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Article(id=1311625834771873907, tenantId=1146029695717560320, journalId=1306273872116547672, issueId=1311625833022845462, articleNumber=null, orderNo=null, doi=10.16819/j.1001-7216.2026.250703, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752076800000, receivedDateStr=2025-07-10, revisedDate=1757347200000, revisedDateStr=2025-09-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1790645567838, onlineDateStr=2026-09-29, pubDate=1788969600000, pubDateStr=2026-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790645567838, onlineIssueDateStr=2026-09-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790645567838, creator=13701087609, updateTime=1790645567838, updator=13701087609, issue=Issue{id=1311625833022845462, tenantId=1146029695717560320, journalId=1306273872116547672, year='2026', volume='40', issue='5', pageStart='569', pageEnd='704', issueExtLink='null', onlineDate='null', pubDate='1788969600000', pubDateStr='2026-09-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790645567421, creator='13701087609', updateTime=1790746157512, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312047738519384984, tenantId=1146029695717560320, journalId=1306273872116547672, issueId=1311625833022845462, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312047738519384985, tenantId=1146029695717560320, journalId=1306273872116547672, issueId=1311625833022845462, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=663, endPage=678, ext={EN=ArticleExt(id=1311625835266801780, articleId=1311625834771873907, tenantId=1146029695717560320, journalId=1306273872116547672, language=EN, title=Intelligent Recognition Method of Rice Growth Stage Based on Multimodal Fusion, columnId=1307407385149071973, journalTitle=Chinese Journal of Rice Science, columnName=Research Papers, runingTitle=null, highlight=null, articleAbstract=
Objective

The growth period of rice is an important reference indicator for irrigation, fertilization, and pest control measures in rice planting management. Researching an intelligent recognition method for rice growth period based on multimodal fusion, improving the accuracy of rice growth period recognition, is of great significance for optimizing rice planting management measures and increasing rice yield.

Method

To address the vulnerability of RGB images to lighting, an H-color channel image is extracted, and a dual-branch rice growth stage image recognition model based on residual network and attention mechanism is established; Due to the influence of meteorological factors on the length of rice growth period, a rice growth period meteorological identification model based on three meteorological factors and a bidirectional long-short period network is established; Using decision trees for decision-level multimodal feature fusion, multiple models were trained and tested on a self-built rice growth period dataset.

Result

The improved rice growth period image recognition model achieved an accuracy of 84.25%, which is superior to traditional image recognition models such as ResNet50 and ConvNeXt. The multi-modality model based on images and meteorological achieved an accuracy of 87.66% in identifying 9 rice growth stages, which is superior to the single modality recognition model based on images and meteorology.

Conclusion

The multimodal recognition model based on image and meteorological fusion compensates for the shortcomings of single modal recognition methods. Compared with single modal recognition models, it has significantly improved the accuracy of identifying 9 rice growth stages.

, authors=Yongwei XU1, Shuhua LIU2, Zelin FENG1, 3, Ju LUO2, Zhaoxin NI1, Baojun YANG2, Qing YAO1, Agen LI4, *, authorsList=Yongwei XU, Shuhua LIU, Zelin FENG, Ju LUO, Zhaoxin NI, Baojun YANG, Qing YAO, Agen LI, authorCompany=null, correspAuthors=Qing YAO, Agen LI, 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=1311625835673649269, articleId=1311625834771873907, tenantId=1146029695717560320, journalId=1306273872116547672, language=CN, title=基于多模态融合的水稻生育期智能识别方法, columnId=1307407385430090343, journalTitle=中国水稻科学, columnName=研究报告, runingTitle=null, highlight=null, articleAbstract=
目的

水稻生育期是水稻种植管理过程中灌溉、施肥和病虫害防控措施的重要参考指标。研究基于多模态融合的水稻生育期智能识别方法，提高水稻生育期识别准确率，对优化水稻种植管理措施，提升水稻产量具有重要意义。

方法

针对RGB图像易受光照影响，提取H颜色通道图像，建立基于残差网络与注意力机制的双分支水稻生育期图像识别模型；由于气象因子影响水稻生育期长短，建立基于3个气象因子和双向长短期记忆网络的水稻生育期气象识别模型；利用决策树进行决策级多模态特征融合，在自建数据集上进行多种模型的训练和测试。

结果

改进的水稻生育期图像识别模型准确率达到84.25%，优于ResNet50、ConvNeXt等传统图像识别模型。基于图像和气象的多模态模型对9个水稻生育期识别准确率达到87.66%，优于单一图像和气象的单模态识别模型。

结论

基于图像与气象融合的多模态识别模型弥补了单模态识别方法的不足，相较于单模态识别模型，显著提升了对9个水稻生育期的识别准确率。

, authors=许永炜1, 刘淑华2, 冯泽霖1, 3, 罗举2, 倪兆新1, 杨保军2, 姚青1, *, *, 李阿根4, *, authorsList=许永炜, 刘淑华, 冯泽霖, 罗举, 倪兆新, 杨保军, 姚青, 李阿根, authorCompany=null, correspAuthors=姚青, 李阿根, authorNote=null, correspAuthorsNote=

