A Correction on
Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation
By Wang H, Che X, Nan J, Miao Y, Wang Y, Zhang W, Li F and Han J (2025) Front. Plant Sci. 16:1604088. doi: 10.3389/fpls.2025.1604088
An incorrect Funding statement was provided. The original Funding statement stated, “the National Natural Science Foundation of China (NSFC)(U21A20216) and Shanxi Key Project (2022ZDYF108)”. The correct funder is “China Agriculture Research System of MOF and MARA(CARS-07); the grand science and technology special project in Shanxi Province (202101140601027)”. The correct Funding statement reads:
“The research was funded by China Agriculture Research System of MOF and MARA(CARS-07), and the grand science and technology special project in Shanxi Province (202101140601027)”.
In the Acknowledgements, the contributions of Mingyang Zhang, Dake Guo, and Zhaoxia Sun to this research were erroneously omitted. The original Acknowledgements section only stated: “We acknowledge the valuable contribution of staff and students of Shanxi Agricultural University for collecting the field samples.” The correct Acknowledgements section reads, “We acknowledge the valuable contribution of staff and students of Shanxi Agricultural University for collecting the field samples. We also express our sincere gratitude to Mr. Mingyang Zhang and Mr. Dake Guo for their participation in buckwheat sample collection and hyperspectral data acquisition, which laid a solid foundation for the experimental data of this study; we thank Prof. Zhaoxia Sun for her contributions to the optimization of research methodology and critical revision of the manuscript, which improved the scientific rigor and clarity of the work”.
The original version of this article has been updated.
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Keywords: buckwheat, spectroscopy, machine learning, generative adversarial networks, NIR, precision agriculture
Citation: Wang H, Che X, Nan J, Miao Y, Wang Y, Zhang W, Li F and Han J (2025) Correction: Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation. Front. Plant Sci. 16:1757122. doi: 10.3389/fpls.2025.1757122
Received: 29 November 2025; Accepted: 05 December 2025; Revised: 04 December 2025;
Published: 18 December 2025.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2025 Wang, Che, Nan, Miao, Wang, Zhang, Li and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Fuzhong Li, bGlmdXpob25nQHN4YXUuZWR1LmNu; Jiwan Han, aGFuaml3YW5Ac3hhdS5lZHUuY24=
Xiaoxue Che