A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions
- DOI
- 10.3389/fpls.2026.1892566
- Published
- 2026-07-01
- Container
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fpls.2026.1892566,
title = {A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions},
author = {Lei Sun and Hao Li and Shangkun Li and Shuanghui Zhao and Yanqun Zhang and Yan Mo and Baozhong Zhang and Youjie Wu},
year = {2026},
journal = {Frontiers in Plant Science},
doi = {10.3389/fpls.2026.1892566},
url = {https://doi.org/10.3389/fpls.2026.1892566}
}RIS
TY - JOUR TI - A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions AU - Lei Sun AU - Hao Li AU - Shangkun Li AU - Shuanghui Zhao AU - Yanqun Zhang AU - Yan Mo AU - Baozhong Zhang AU - Youjie Wu PY - 2026 JO - Frontiers in Plant Science DO - 10.3389/fpls.2026.1892566 UR - https://doi.org/10.3389/fpls.2026.1892566 ER -
APA
Sun, L., Li, H., Li, S., Zhao, S., Zhang, Y., Mo, Y., Zhang, B., & Wu, Y. (2026). A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2026.1892566
Source records
- crossref · retrieved 2026-09-26T00:35:00.750Z