High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning
This study demonstrates that multi-source UAV image feature fusion with ensemble learning effectively leverages complementary data strengths, offering an efficient solution for monitoring rice AGB across growth stages.
Authors
- Dongxue Zhao
- Qiang Cao (ORCID: https://orcid.org/0000-0001-9890-323X)
- Fenghua Yu (ORCID: https://orcid.org/0000-0001-6545-9873)
- Yingli Cao (ORCID: https://orcid.org/0000-0002-6655-1302)
- Jinpeng Li (ORCID: https://orcid.org/0000-0001-9616-2239)
- Shuai Feng (ORCID: https://orcid.org/0009-0002-8025-9313)
- Tongyu Xu (ORCID: https://orcid.org/0000-0001-9284-649X)
- Jinxuan Li (ORCID: https://orcid.org/0000-0002-4595-8568)
Institutions
- Shenyang Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-04-15
- DOI
- https://doi.org/10.3389/fpls.2025.1576212
- Citations
- 4
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 6.87
Funders
- National Science Foundation
- National Natural Science Foundation of China