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Article(id=1325193038393377244, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325192975050997820, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2025.02.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716048000000, receivedDateStr=2024-05-19, revisedDate=1728489600000, revisedDateStr=2024-10-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880241248, onlineDateStr=2026-11-05, pubDate=1747238400000, pubDateStr=2025-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880241248, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880241248, creator=13701087609, updateTime=1793880241248, updator=13701087609, issue=Issue{id=1325192975050997820, tenantId=1146029695717560320, journalId=1319690209941618849, year='2025', volume='6', issue='2', pageStart='1', pageEnd='110', issueExtLink='null', onlineDate='null', pubDate='1747238400000', pubDateStr='2025-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880226147, creator='13701087609', updateTime=1793943655676, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325459019401397052, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325192975050997820, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325459019401397053, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325192975050997820, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=97, endPage=104, ext={EN=ArticleExt(id=1325193038556955101, articleId=1325193038393377244, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to ensure national food security and promote sustainable agricultural development, it is crucial to accurately predict regional rice yield by formulating effective food strategies. While advancemnets have been achieved by integrating remote sensing data with machine learning technology for yield prediction, a more in-depth analysis of the machine learning model mechanism and the application of high spatio-temporal resolution data remains necessary. This study utilized Sentinel and MODIS Normalized Difference Vegetation Index (NDVI) data along with county-level yield statistics from 2023 to implement a Random Forest (RF) model for predicting yield from county level to pixel level with case study of Sheyang unmanned farm in Jiangsu Province, and investigated the impact of different features on the model's learning mechanism. The results demonstrated that there was a significant correlation between NDVI data and rice yield during the period of August to October 2023, which is a critical period in the crop phenological cycle. The RF model effectively predicted rice yield at the county level, with a Root Mean Square Error (RMSE) value of 339.5 kg /hm2. Furthermore, spatial distribution mapping of the predicted yields within Sheyang County for 2023 revealed significant heterogeneity, indicating lower yields in marginal regions and higher yields in central areas ranging from 9 000 to 9 300 kg/hm2. This study not only enhances understanding of machine learning application and vegetation data in yield prediction but also provides theoretical support for improving model accuracy and formulating scientific food production strategies.

, authors=Jun WANG1, Zhenwei WU1, Hai JIANG1, Juan KE1, Ahmed Prodhan FOYEZ2, authorsList=Jun WANG, Zhenwei WU, Hai JIANG, Juan KE, Ahmed Prodhan FOYEZ, authorCompany=null, correspAuthors=null, authorNote=

WANG Jun, E-mail:

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为确保国家粮食安全、制定有效的粮食策略以及促进农业的可持续发展，对区域内水稻产量进行及时且准确的预测至关重要。尽管利用遥感数据结合机器学习技术进行产量预测已有一定进展，但现有研究在深入解析机器学习模型机理及高时空分辨率数据的应用方面仍显不足。本研究基于2023年的Sentinel和MODIS NDVI数据及县级产量信息，应用随机森林模型从县级扩展至像素级，以江苏射阳无人农场为例对水稻产量进行预测，并探讨不同特征对模型学习机理的影响。研究结果表明：（1）2023年8—10月的NDVI数据与产量之间存在显著相关性，此阶段为作物生长周期中的关键时期，植被覆盖和生长状态在此期间对产量的预测具有重要作用。随机森林模型能够有效预测县级水稻产量，其中RMSE值为339.5 kg/hm2。（2）此外，分析2023年射阳农场水稻产量空间分布图发现，水稻产量在该区域呈现显著的空间异质性，边缘地区产量较低，中心区域产量较高，产量主要集中在9 000~9 300 kg/hm2。其研究成果不仅加深了对机器学习及植被数据在产量预测中的应用理解，也为提高模型准确性及制定科学的粮食生产策略提供了理论支持。

, authors=王俊1, 吴振伟1, 姜海1, 柯娟1, Ahmed Prodhan FOYEZ2, authorsList=王俊, 吴振伟, 姜海, 柯娟, Ahmed Prodhan FOYEZ, authorCompany=null, correspAuthors=null, authorNote=

王俊，男，1987年生，江苏常州人，硕士，工程师；研究方向农业信息化、农业物联网。E-mail：

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王俊，男，1987年生，江苏常州人，硕士，工程师；研究方向农业信息化、农业物联网。E-mail：

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王俊，男，1987年生，江苏常州人，硕士，工程师；研究方向农业信息化、农业物联网。E-mail：

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基于随机森林及遥感植被指数的无人农场水稻产量预测研究
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王俊 1 , 吴振伟 1 , 姜海 1 , 柯娟 1 , Ahmed Prodhan FOYEZ 2
智能化农业装备学报（中英文） | 2025,6(2): 97-104
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智能化农业装备学报（中英文） | 2025 , 6 (2) : 97 -104
基于随机森林及遥感植被指数的无人农场水稻产量预测研究
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王俊1 , 吴振伟1, 姜海1, 柯娟1, Ahmed Prodhan FOYEZ2
作者信息
  • 1.江苏立卓信息技术有限公司，江苏 常州，213000
  • 2.班加班杜谢赫·穆吉布尔·拉赫曼农业大学，孟加拉国 加济布尔，1706，
作者简介:

