Image-based quantitative root phenotyping enables reliable origin classification of wild-simulated ginseng
- DOI
- 10.1016/j.jafr.2026.103110
- Published
- 2026-07
- Container
- Journal of Agriculture and Food Research
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jafr.2026.103110,
title = {Image-based quantitative root phenotyping enables reliable origin classification of wild-simulated ginseng},
author = {Do-Sin Lee and Yeong-Bae Yun and Jun-Seo Yi and Dong-Young Kim and Sung-Hwan Jo and Yurry Um},
year = {2026},
journal = {Journal of Agriculture and Food Research},
doi = {10.1016/j.jafr.2026.103110},
url = {https://doi.org/10.1016/j.jafr.2026.103110}
}RIS
TY - JOUR TI - Image-based quantitative root phenotyping enables reliable origin classification of wild-simulated ginseng AU - Do-Sin Lee AU - Yeong-Bae Yun AU - Jun-Seo Yi AU - Dong-Young Kim AU - Sung-Hwan Jo AU - Yurry Um PY - 2026 JO - Journal of Agriculture and Food Research DO - 10.1016/j.jafr.2026.103110 UR - https://doi.org/10.1016/j.jafr.2026.103110 ER -
APA
Lee, D., Yun, Y., Yi, J., Kim, D., Jo, S., & Um, Y. (2026). Image-based quantitative root phenotyping enables reliable origin classification of wild-simulated ginseng. Journal of Agriculture and Food Research. https://doi.org/10.1016/j.jafr.2026.103110
Source records
- crossref · retrieved 2026-09-26T12:55:12.073Z