Estimation of winter wheat <scp>LAI</scp> based on color indices and texture features of <scp>RGB</scp> images taken by <scp>UAV</scp>
This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer-level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.
Authors
- Hao Li (ORCID: https://orcid.org/0009-0000-2663-4915)
- Wude Yang
- Fahad Shafiq (ORCID: https://orcid.org/0000-0003-2183-2714)
- Pengyan Su
- Xingxing Qiao (ORCID: https://orcid.org/0000-0003-1720-263X)
- Yiming Su
- Xiaobin Yan
- Chunrui Gao
- Meichen Feng (ORCID: https://orcid.org/0000-0002-0338-7963)
- Chao Wang
- Yu Zhao (ORCID: https://orcid.org/0009-0004-3500-6063)
- Junfeng Li (ORCID: https://orcid.org/0000-0003-4385-632X)
- Zixin Xu (ORCID: https://orcid.org/0000-0002-9421-0038)
- Lujie Xiao
Institutions
- Shanxi Agricultural University (CN)
- Government College University, Lahore (PK)
Publication Details
- Journal
- Journal of the Science of Food and Agriculture
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1002/jsfa.13817
- Citations
- 17
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 7.80
Funders
- National Natural Science Foundation of China