10.46243/jst.2020.v5.i3.pp160-165 registered
Arecanut Crop Disease Prediction using IoT and Machine Learning
Resolves to https://www.jst.org.in/index.php/pub/article/view/438
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2020.v5.i3.pp160-165
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 95de679399f2414d…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
Arecanut Crop Disease Prediction using IoT and Machine Learning (PrincipalTitle)
Published 2020-05-29
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 5 · issue 3
Agents
- SharathKumar KR (author)
- Mohan K (author) · ORCID 0009-0002-7687-9798
- Nirisha (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2020.v5.i3.pp160-165
Abstract
A prevailing recession in the agricultural goods sector is evident from the present scarcity and lack of food supplies. A major reason for this scarcity is the inherent growth of diseases in essential crops. A major development is thus required in this field for avoiding these problems in the future. This development is intended to simplify the management tasks of different roles in agricultural industries. A proper intimation of the importance of disease prediction and environmental factors must be done to the less aware farmers. To address these challenges, we have proposed a disease prediction system that takes into consideration temperature (°C), humidity(%), rainfall(cm), wind flow(m/s) and soil moisture (%) around the region of crop and developed a model to predict the occurrence of disease. This system will provide information prior to the occurrence of disease by analyzing different relationships among environmental factors.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2020.v5.i3.pp160-165 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Arecanut Crop Disease Prediction using IoT and Machine Learning (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: SharathKumar KR author: Mohan K author: Nirisha publisher: Longman Publishers published: 2020-05-29 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 5 · no. 3 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2020-05-29 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
complete Every System Metadata element is here, with the basic metadata a journal article needs.
Recommended for a journal article and not in this record: the first page or an article number.
The System Metadata Declaration (JSON) · the Kernel Metadata Declaration (XML) · what each sub-type needs
History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 80 fields set · sha256 1803d9febb5f… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
Machine-readable: the history as JSON, with the full record after each change.
