Publication2026
Detection of Fungal Diseases in Cashew Plant Leaves Using Machine Learning and Advanced Deep Learning Techniques
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1367 LNNS
- Pages
- 463-475
- Published
- 1 January 2026
- DOI
- 10.1007/978-981-96-6329-3_36
- Scopus ID
- 105027933238
- ISSN
- 23673370
- Authors
- 7
- 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 for early fungal disease detection in cashew plants enables farmer decision-making and crop loss prevention
- 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
vb-2.1 Agriculture and the rural economy
Agricultural disease management through advanced ML supports farmer livelihoods and rural economy resilience
- 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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Provenance
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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. “Detection of Fungal Diseases in Cashew Plant Leaves Using Machine Learning and Advanced Deep Learning Techniques”. Kortex.