Publication2021
Parametric analysis on crop/weed classification via optimized convolutional neural network
The record
- Type
- Publication
- Date
- 4 February 2021
- Level
- National
- Journal
- Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2021
- Pages
- 1477-1484
- Published
- 4 February 2021
- DOI
- 10.1109/icicv50876.2021.9388462
- Scopus ID
- 85104488627
- Authors
- 4
- Open access
- No
- Citations
- 2
- 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
Optimized CNN-based crop/weed classification improves agricultural productivity through automated field 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
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Machine learning application for precision agriculture supports sustainable crop production and food security.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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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. “Parametric analysis on crop/weed classification via optimized convolutional neural network”. Kortex.