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CORRECTION article

Front. Plant Sci., 10 July 2025

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | https://doi.org/10.3389/fpls.2025.1648292

Correction: Deep learning-based text generation for plant phenotyping and precision agriculture

Li ZhuLi Zhu1Long Tang*Long Tang2*Shan Ren,Shan Ren3,4
  • 1School of Computer Science, Guangzhou Maritime University, Guangzhou, Guangdong, China
  • 2Hubei University of Economics, Wuhan, China
  • 3Hebei Academy of Fine Arts, Shijiazhuang, Hebei, China
  • 4Hanyang University, Ansan-si, Gyeonggi-do, Republic of Korea

A Correction on
Deep learning-based text generation for plant phenotyping and precision agriculture

By Zhu L, Tang L and Ren S (2025). Front. Plant Sci. 16:1564394. doi: 10.3389/fpls.2025.1564394

Author “Shan Ren” was assigned as corresponding author. The correct corresponding author is “Long Tang”.

The original version of this article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: plant phenotyping, deep learning, generative model, biologically-constrained optimization, precision agriculture

Citation: Zhu L, Tang L and Ren S (2025) Correction: Deep learning-based text generation for plant phenotyping and precision agriculture. Front. Plant Sci. 16:1648292. doi: 10.3389/fpls.2025.1648292

Received: 17 June 2025; Accepted: 27 June 2025;
Published: 10 July 2025.

Edited and Reviewed by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Zhu, Tang and Ren. 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: Long Tang, bWxobTU5QDE2My5jb20=

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.