Article(id=1305136092932493523, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305136047835341416, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2025.02.14, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1705248000000, receivedDateStr=2024-01-15, revisedDate=1709136000000, revisedDateStr=2024-02-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1789098292811, onlineDateStr=2026-09-11, pubDate=1739548800000, pubDateStr=2025-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789098292811, onlineIssueDateStr=2026-09-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789098292811, creator=13701087609, updateTime=1789098292811, updator=13701087609, issue=Issue{id=1305136047835341416, tenantId=1146029695717560320, journalId=1303687221787852879, year='2025', volume='45', issue='2', pageStart='F2', pageEnd='278', issueExtLink='null', onlineDate='null', pubDate='1739548800000', pubDateStr='2025-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789098282059, creator='13701087609', updateTime=1789103393231, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305157485724197250, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305136047835341416, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305157485728391555, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305136047835341416, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=264, endPage=274, ext={EN=ArticleExt(id=1305136093184151764, articleId=1305136092932493523, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Research on Wheat Yield Prediction Based on UAV Imagery and SHAP Feature Selection, columnId=1305133133406433355, journalTitle=Journal of Triticeae Crops, columnName=Physiology, Ecology and Cultivation, runingTitle=null, highlight=null, articleAbstract=

Accurate and effective yield prediction is essential for wheat breeding, cultivation and field management. In this study, the multispectral and RGB images of winter wheat during the grain filling stage were collected from UAV, and 14 spectral traits and 28 morphological traits were extracted as feature variables. Ten machine learning methods, including linear regression, random forest and neural network, were used to construct wheat yield prediction models, and the differences between the models were compared to select the best one. Additionally, machine learning interpretability method SHAP was introduced to analyze the importance of the feature variables, in order to improve the prediction performance of the model. The results showed that among the 10 machine learning methods used, the BPNN model had the best prediction performance (r2=0.826, RMSE=0.094 t·hm-2). According to the feature importance ranking determined by SHAP, Anthocyanin Reflectance Index (ARI) and Three-Dimensional Canopy Volume (Volume) had the greatest impact on the prediction results, accounting for 45.48% of the total feature importance. After feature selection using SHAP, the BPNN model with the best performance was determined based on nine feature variables (r2=0.865, RMSE=0.075 t·hm-2). This improved the prediction accuracy compared to the BPNN model using all features and the pre-analysis method Pearson correlation analysis. Therefore, based on the optimal yield prediction model, SHAP mechanism can be used to select and analyze the importance of feature variables, so as to further improve the accuracy of wheat yield prediction.

, authors=Zhichang ZHU1, Yan GE1, 2, Jingrong ZANG3, Qing LI3, Shichao JIN4, Huanliang XU1, Zhaoyu ZHAI1, authorsList=Zhichang ZHU, Yan GE, Jingrong ZANG, Qing LI, Shichao JIN, Huanliang XU, Zhaoyu ZHAI, authorCompany=null, correspAuthors=Zhaoyu ZHAI, authorNote=null, correspAuthorsNote=null, 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=1305136093435810005, articleId=1305136092932493523, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=基于无人机图像和SHAP特征筛选的小麦田间产量预测方法研究, columnId=1305133133590982733, journalTitle=麦类作物学报, columnName=生理、生态与栽培, runingTitle=null, highlight=null, articleAbstract=

为了探寻适宜的小麦产量预测模型并提高其精度,从冬小麦灌浆期的无人机多光谱和RGB图像中提取了14种光谱参数和28种形态参数作为特征变量,利用线性回归、随机森林、神经网络等10种机器学习方法构建小麦田间产量预测模型,并比较了模型间预测能力的差异;同时,引入机器学习事后可解释性方法SHAP对输入的特征变量进行重要性分析和筛选,了解其提高模型预测能力的效果。结果表明:(1)10种机器学习模型中,误差逆传播神经网络BPNN的产量预测表现最好(r2=0.826, RMSE=0.094 t·hm-2);(2)根据SHAP确定的特征变量重要性排序,花青素反射指数ARI和三维冠层体积Volume对于预测结果的影响最大,占全部特征重要性总和的45.48%;(3)经过SHAP特征筛选后,确定了在BPNN产量预测模型上表现最优的9个特征变量,其预测结果r2为0.865,RMSE为0.075 t·hm-2, 比使用全特征的BPNN和事前Pearson相关性分析方法在预测精度上均有提升。因此,在优选产量预测模型基础上,可采用SHAP机制对特征变量的重要性进行筛选和分析,以此进一步提高田间小麦产量预测精度。

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基于无人机图像和SHAP特征筛选的小麦田间产量预测方法研究
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朱志畅 1 , 葛焱 1, 2 , 臧晶荣 3 , 李庆 3 , 金时超 4 , 徐焕良 1 , 翟肇裕 1
麦类作物学报 | 生理、生态与栽培 2025,45(2): 264-274
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麦类作物学报 |生理、生态与栽培 2025 , 45 (2) : 264 -274
基于无人机图像和SHAP特征筛选的小麦田间产量预测方法研究
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朱志畅1 , 葛焱1, 2 , 臧晶荣3, 李庆3, 金时超4, 徐焕良1, 翟肇裕1
作者信息
  • 1.南京农业大学人工智能学院,江苏 南京 210031
  • 2.南京农业大学工学院,江苏 南京 210031
  • 3.南京农业大学农学院,江苏 南京 210095
  • 4.南京农业大学前沿交叉研究院,江苏 南京 210095
通讯作者:
E-mail: (翟肇裕)
作者简介:

