Publication2026
AI-Powered crop yield forecasting using multispectral satellite data and LSTM-based sequence models
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- International — held abroad
- Journal
- Connecting Intelligence Trends in Computation and Data Communication
- Pages
- 660-665
- Published
- 1 January 2026
- DOI
- 10.1201/9781003773504-112
- Scopus ID
- 105033883648
- Authors
- 3
- Open access
- No
- Citations
- 0
- International co-authorship
- Yes
- Co-author countries
- IN, TW
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
2.3 Double the agricultural productivity and incomes of small-scale food producers
LSTM deep learning model using multispectral satellite data (NDVI, EVI, reflectance bands) achieves R² >0.85 for crop yield forecasting enabling precision agriculture
- 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
Advanced temporal sequence modeling and spectral data integration methodology advancing agricultural remote sensing and deep learning research 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
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
AI-powered crop yield prediction using satellite remote sensing supports decision-making for precision agriculture and sustainable rural economic 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.
Evidence
Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.
No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.
Provenance
Which source system this record came from, how many source rows were resolved into it, and when each was last read.
- Source rows resolved into this record
- 1
Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.
Citing this record
This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.
KIIT Deemed to be University. “AI-Powered crop yield forecasting using multispectral satellite data and LSTM-based sequence models”. Kortex.