classification of tomato leaf diseases from combined public and local datasets, improving crop yield and food security.
- 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
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-2 Thriving and Sustainable Economy
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 2 label. Custom CNN model achieves 99.8% accuracy in multi-class classification of tomato leaf diseases from combined public and local datasets, improving crop yield and food security.
- 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. “Development of an SDG-driven Convolutional Neural Network (CNN) model for multi-class classification of Tomato Leaf diseases using combined public and local datasets”. Kortex.