Publication2023
Ascribing Machine Learning Classifiers to diagnose the attacks of Alternaria solani on Leaves of Solanum tuberosum
The record
- Type
- Publication
- Date
- 1 January 2023
- Level
- National
- Journal
- Iccsc 2023 Proceedings of the 2nd International Conference on Computational Systems and Communication
- Published
- 1 January 2023
- DOI
- 10.1109/iccsc56913.2023.10142978
- Scopus ID
- 85162619517
- Authors
- 6
- Open access
- No
- Citations
- 9
- 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
2.1 End hunger and ensure access to safe, nutritious and sufficient food all year round
Machine learning classifiers detect Alternaria solani early blight disease in potato leaves for crop protection
- 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
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Agricultural disease detection technology directly supports crop productivity and rural economy
- 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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Provenance
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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. “Ascribing Machine Learning Classifiers to diagnose the attacks of Alternaria solani on Leaves of Solanum tuberosum”. Kortex.