A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle
The experimental results suggest that the new BWO-based vegetation index has a higher feature expression ability than single indices, and the new BWO-based ensemble strategy can yield more accurate detection results. This approach provides an effective means for low-cost pests and diseases detection of CCB trees.
Authors
- Zhihui Wang (ORCID: https://orcid.org/0000-0001-8140-1882)
- Keliang Hu
- Junchen Liu (ORCID: https://orcid.org/0000-0003-2765-9780)
- Man Li (ORCID: https://orcid.org/0009-0003-3431-6134)
- Jun Liu (ORCID: https://orcid.org/0000-0001-8697-3089)
- Lei Zhang (ORCID: https://orcid.org/0000-0003-4611-3417)
- Qiangguo Zeng
- Hai Xiao (ORCID: https://orcid.org/0009-0004-0997-0422)
Institutions
- Hunan University of Traditional Chinese Medicine (CN)
- Nanjing Surveying and Mapping Research Institute (China) (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2024-12-11
- DOI
- https://doi.org/10.3389/fpls.2024.1464723
- Citations
- 3
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.38
Funders
- National Natural Science Foundation of China
- Hunan University
- Natural Science Foundation of Hunan Province
- Hunan University of Chinese Medicine