UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model.
- DOI
- 10.3390/plants15182854
- Published
- 2026 Sep 18
- Container
- Plants (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- unknown
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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 = {Liang X and Song Q and Lian H and Pang B and Zhang H and Guo F and Zang Y and Cao Y},
year = {2026},
journal = {Plants (Basel, Switzerland)},
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 - Liang X AU - Song Q AU - Lian H AU - Pang B AU - Zhang H AU - Guo F AU - Zang Y AU - Cao Y PY - 2026 JO - Plants (Basel, Switzerland) DO - 10.3390/plants15182854 UR - https://doi.org/10.3390/plants15182854 ER -
APA
X, L., Q, S., H, L., B, P., H, Z., F, G., Y, Z., & Y, C. (2026). UAV Multispectral Remote Sensing for Rice Leaf Blast Severity Grading Using an Improved 1DCNN-Transformer Ensemble Model.. Plants (Basel, Switzerland). https://doi.org/10.3390/plants15182854
Source records
- pubmed · retrieved 2026-09-26T14:09:37.601Z