Application of Machine Learning Random Forest Algorithm in Digital Soil Mapping of Key Soil Properties for Sustainable Tea Productivity
- DOI
- 10.21203/rs.3.rs-10639002/v1
- Published
- 2026-09-08
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- Not recorded
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.21203/rs.3.rs-10639002/v1,
title = {Application of Machine Learning Random Forest Algorithm in Digital Soil Mapping of Key Soil Properties for Sustainable Tea Productivity},
author = {Finias F. Mwesige and Boniface H. J. Massawe and Hilda G. Sanga and Braison E. Mjanja and Erasto Focus},
year = {2026},
doi = {10.21203/rs.3.rs-10639002/v1},
url = {https://doi.org/10.21203/rs.3.rs-10639002/v1}
}RIS
TY - JOUR TI - Application of Machine Learning Random Forest Algorithm in Digital Soil Mapping of Key Soil Properties for Sustainable Tea Productivity AU - Finias F. Mwesige AU - Boniface H. J. Massawe AU - Hilda G. Sanga AU - Braison E. Mjanja AU - Erasto Focus PY - 2026 DO - 10.21203/rs.3.rs-10639002/v1 UR - https://doi.org/10.21203/rs.3.rs-10639002/v1 ER -
APA
Mwesige, F. F., Massawe, B. H. J., Sanga, H. G., Mjanja, B. E., & Focus, E. (2026). Application of Machine Learning Random Forest Algorithm in Digital Soil Mapping of Key Soil Properties for Sustainable Tea Productivity. https://doi.org/10.21203/rs.3.rs-10639002/v1
Source records
- crossref · retrieved 2026-09-26T07:11:34.531Z