王俊，男，1987年生，江苏常州人，硕士，工程师；研究方向农业信息化、农业物联网。E-mail：

Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index
Jun WANG1 , Zhenwei WU1, Hai JIANG1, Juan KE1, Ahmed Prodhan FOYEZ2
Affiliations
  • 1.Jiangsu Lizhuo Information Technology Co., Ltd., Changzhou 213000, China
  • 2.Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh

    • WANG Jun, E-mail:

出版时间: 2025-05-15 doi: 10.12398/j.issn.2096-7217.2025.02.009
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为确保国家粮食安全、制定有效的粮食策略以及促进农业的可持续发展，对区域内水稻产量进行及时且准确的预测至关重要。尽管利用遥感数据结合机器学习技术进行产量预测已有一定进展，但现有研究在深入解析机器学习模型机理及高时空分辨率数据的应用方面仍显不足。本研究基于2023年的Sentinel和MODIS NDVI数据及县级产量信息，应用随机森林模型从县级扩展至像素级，以江苏射阳无人农场为例对水稻产量进行预测，并探讨不同特征对模型学习机理的影响。研究结果表明：（1）2023年8—10月的NDVI数据与产量之间存在显著相关性，此阶段为作物生长周期中的关键时期，植被覆盖和生长状态在此期间对产量的预测具有重要作用。随机森林模型能够有效预测县级水稻产量，其中RMSE值为339.5 kg/hm2。（2）此外，分析2023年射阳农场水稻产量空间分布图发现，水稻产量在该区域呈现显著的空间异质性，边缘地区产量较低，中心区域产量较高，产量主要集中在9 000~9 300 kg/hm2。其研究成果不仅加深了对机器学习及植被数据在产量预测中的应用理解，也为提高模型准确性及制定科学的粮食生产策略提供了理论支持。

随机森林  /  机器学习  /  遥感  /  植被指数  /  产量预测  /  生育期

In order to ensure national food security and promote sustainable agricultural development, it is crucial to accurately predict regional rice yield by formulating effective food strategies. While advancemnets have been achieved by integrating remote sensing data with machine learning technology for yield prediction, a more in-depth analysis of the machine learning model mechanism and the application of high spatio-temporal resolution data remains necessary. This study utilized Sentinel and MODIS Normalized Difference Vegetation Index (NDVI) data along with county-level yield statistics from 2023 to implement a Random Forest (RF) model for predicting yield from county level to pixel level with case study of Sheyang unmanned farm in Jiangsu Province, and investigated the impact of different features on the model's learning mechanism. The results demonstrated that there was a significant correlation between NDVI data and rice yield during the period of August to October 2023, which is a critical period in the crop phenological cycle. The RF model effectively predicted rice yield at the county level, with a Root Mean Square Error (RMSE) value of 339.5 kg /hm2. Furthermore, spatial distribution mapping of the predicted yields within Sheyang County for 2023 revealed significant heterogeneity, indicating lower yields in marginal regions and higher yields in central areas ranging from 9 000 to 9 300 kg/hm2. This study not only enhances understanding of machine learning application and vegetation data in yield prediction but also provides theoretical support for improving model accuracy and formulating scientific food production strategies.

Random Forest  /  machine learning  /  remote sensing  /  vegetation index  /  yield forecast  /  growth stage
王俊, 吴振伟, 姜海, 柯娟, Ahmed Prodhan FOYEZ. 基于随机森林及遥感植被指数的无人农场水稻产量预测研究. 智能化农业装备学报（中英文）, 2025 , 6 (2) : 97 -104 . DOI: 10.12398/j.issn.2096-7217.2025.02.009
Jun WANG, Zhenwei WU, Hai JIANG, Juan KE, Ahmed Prodhan FOYEZ. Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index[J]. Journal of Intelligent Agricultural Mechanization, 2025 , 6 (2) : 97 -104 . DOI: 10.12398/j.issn.2096-7217.2025.02.009
基金
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  • 国家自然科学基金(42071425)
  • 江苏常州钟楼区揭榜挂帅项目(JBGS2023011)
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2025年第6卷第2期
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doi: 10.12398/j.issn.2096-7217.2025.02.009
  • 接收时间：2024-05-19
  • 首发时间：2026-11-05
  • 出版时间：2025-05-15
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  • 收稿日期：2024-05-19
  • 修回日期：2024-10-10
基金
National Natural Science Foundation of China(42071425)
国家自然科学基金(42071425)
Jiangsu Changzhou Zhonglou District Challenge-Response System Project(JBGS2023011)
江苏常州钟楼区揭榜挂帅项目(JBGS2023011)
作者信息
    1.江苏立卓信息技术有限公司，江苏 常州，213000
    2.班加班杜谢赫·穆吉布尔·拉赫曼农业大学，孟加拉国 加济布尔，1706，
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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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