干旱胁迫下玉米幼苗性状的机器学习分析

IF 3.5 3区 生物学 Q1 BIOLOGY
Lei Zhang, Fulai Zhang, Wentao Du, Mengting Hu, Ying Hao, Shuqi Ding, Huijuan Tian, Dan Zhang
{"title":"干旱胁迫下玉米幼苗性状的机器学习分析","authors":"Lei Zhang, Fulai Zhang, Wentao Du, Mengting Hu, Ying Hao, Shuqi Ding, Huijuan Tian, Dan Zhang","doi":"10.3390/biology14070787","DOIUrl":null,"url":null,"abstract":"<p><p>The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (<i>Zea mays</i> L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods-random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)-were employed to systematically analyze the relevant traits of maize seedlings' drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R<sup>2</sup> = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies.</p>","PeriodicalId":48624,"journal":{"name":"Biology-Basel","volume":"14 7","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Analysis of Maize Seedling Traits Under Drought Stress.\",\"authors\":\"Lei Zhang, Fulai Zhang, Wentao Du, Mengting Hu, Ying Hao, Shuqi Ding, Huijuan Tian, Dan Zhang\",\"doi\":\"10.3390/biology14070787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (<i>Zea mays</i> L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods-random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)-were employed to systematically analyze the relevant traits of maize seedlings' drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R<sup>2</sup> = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies.</p>\",\"PeriodicalId\":48624,\"journal\":{\"name\":\"Biology-Basel\",\"volume\":\"14 7\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biology-Basel\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.3390/biology14070787\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology-Basel","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3390/biology14070787","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

温室气体浓度的增加正在加大全球干旱对作物产量的影响。本研究旨在探讨干旱对玉米幼苗生长的影响。本研究选用78个玉米杂交种,通过盆栽法模拟干旱条件。在一个标准的浇水周期后,玉米幼苗遭受10天的断水期,直到它们达到第三叶箍期(V3)。评估的参数包括株高、茎粗、叶绿素含量和根数。8个表型性状包括地上部分和地下部分的鲜重和干重。采用随机森林(random forest, RF)、k近邻(K-nearest neighbor, KNN)和极端梯度增强(extreme gradient boost, XGBoost)三种机器学习方法,系统分析玉米幼苗抗旱性的相关性状,并评估其预测性能。结果表明,株高、地上重和叶绿素含量是干旱条件下玉米幼苗表型的主要指标。XGBoost模型在分类(AUC = 0.993)和回归(R2 = 0.863)任务中表现最佳,是最有效的预测模型。本研究为选育耐旱玉米品种和完善精准育种策略的可行性和可靠性奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Analysis of Maize Seedling Traits Under Drought Stress.

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods-random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)-were employed to systematically analyze the relevant traits of maize seedlings' drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R2 = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biology-Basel
Biology-Basel Biological Science-Biological Science
CiteScore
5.70
自引率
4.80%
发文量
1618
审稿时长
11 weeks
期刊介绍: Biology (ISSN 2079-7737) is an international, peer-reviewed, quick-refereeing open access journal of Biological Science published by MDPI online. It publishes reviews, research papers and communications in all areas of biology and at the interface of related disciplines. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信