Publication2026
Mango Leaf Disease Detection: Deep Hybrid Model With Optimal Tuned Weights
The record
- Type
- Publication
- Date
- 1 March 2026
- Level
- National
- Journal
- Journal of Phytopathology
- Volume
- 174
- Issue
- 2
- Published
- 1 March 2026
- DOI
- 10.1111/jph.70271
- Scopus ID
- 105032145725
- ISSN
- 09311785
- Authors
- 4
- Open access
- No
- Citations
- 2
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
2.3 Double the agricultural productivity and incomes of small-scale food producers
Deep learning system for automated mango leaf disease detection achieves 93% accuracy, enabling early identification to minimize crop losses and improve agricultural productivity.
- 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
Hybrid Bi-LSTM and Deep-Maxout neural network architecture advances machine learning innovation for precision agriculture and crop disease detection applications.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Artificial intelligence for crop disease detection supports agricultural productivity improvement and rural economic resilience in farming communities.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Mango Leaf Disease Detection: Deep Hybrid Model With Optimal Tuned Weights”. Kortex.