A computer vision-based approach for high-throughput automated analysis of Arabidopsis seedling phenotypes

Zhongxiang Wan, Weiji Kong, Yan Tang, Feixiang Ma, Yusi Ji, Yang Peng, Ziqiang Zhu

Open source

DOI: 10.1093/plphys/kiaf275
Published: 2025-06-25
Container: Plant Physiology
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/plphys/kiaf275,
  title = {A computer vision-based approach for high-throughput automated analysis of Arabidopsis seedling phenotypes},
  author = {Zhongxiang Wan and Weiji Kong and Yan Tang and Feixiang Ma and Yusi Ji and Yang Peng and Ziqiang Zhu},
  year = {2025},
  journal = {Plant Physiology},
  doi = {10.1093/plphys/kiaf275},
  url = {https://doi.org/10.1093/plphys/kiaf275}
}

RIS

TY  - JOUR
TI  - A computer vision-based approach for high-throughput automated analysis of Arabidopsis seedling phenotypes
AU  - Zhongxiang Wan
AU  - Weiji Kong
AU  - Yan Tang
AU  - Feixiang Ma
AU  - Yusi Ji
AU  - Yang Peng
AU  - Ziqiang Zhu
PY  - 2025
JO  - Plant Physiology
DO  - 10.1093/plphys/kiaf275
UR  - https://doi.org/10.1093/plphys/kiaf275
ER  -

APA

Wan, Z., Kong, W., Tang, Y., Ma, F., Ji, Y., Peng, Y., & Zhu, Z. (2025). A computer vision-based approach for high-throughput automated analysis of Arabidopsis seedling phenotypes. Plant Physiology. https://doi.org/10.1093/plphys/kiaf275

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crossref · retrieved 2026-09-24T21:03:03.058Z