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Article(id=1307407767040454887, tenantId=1146029695717560320, journalId=1306273872116547672, issueId=1307407751768986283, articleNumber=null, orderNo=null, doi=10.16819/j.1001-7216.2026.251104, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762358400000, receivedDateStr=2025-11-06, revisedDate=1767801600000, revisedDateStr=2026-01-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1789639902124, onlineDateStr=2026-09-17, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789639902124, onlineIssueDateStr=2026-09-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789639902124, creator=13701087609, updateTime=1789639902124, updator=13701087609, issue=Issue{id=1307407751768986283, tenantId=1146029695717560320, journalId=1306273872116547672, year='2026', volume='40', issue='4', pageStart='425', pageEnd='568', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789639898482, creator='13701087609', updateTime=1790746138035, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312047656814343051, tenantId=1146029695717560320, journalId=1306273872116547672, issueId=1307407751768986283, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312047656814343052, tenantId=1146029695717560320, journalId=1306273872116547672, issueId=1307407751768986283, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=560, endPage=568, ext={EN=ArticleExt(id=1307407767254364392, articleId=1307407767040454887, tenantId=1146029695717560320, journalId=1306273872116547672, language=EN, title=Prediction of Rice Heading Date Based on Panicle Detection and Machine Learning, columnId=1307407385149071973, journalTitle=Chinese Journal of Rice Science, columnName=Research Papers, runingTitle=null, highlight=null, articleAbstract=
【Objective】

To address the problems of high labor intensity and low efficiency in manual measurements of heading date in rice breeding, this study proposes an intelligent prediction method that integrates UAV imagery, the YOLOv10b object detection algorithm, and a random forest regression model to achieve automated and efficient prediction of heading date in rice breeding plots.

【Methods】

Using rice breeding plots as the research subject, UAV imagery was acquired at 15 observation time points from booting to maturity. Based on the YOLOv10b algorithm, a rice panicle detection model was trained to recognize and count panicles in plot images. Subsequently, the numbers of panicles at each observation time point were used as input variables, and the number of days from transplanting to heading was used as the prediction variable to construct and compare multiple heading date prediction models. Based on this, feature importance analysis and dimensionality reduction were performed to develop a simplified model.

【Results】

Experimental results showed that the YOLOv10b model achieved a precision of 91.7%, a recall of 92.1%, and an F1 score of 0.919 in the panicle detection task. In the heading date prediction task, the random forest model constructed using the sequential features of panicle counts from all 15 observation time points achieved an R2 of 0.84, an RMSE of 1.57 days, and an MAE of 1.12 days. Furthermore, after feature dimensionality reduction based on feature importance analysis, a simplified model constructed using 9 observation time points achieved an R2 of 0.83, an RMSE of 1.65 days, and an MAE of 1.16 days. This approach effectively reduced feature dimensionality while maintaining prediction accuracy, thereby improving the model's efficiency and practicality.

【Conclusion】

The proposed method can effectively reduce the workload of manual measurements, improve the efficiency of heading date monitoring, and realize automated prediction of heading date in breeding plots. It provides a feasible data foundation and technical pathway for intelligent breeding and precision agricultural management.

, authors=Qing LIU1, 2, Qing GU2, Yihang ZHU2, Weidong LOU2, Fudeng HUANG3, Ying ZHU4, Xiaobin ZHANG2, authorsList=Qing LIU, Qing GU, Yihang ZHU, Weidong LOU, Fudeng HUANG, Ying ZHU, Xiaobin ZHANG, authorCompany=null, correspAuthors=Xiaobin ZHANG, authorNote=null, correspAuthorsNote=

*

email:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1307407767665406185, articleId=1307407767040454887, tenantId=1146029695717560320, journalId=1306273872116547672, language=CN, title=基于稻穗检测与机器学习的水稻始穗期预测, columnId=1307407385430090343, journalTitle=中国水稻科学, columnName=研究报告, runingTitle=null, highlight=null, articleAbstract=
【目的】

针对当前水稻育种过程中人工测量始穗期存在劳动强度大、效率低等问题，本研究提出一种融合无人机图像、YOLOv10b目标检测算法与随机森林回归模型的智能预测方法，实现水稻育种小区始穗期的自动化高效预测。

