Crops yield prediction based on machine learning models: Case of West African countries
- DOI
- 10.1016/j.atech.2022.100049
- Published
- 12
- Container
- Smart Agricultural Technology
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1016/j.atech.2022.100049,
title = {Crops yield prediction based on machine learning models: Case of West African countries},
author = {Lontsi Saadio Cedric and Wilfried Yves Hamilton Adoni and Rubby Aworka and Jérémie Thouakesseh Zoueu and Franck Kalala Mutombo and Moez Krichen and Charles Lebon Mberi Kimpolo},
year = {2022},
journal = {Smart Agricultural Technology},
doi = {10.1016/j.atech.2022.100049},
url = {https://doi.org/10.1016/j.atech.2022.100049}
}RIS
TY - JOUR TI - Crops yield prediction based on machine learning models: Case of West African countries AU - Lontsi Saadio Cedric AU - Wilfried Yves Hamilton Adoni AU - Rubby Aworka AU - Jérémie Thouakesseh Zoueu AU - Franck Kalala Mutombo AU - Moez Krichen AU - Charles Lebon Mberi Kimpolo PY - 2022 JO - Smart Agricultural Technology DO - 10.1016/j.atech.2022.100049 UR - https://doi.org/10.1016/j.atech.2022.100049 ER -
APA
Cedric, L. S., Adoni, W. Y. H., Aworka, R., Zoueu, J. T., Mutombo, F. K., Krichen, M., & Kimpolo, C. L. M. (2022). Crops yield prediction based on machine learning models: Case of West African countries. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2022.100049
Source records
- doaj · retrieved 2026-09-25T08:58:02.168Z