Article(id=1305139340028441324, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2026.03.11, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745337600000, receivedDateStr=2025-04-23, revisedDate=1750867200000, revisedDateStr=2025-06-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1789099066979, onlineDateStr=2026-09-11, pubDate=1773504000000, pubDateStr=2026-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789099066979, onlineIssueDateStr=2026-09-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789099066979, creator=13701087609, updateTime=1789099066979, updator=13701087609, issue=Issue{id=1305139324555649224, tenantId=1146029695717560320, journalId=1303687221787852879, year='2026', volume='46', issue='3', pageStart='F2', pageEnd='424', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789099063289, creator='13701087609', updateTime=1789103147683, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305156455804465602, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305156455804465603, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139324555649224, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=384, endPage=392, ext={EN=ArticleExt(id=1305139341798437613, articleId=1305139340028441324, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Prediction of Wheat Grain Protein Content Based on Vegetation Indices and Texture Features, columnId=1305133133406433355, journalTitle=Journal of Triticeae Crops, columnName=Physiology, Ecology and Cultivation, runingTitle=null, highlight=null, articleAbstract=

To achieve rapid pre-harvest prediction of grain protein content (GPC) in winter wheat, this study developed an indirect prediction method integrating multispectral vegetation indices and texture features. Based on nitrogen fertilization experiments and spectral data collected from 2022 to 2024, texture parameters were extracted using Gray-level co-occurrence matrix (GLCM). Leaf nitrogen content (LNC) served as the critical intermediary to link spectral models with GPC, establishing a multispectral-based prediction model for winter wheat GPC. The results demonstrated that during the early grain-filling stage, vegetation indices combined with soil-background-filtered texture features achieved optimal LNC estimation. The exponential model y=0.281exp (0.097x), showed superior performance (r²=0.787, RMSE=0.221 g·kg^-1); significant correlations were observed between LNC and GPC across key growth stages, with correlation coefficients of 0.780(anthesis), 0.810 (early grain-filling), 0.704(mid-grain-filling), and 0.714(late grain-filling); the exponential model y=7.160exp (0.018x), developed using early grain-filling stage data, achieved the highest GPC prediction accuracy [r²=0.697, RMSE=0.096 g· (100 g)^-1]. The established early grain-filling stage GPC prediction model enhances field management optimization, enables grain quality classification, and provides technical support for applying remote sensing technology in high-quality wheat production and precision agriculture.

, authors=Bingxiao DU, Dasheng ZHENG, Yulan YE, Zhifeng CUI, Lulu YANG, Nannan LIANG, Rui WANG, authorsList=Bingxiao DU, Dasheng ZHENG, Yulan YE, Zhifeng CUI, Lulu YANG, Nannan LIANG, Rui WANG, authorCompany=null, correspAuthors=Rui WANG, 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=1305139342003958510, articleId=1305139340028441324, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=基于植被指数和纹理特征的小麦籽粒蛋白质含量预测, columnId=1305133133590982733, journalTitle=麦类作物学报, columnName=生理、生态与栽培, runingTitle=null, highlight=null, articleAbstract=

为实现冬小麦收获前籽粒蛋白质含量的快速预测，基于2022—2024年的氮肥运筹试验数据，结合灰度共生矩阵提取纹理参数，通过多光谱植被指数与纹理特征的融合，构建小麦叶片氮含量预测模型，再通过叶片氮含量将光谱模型和籽粒蛋白质含量连接起来，建立冬小麦籽粒蛋白质含量预测模型。结果表明，利用灌浆初期剔除土壤背景后的植被指数和纹理特征数据能较好预测小麦叶片氮含量，其模型为y=0.281exp（0.097x），r²为0.787，RMSE为0.221 g·kg^-1；冬小麦开花期、灌浆初期、灌浆中期和灌浆后期的叶片氮含量与籽粒蛋白质含量相关性均达到极显著水平（P<0.01），相关系数分别为0.780、0.810、0.704和0.714；灌浆初期，利用指数函数构建的籽粒蛋白质预测模型的精度最优，其函数模型为y=7.160exp（0.018x），r²为0.697，RMSE为0.096 g·（100 g）^-1。因此，可以基于植被指数、纹理特征和灌浆初期叶片含氮量间接实现冬小麦籽粒蛋白质含量预测。

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E-mail: dbx@nwafu.edu.cn（杜冰笑）

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