【方法】

以水稻育种小区为研究对象，利用无人机在水稻孕穗至成熟阶段获取15个观测期的小区图像，基于YOLOv10b算法训练稻穗检测模型，实现小区图像中稻穗的识别与计数。随后以小区各观测期的稻穗数量为输入变量，始穗期距移栽天数为预测变量，构建多种始穗期预测模型并进行对比分析。在此基础上开展特征重要性分析，进行特征降维，构建精简模型。

【结果】

YOLOv10b模型在稻穗检测任务中精确率为91.7%，召回率为92.1%，F1值为0.919。在始穗期预测任务中，基于全部15个观测期穗数时序特征所构建的随机森林模型取得了R2为0.84，RMSE为1.57 d，MAE为1.12 d的预测表现；进一步通过特征重要性降维至9个观测期所构建的精简模型，取得了R2为0.83，RMSE为1.65 d，MAE为1.16 d的预测表现，在保证预测精度的同时，显著降低了特征维度，提升了模型的效率与实用性。

【结论】

该研究提出的方法可有效减少人工测量的工作量，提升水稻始穗期监测效率，实现育种小区始穗期的自动化预测，为智能育种与精准农业管理提供可行的数据支撑与技术路径。

, authors=刘庆1, 2, 顾清2, 朱怡航2, 娄卫东2, 黄福灯3, 朱英4, 张小斌2, *, *, authorsList=刘庆, 顾清, 朱怡航, 娄卫东, 黄福灯, 朱英, 张小斌, authorCompany=null, correspAuthors=张小斌, authorNote=null, correspAuthorsNote=

*

email:
, copyrightStatement=作者同意论文在本刊发表,论文的数字化复制权、发行权、汇编权及信息网络传播权即转让本刊编辑部。论文的著作权转让费在本刊稿酬中一次性支付。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=UNj94Z5ERNY/+Q+ZRUFo8g==, magXml=G7ZFzyMcpnq0fGwJ+DHDPw==, pdfUrl=null, pdf=QR3RYbyLhHUfPXCfB7gxTA==, pdfFileSize=1481417, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=7fh7MP+7Mh8E2BSke/7uqw==, mapNumber=null, fund=null)}, authors=[Author(id=1307766468708426373, tenantId=1146029695717560320, journalId=1306273872116547672, articleId=1307407767040454887, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1307766471598301832, tenantId=1146029695717560320, journalId=1306273872116547672, articleId=1307407767040454887, authorId=1307766468708426373, language=EN, stringName=Qing LIU, firstName=Qing, middleName=null, lastName=LIU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1School of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
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基于稻穗检测与机器学习的水稻始穗期预测
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刘庆 1, 2 , 顾清 2 , 朱怡航 2 , 娄卫东 2 , 黄福灯 3 , 朱英 4 , 张小斌 2, *, *
中国水稻科学 | 研究报告 2026,40(4): 560-568
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中国水稻科学 |研究报告 2026 , 40 (4) : 560 -568
基于稻穗检测与机器学习的水稻始穗期预测
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刘庆1, 2, 顾清2, 朱怡航2, 娄卫东2, 黄福灯3, 朱英4, 张小斌2, *, *
作者信息
  • 1浙江农林大学 数学与计算机科学学院，杭州 311300
  • 2浙江省农业科学院 数字农业研究所，杭州 310021
  • 3浙江省农业科学院 作物与核利用研究所，杭州 310021
  • 4浙江省农业科学院 病毒学与生物技术研究所，杭州 310021
通讯作者:
Prediction of Rice Heading Date Based on Panicle Detection and Machine Learning
Qing LIU1, 2, Qing GU2, Yihang ZHU2, Weidong LOU2, Fudeng HUANG3, Ying ZHU4, Xiaobin ZHANG2
Affiliations
  • 1School of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
  • 2Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
  • 3Institute of Crop and Nuclear Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
  • 4Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
    • Corresponding Author:

    *

    email:
出版时间: 2026-07-10 doi: 10.16819/j.1001-7216.2026.251104
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摘要
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【目的】

针对当前水稻育种过程中人工测量始穗期存在劳动强度大、效率低等问题，本研究提出一种融合无人机图像、YOLOv10b目标检测算法与随机森林回归模型的智能预测方法，实现水稻育种小区始穗期的自动化高效预测。

【方法】

以水稻育种小区为研究对象，利用无人机在水稻孕穗至成熟阶段获取15个观测期的小区图像，基于YOLOv10b算法训练稻穗检测模型，实现小区图像中稻穗的识别与计数。随后以小区各观测期的稻穗数量为输入变量，始穗期距移栽天数为预测变量，构建多种始穗期预测模型并进行对比分析。在此基础上开展特征重要性分析，进行特征降维，构建精简模型。

