UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model
- DOI
- 10.3390/plants15182854
- Published
- 2026-09-18
- Container
- Plants
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/plants15182854,
title = {UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model},
author = {Xiao Liang and Qingbo Song and Hongli Lian and Bo Pang and Hongze Zhang and Fuxu Guo and Ying Zang and Yingli Cao},
year = {2026},
journal = {Plants},
doi = {10.3390/plants15182854},
url = {https://doi.org/10.3390/plants15182854}
}RIS
TY - JOUR TI - UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model AU - Xiao Liang AU - Qingbo Song AU - Hongli Lian AU - Bo Pang AU - Hongze Zhang AU - Fuxu Guo AU - Ying Zang AU - Yingli Cao PY - 2026 JO - Plants DO - 10.3390/plants15182854 UR - https://doi.org/10.3390/plants15182854 ER -
APA
Liang, X., Song, Q., Lian, H., Pang, B., Zhang, H., Guo, F., Zang, Y., & Cao, Y. (2026). UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model. Plants. https://doi.org/10.3390/plants15182854
Source records
- crossref · retrieved 2026-11-10T20:07:07.179Z