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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2025

Multi-Spectral Image Analysis Using Different CNN Models to Detect the Plant Diseases in its Early Stages

The record

Type
Publication
Date
31 December 2025
Level
National
Journal
International Journal of Advanced Computer Science and Applications
Volume
16
Issue
12
Pages
1230-1237
Published
31 December 2025
DOI
10.14569/ijacsa.2025.01612119
Scopus ID
105028714800
ISSN
2158107X
Authors
3
Open access
Yes
Citations
0
International co-authorship
No
Co-author countries
IN

Classification

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UN Sustainable Development Goals

  1. 2.3 Double the agricultural productivity and incomes of small-scale food producers

    TargetPrimaryHigh confidenceJudged

    Multi-spectral CNN models achieve 93% early-stage plant disease detection accuracy, enabling precision agriculture 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

  1. vb-2.1 Agriculture and the rural economy

    Sub-themePrimaryHigh confidenceJudged

    AI-powered crop disease detection supports sustainable agriculture and productive farm management.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “Multi-Spectral Image Analysis Using Different CNN Models to Detect the Plant Diseases in its Early Stages”. Kortex.

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Multi-Spectral Image Analysis Using Different CNN Models to Detect the Plant Diseases in its Early Stages · Kortex