Publication2026
Machine Learning Based Terrain Recognition for Agricultural and Environmental Development
- Zero Hunger
- Industry, Innovation and Infrastructure
- Thriving and Sustainable Economy
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1877 LNNS
- Pages
- 533-540
- Published
- 1 January 2026
- DOI
- 10.1007/978-3-032-19672-9_50
- Scopus ID
- 105039869340
- ISSN
- 23673370
- Authors
- 6
- Open access
- No
- Citations
- 0
- 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
Machine learning CNN models for real-time terrain classification supporting agricultural planning and environmental assessment.
- 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 transfer learning approaches for terrain recognition system development and innovation.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, 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. Machine learning CNN models for real-time terrain classification supporting agricultural planning and environmental assessment.
- 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 Terrain Recognition for Agricultural and Environmental Development”. Kortex.