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Updated: Jan 9, 2026

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

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Published on: August 17, 2022

校正:通过将生理特征与SSA-XGBoost算法集成来智能分级甘叶病的严重程度

Xinrui Wang¹, Jihong Sun², Peng Tian¹

¹College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, China.

Frontiers in plant science

|December 4, 2025

概括

这项研究纠正了先前发表的一篇文章的DOI. 校正确保准确引用和检索植物分子生物学和遗传学的研究.

科学领域:

植物科学植物科学
遗传学遗传学是一个
分子生物学分子生物学

背景情况:

准确的引用对于科学完整性和研究可重复性至关重要.
数字物体识别器 (DOI) 对于独特识别和链接到科学出版物至关重要.

研究的目的:

纠正特定物品的数字物体标识符 (DOI).
确保研究成果的适当归因和可访问性.

主要方法:

发出了一份正式的纠正通知.
文章的元数据被更新到正确的DOI.

主要成果:

错误的DOI已经被纠正.
这篇文章现在已经准确地链接,并且可以通过正确的DOI来发现.

结论:

校正符合科学出版的标准.
确保准确的DOI有助于持续的研究和知识传播.

关键词:

疾病严重程度分级疾病严重程度分级.超参数优化超参数优化机器学习分类机器学习分类.生理特征生理特征生理特征甘的叶病甘的叶病

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