YOLO-ACBG: an enhanced deep learning model for precision monitoring of wheat stripe rust using different vegetation indices

Fusheng Yu, Xuanyuan Tang, Qi Liu, Muzaipaer Maimaiti, Jing Chen

Open source

DOI: 10.3389/fpls.2026.1849651
Published: 2026-06-30
Container: Frontiers in Plant Science
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/fpls.2026.1849651,
  title = {YOLO-ACBG: an enhanced deep learning model for precision monitoring of wheat stripe rust using different vegetation indices},
  author = {Fusheng Yu and Xuanyuan Tang and Qi Liu and Muzaipaer Maimaiti and Jing Chen},
  year = {2026},
  journal = {Frontiers in Plant Science},
  doi = {10.3389/fpls.2026.1849651},
  url = {https://doi.org/10.3389/fpls.2026.1849651}
}

RIS

TY  - JOUR
TI  - YOLO-ACBG: an enhanced deep learning model for precision monitoring of wheat stripe rust using different vegetation indices
AU  - Fusheng Yu
AU  - Xuanyuan Tang
AU  - Qi Liu
AU  - Muzaipaer Maimaiti
AU  - Jing Chen
PY  - 2026
JO  - Frontiers in Plant Science
DO  - 10.3389/fpls.2026.1849651
UR  - https://doi.org/10.3389/fpls.2026.1849651
ER  -

APA

Yu, F., Tang, X., Liu, Q., Maimaiti, M., & Chen, J. (2026). YOLO-ACBG: an enhanced deep learning model for precision monitoring of wheat stripe rust using different vegetation indices. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2026.1849651

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crossref · retrieved 2026-09-25T23:31:33.781Z