【结果】

YOLOv10b模型在稻穗检测任务中精确率为91.7%，召回率为92.1%，F1值为0.919。在始穗期预测任务中，基于全部15个观测期穗数时序特征所构建的随机森林模型取得了R2为0.84，RMSE为1.57 d，MAE为1.12 d的预测表现；进一步通过特征重要性降维至9个观测期所构建的精简模型，取得了R2为0.83，RMSE为1.65 d，MAE为1.16 d的预测表现，在保证预测精度的同时，显著降低了特征维度，提升了模型的效率与实用性。

【结论】

该研究提出的方法可有效减少人工测量的工作量，提升水稻始穗期监测效率，实现育种小区始穗期的自动化预测，为智能育种与精准农业管理提供可行的数据支撑与技术路径。

随机森林  /  水稻始穗期  /  智能化育种  /  目标检测
【Objective】

To address the problems of high labor intensity and low efficiency in manual measurements of heading date in rice breeding, this study proposes an intelligent prediction method that integrates UAV imagery, the YOLOv10b object detection algorithm, and a random forest regression model to achieve automated and efficient prediction of heading date in rice breeding plots.

【Methods】

Using rice breeding plots as the research subject, UAV imagery was acquired at 15 observation time points from booting to maturity. Based on the YOLOv10b algorithm, a rice panicle detection model was trained to recognize and count panicles in plot images. Subsequently, the numbers of panicles at each observation time point were used as input variables, and the number of days from transplanting to heading was used as the prediction variable to construct and compare multiple heading date prediction models. Based on this, feature importance analysis and dimensionality reduction were performed to develop a simplified model.

【Results】

Experimental results showed that the YOLOv10b model achieved a precision of 91.7%, a recall of 92.1%, and an F1 score of 0.919 in the panicle detection task. In the heading date prediction task, the random forest model constructed using the sequential features of panicle counts from all 15 observation time points achieved an R2 of 0.84, an RMSE of 1.57 days, and an MAE of 1.12 days. Furthermore, after feature dimensionality reduction based on feature importance analysis, a simplified model constructed using 9 observation time points achieved an R2 of 0.83, an RMSE of 1.65 days, and an MAE of 1.16 days. This approach effectively reduced feature dimensionality while maintaining prediction accuracy, thereby improving the model's efficiency and practicality.

【Conclusion】

The proposed method can effectively reduce the workload of manual measurements, improve the efficiency of heading date monitoring, and realize automated prediction of heading date in breeding plots. It provides a feasible data foundation and technical pathway for intelligent breeding and precision agricultural management.

random forest  /  rice heading date  /  intelligent breeding  /  object detection
刘庆, 顾清, 朱怡航, 娄卫东, 黄福灯, 朱英, 张小斌. 基于稻穗检测与机器学习的水稻始穗期预测. 中国水稻科学, 2026 , 40 (4) : 560 -568 . DOI: 10.16819/j.1001-7216.2026.251104
Qing LIU, Qing GU, Yihang ZHU, Weidong LOU, Fudeng HUANG, Ying ZHU, Xiaobin ZHANG. Prediction of Rice Heading Date Based on Panicle Detection and Machine Learning[J]. Chinese Journal of Rice Science, 2026 , 40 (4) : 560 -568 . DOI: 10.16819/j.1001-7216.2026.251104
基金
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  • 浙江省“三农九方”科技协作计划项目(2025SNJF012)
  • 浙江省农业科学院重大科技任务攻关项目(ZDZX202500)
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doi: 10.16819/j.1001-7216.2026.251104
  • 接收时间：2025-11-06
  • 首发时间：2026-09-17
  • 出版时间：2026-07-10
补充材料
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  • 收稿日期：2025-11-06
  • 修回日期：2026-01-08
基金
浙江省“三农九方”科技协作计划项目(2025SNJF012)
浙江省农业科学院重大科技任务攻关项目(ZDZX202500)
作者信息
    1浙江农林大学 数学与计算机科学学院，杭州 311300
    2浙江省农业科学院 数字农业研究所，杭州 310021
    3浙江省农业科学院 作物与核利用研究所，杭州 310021
    4浙江省农业科学院 病毒学与生物技术研究所，杭州 310021

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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