E-mail: (朱志畅)

(葛焱)

Research on Wheat Yield Prediction Based on UAV Imagery and SHAP Feature Selection
Zhichang ZHU1 , Yan GE1, 2 , Jingrong ZANG3, Qing LI3, Shichao JIN4, Huanliang XU1, Zhaoyu ZHAI1
Affiliations
  • 1.College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
  • 2.College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
  • 3.College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
  • 4.Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
出版时间: 2025-02-15 doi: 10.7606/j.issn.1009-1041.2025.02.14
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为了探寻适宜的小麦产量预测模型并提高其精度,从冬小麦灌浆期的无人机多光谱和RGB图像中提取了14种光谱参数和28种形态参数作为特征变量,利用线性回归、随机森林、神经网络等10种机器学习方法构建小麦田间产量预测模型,并比较了模型间预测能力的差异;同时,引入机器学习事后可解释性方法SHAP对输入的特征变量进行重要性分析和筛选,了解其提高模型预测能力的效果。结果表明:(1)10种机器学习模型中,误差逆传播神经网络BPNN的产量预测表现最好(r2=0.826, RMSE=0.094 t·hm-2);(2)根据SHAP确定的特征变量重要性排序,花青素反射指数ARI和三维冠层体积Volume对于预测结果的影响最大,占全部特征重要性总和的45.48%;(3)经过SHAP特征筛选后,确定了在BPNN产量预测模型上表现最优的9个特征变量,其预测结果r2为0.865,RMSE为0.075 t·hm-2, 比使用全特征的BPNN和事前Pearson相关性分析方法在预测精度上均有提升。因此,在优选产量预测模型基础上,可采用SHAP机制对特征变量的重要性进行筛选和分析,以此进一步提高田间小麦产量预测精度。

小麦  /  无人机图像  /  机器学习  /  SHAP加性解释方法  /  产量预测

Accurate and effective yield prediction is essential for wheat breeding, cultivation and field management. In this study, the multispectral and RGB images of winter wheat during the grain filling stage were collected from UAV, and 14 spectral traits and 28 morphological traits were extracted as feature variables. Ten machine learning methods, including linear regression, random forest and neural network, were used to construct wheat yield prediction models, and the differences between the models were compared to select the best one. Additionally, machine learning interpretability method SHAP was introduced to analyze the importance of the feature variables, in order to improve the prediction performance of the model. The results showed that among the 10 machine learning methods used, the BPNN model had the best prediction performance (r2=0.826, RMSE=0.094 t·hm-2). According to the feature importance ranking determined by SHAP, Anthocyanin Reflectance Index (ARI) and Three-Dimensional Canopy Volume (Volume) had the greatest impact on the prediction results, accounting for 45.48% of the total feature importance. After feature selection using SHAP, the BPNN model with the best performance was determined based on nine feature variables (r2=0.865, RMSE=0.075 t·hm-2). This improved the prediction accuracy compared to the BPNN model using all features and the pre-analysis method Pearson correlation analysis. Therefore, based on the optimal yield prediction model, SHAP mechanism can be used to select and analyze the importance of feature variables, so as to further improve the accuracy of wheat yield prediction.

Wheat  /  UAV imagery  /  Machine learning  /  Shapley additive explanations  /  Yield prediction
朱志畅, 葛焱, 臧晶荣, 李庆, 金时超, 徐焕良, 翟肇裕. 基于无人机图像和SHAP特征筛选的小麦田间产量预测方法研究. 麦类作物学报, 2025 , 45 (2) : 264 -274 . DOI: 10.7606/j.issn.1009-1041.2025.02.14
Zhichang ZHU, Yan GE, Jingrong ZANG, Qing LI, Shichao JIN, Huanliang XU, Zhaoyu ZHAI. Research on Wheat Yield Prediction Based on UAV Imagery and SHAP Feature Selection[J]. Journal of Triticeae Crops, 2025 , 45 (2) : 264 -274 . DOI: 10.7606/j.issn.1009-1041.2025.02.14
  • 江苏省自然科学基金项目(BK20231004)
  • 中央高校基本科研业务费专项(KYCXJC2023007)
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doi: 10.7606/j.issn.1009-1041.2025.02.14
  • 接收时间:2024-01-15
  • 首发时间:2026-09-11
  • 出版时间:2025-02-15
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  • 收稿日期:2024-01-15
  • 修回日期:2024-02-29
基金
江苏省自然科学基金项目(BK20231004)
中央高校基本科研业务费专项(KYCXJC2023007)
作者信息
    1.南京农业大学人工智能学院,江苏 南京 210031
    2.南京农业大学工学院,江苏 南京 210031
    3.南京农业大学农学院,江苏 南京 210095
    4.南京农业大学前沿交叉研究院,江苏 南京 210095

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E-mail: (翟肇裕)
参考文献
分享链接
https://castjournals.cast.org.cn/joweb/mlzwxb/CN/10.7606/j.issn.1009-1041.2025.02.14
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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