*

email:
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基于多模态融合的水稻生育期智能识别方法
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许永炜 1 , 刘淑华 2 , 冯泽霖 1, 3 , 罗举 2 , 倪兆新 1 , 杨保军 2 , 姚青 1, *, * , 李阿根 4, *
中国水稻科学 | 研究报告 2026,40(5): 663-678
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中国水稻科学 |研究报告 2026 , 40 (5) : 663 -678
基于多模态融合的水稻生育期智能识别方法
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许永炜1, 刘淑华2, 冯泽霖1, 3, 罗举2, 倪兆新1, 杨保军2, 姚青1, *, * , 李阿根4, *
作者信息
  • 1浙江理工大学 计算机科学与技术学院，杭州 310018
  • 2中国水稻研究所，杭州 311401
  • 3浙江理工大学 信息与控制学院，杭州 312369
  • 4余杭区农业生态与植物保护服务站，杭州 311100
通讯作者:
Intelligent Recognition Method of Rice Growth Stage Based on Multimodal Fusion
Yongwei XU1, Shuhua LIU2, Zelin FENG1, 3, Ju LUO2, Zhaoxin NI1, Baojun YANG2, Qing YAO1 , Agen LI4, *
Affiliations
  • 1College of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
  • 2China National Rice Research Institute, Hangzhou 311401, China
  • 3School of Information and Control, Zhejiang Sci-Tech University, Hangzhou 312369, China
  • 4Yuhang Agro-ecological Environment & Crop Protection Service Station, Hangzhou 311100, China
    • Corresponding Author:

    *

    email:
出版时间: 2026-09-10 doi: 10.16819/j.1001-7216.2026.250703
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水稻生育期是水稻种植管理过程中灌溉、施肥和病虫害防控措施的重要参考指标。研究基于多模态融合的水稻生育期智能识别方法，提高水稻生育期识别准确率，对优化水稻种植管理措施，提升水稻产量具有重要意义。

方法

针对RGB图像易受光照影响，提取H颜色通道图像，建立基于残差网络与注意力机制的双分支水稻生育期图像识别模型；由于气象因子影响水稻生育期长短，建立基于3个气象因子和双向长短期记忆网络的水稻生育期气象识别模型；利用决策树进行决策级多模态特征融合，在自建数据集上进行多种模型的训练和测试。

结果

改进的水稻生育期图像识别模型准确率达到84.25%，优于ResNet50、ConvNeXt等传统图像识别模型。基于图像和气象的多模态模型对9个水稻生育期识别准确率达到87.66%，优于单一图像和气象的单模态识别模型。

结论

基于图像与气象融合的多模态识别模型弥补了单模态识别方法的不足，相较于单模态识别模型，显著提升了对9个水稻生育期的识别准确率。

水稻生育期  /  多模态融合  /  图像模态  /  气象模态  /  决策树
Objective

The growth period of rice is an important reference indicator for irrigation, fertilization, and pest control measures in rice planting management. Researching an intelligent recognition method for rice growth period based on multimodal fusion, improving the accuracy of rice growth period recognition, is of great significance for optimizing rice planting management measures and increasing rice yield.

Method

To address the vulnerability of RGB images to lighting, an H-color channel image is extracted, and a dual-branch rice growth stage image recognition model based on residual network and attention mechanism is established; Due to the influence of meteorological factors on the length of rice growth period, a rice growth period meteorological identification model based on three meteorological factors and a bidirectional long-short period network is established; Using decision trees for decision-level multimodal feature fusion, multiple models were trained and tested on a self-built rice growth period dataset.

Result

The improved rice growth period image recognition model achieved an accuracy of 84.25%, which is superior to traditional image recognition models such as ResNet50 and ConvNeXt. The multi-modality model based on images and meteorological achieved an accuracy of 87.66% in identifying 9 rice growth stages, which is superior to the single modality recognition model based on images and meteorology.

Conclusion

The multimodal recognition model based on image and meteorological fusion compensates for the shortcomings of single modal recognition methods. Compared with single modal recognition models, it has significantly improved the accuracy of identifying 9 rice growth stages.

rice growth period  /  multimodal  /  image modality  /  meteorological modality  /  decision tree
许永炜, 刘淑华, 冯泽霖, 罗举, 倪兆新, 杨保军, 姚青, 李阿根. 基于多模态融合的水稻生育期智能识别方法. 中国水稻科学, 2026 , 40 (5) : 663 -678 . DOI: 10.16819/j.1001-7216.2026.250703
Yongwei XU, Shuhua LIU, Zelin FENG, Ju LUO, Zhaoxin NI, Baojun YANG, Qing YAO, Agen LI. Intelligent Recognition Method of Rice Growth Stage Based on Multimodal Fusion[J]. Chinese Journal of Rice Science, 2026 , 40 (5) : 663 -678 . DOI: 10.16819/j.1001-7216.2026.250703
基金
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  • 浙江省“三农九方”科技协作计划项目(2024SNJF010)
  • 浙江省农业重大技术协同推广计划项目(2023ZDXT01-5)
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2026年第40卷第5期
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doi: 10.16819/j.1001-7216.2026.250703
  • 接收时间：2025-07-10
  • 首发时间：2026-09-29
  • 出版时间：2026-09-10
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  • 收稿日期：2025-07-10
  • 修回日期：2025-09-09
基金
浙江省“三农九方”科技协作计划项目(2024SNJF010)
浙江省农业重大技术协同推广计划项目(2023ZDXT01-5)
作者信息
    1浙江理工大学 计算机科学与技术学院，杭州 310018
    2中国水稻研究所，杭州 311401
    3浙江理工大学 信息与控制学院，杭州 312369
    4余杭区农业生态与植物保护服务站，杭州 311100

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2种不同金属材料的力学参数
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Number of
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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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