Publication2022
Machine learning-based approaches for tomato pest classification
The record
- Type
- Publication
- Date
- 1 January 2022
- Level
- National
- Journal
- Telkomnika Telecommunication Computing Electronics and Control
- Volume
- 20
- Issue
- 2
- Pages
- 321-328
- Published
- 1 January 2022
- DOI
- 10.12928/telkomnika.v20i2.19740
- Scopus ID
- 85126936939
- ISSN
- 16936930
- Authors
- 2
- Open access
- Yes
- Citations
- 37
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
2 End hunger, achieve food security and improved nutrition and promote sustainable agriculture
Machine learning classification of tomato pests achieves 81% accuracy for pest detection, supporting precision agriculture and 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
Precision agriculture pest detection supports sustainable agricultural productivity and rural livelihoods.
- 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. “Machine learning-based approaches for tomato pest classification”. Kortex.