Publication2025
Deep Learning Applications for Detecting Crop Diseases from Image Data
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- IEEE International Conference on Computational Communication and Information Technology Icccit 2025
- Pages
- 566-571
- Published
- 1 January 2025
- DOI
- 10.1109/icccit62592.2025.10928061
- Scopus ID
- 105002218315
- Authors
- 6
- Open access
- No
- Citations
- 4
- 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
2.4 Ensure sustainable food production systems and resilient agricultural practices
Convolutional neural networks for automated crop disease identification from leaf images enable timely intervention preventing crop loss and ensuring 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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep learning methodology for disease classification demonstrates computer vision research advancement in agricultural technology.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Smart farming disease detection technology supports agricultural productivity and rural economy development.
- 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. “Deep Learning Applications for Detecting Crop Diseases from Image Data”. Kortex.