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CORRECTION article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Correction: Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm

Provisionally accepted
Wang XinruiWang Xinrui1Sun JihongSun Jihong2Tian PengTian Peng1Wu MengyaoWu Mengyao1,3Zhao JiaweiZhao Jiawei1Chen JiangquanChen Jiangquan1Qian YeQian Ye1*Wang CanyuWang Canyu1*
  • 1College of Big Data, Yunnan Agricultural University, Kunming, China
  • 2College of Information Engineering, Kunming University, Kunming, China
  • 3Qujing Tobacco Company Shizong Branch, QuJing, China

This article is a correction to:

  1. Plant disease detection model for edge computing devices

    1. Read original article

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• Please read through all the templates before choosing • Pick the most relevant text template(s) from the following page and delete all others. • Edit the text as necessary, ensuring that the original incorrect text is included for the record, please see the below. • Please do not use any extra formatting when editing the templates, and only modify the red text unless absolutely necessary • Submit to Frontiers following the instructions on this page.When the original text contained incorrect information, to preserve the scientific record, please include that text when editing the below templates. For example:There was a mistake in the Funding statement, an incorrect number was used. The correct number is "2015C03Bd051.". The publisher apologizes for this mistake.The original version of this article has been updated. Xinrui Wang 1 , Jihong Sun 2 , Peng Tian 1 , Mengyao Wu 1,3 , Jiawei Zhao 1 , Jiangquan Chen 1 , Ye Qian

Keywords: Sugarcane leaf diseases, Disease severity grading, Physiological traits, Machine learning classification, Hyperparameter optimization

Received: 17 Oct 2025; Accepted: 24 Oct 2025.

Copyright: © 2025 Xinrui, Jihong, Peng, Mengyao, Jiawei, Jiangquan, Ye and Canyu. 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) or licensor 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:
Qian Ye, 2014014@ynau.edu.cn
Wang Canyu, 2001027@ynau.edu.cn

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.

Supplementary Material