Article(id=1305139556047671885, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139508656230632, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2026.02.13, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743091200000, receivedDateStr=2025-03-28, revisedDate=1746460800000, revisedDateStr=2025-05-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1789099118482, onlineDateStr=2026-09-11, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789099118482, onlineIssueDateStr=2026-09-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789099118482, creator=13701087609, updateTime=1789099118482, updator=13701087609, issue=Issue{id=1305139508656230632, tenantId=1146029695717560320, journalId=1303687221787852879, year='2026', volume='46', issue='2', pageStart='F2', pageEnd='278', issueExtLink='null', onlineDate='null', pubDate='1771084800000', pubDateStr='2026-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789099107183, creator='13701087609', updateTime=1789103172485, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1305156559844176362, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139508656230632, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1305156559844176363, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1305139508656230632, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=264, endPage=275, ext={EN=ArticleExt(id=1305139556232221262, articleId=1305139556047671885, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Research on Accurate Monitoring of Wheat Ear Count Based on UAV Video Streams, columnId=1305133133406433355, journalTitle=Journal of Triticeae Crops, columnName=Physiology, Ecology and Cultivation, runingTitle=null, highlight=null, articleAbstract=

To evaluate the feasibility of using deep learning for efficient and accurate wheat spike counting, ten major winter wheat cultivars from the Huang-Huai wheat region (Fanmai 8, Zhoumai 36, Zhongmai 895, Malan 1, Xinmai 26, Yumai 49, Jimai 22, Zhongmai 578, Zhengmai 1860, and Zhongmai 255) were selected as materials. Three planting densities (1.2 million, 2.4 million, and 3.6 million plants·hm^-2) were tested, and four deep learning algorithms (YOLO v5, YOLO v6, YOLO v8, and YOLO v10) were employed to construct real-time video-based wheat spike detection and counting models. The models were validated using field-grown Zhongmai 578 populations (3 million plants·hm^-2). The results showed that the initial loss functions and convergence rates varied among models, with all models improving in spike detection performance as iterations increased. In terms of training speed and inference efficiency, YOLO v6 and YOLO v10 were faster but exhibited lower detection accuracy compared to YOLO v5 and YOLO v8. Although YOLO v5 and YOLO v8 required longer processing time, YOLO v8 achieved the best performance in recall (90.90%), F1-score (93.00%), mean average precision (97.20%), and overall accuracy (88.00%). The correlation (r²) between YOLO v8’s spike counts and manual counts decreased with increasing planting density, yielding values of 0.92, 0.81, and 0.79 for the three densities, respectively. Field validation demonstrated that YOLO v8 outperformed other models in stability and precision across different grain-filling stages, with the highest r² (0.90) for real-time video-based spike counting. The model also showed robustness against variations in planting density, cultivar, and spike growth stage, maintaining high performance in complex field environments. These findings suggest that YOLO v8 is a reliable algorithm for wheat spike counting, suitable for yield prediction, breeding, and cultivation management applications.

, authors=Tonghe HAN^{1, 2}, Bohan ZHANG^{1, 2}, Shuaipeng FEI², Lei LI², Haiyan SUN², Duoxia WANG², Yaxiong MENG¹, Yonggui XIAO², authorsList=Tonghe HAN, Bohan ZHANG, Shuaipeng FEI, Lei LI, Haiyan SUN, Duoxia WANG, Yaxiong MENG, Yonggui XIAO, authorCompany=null, correspAuthors=Yaxiong MENG, Yonggui XIAO, 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=1305139556462907983, articleId=1305139556047671885, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=基于无人机视频流的小麦穗数精准监测研究, columnId=1305133133590982733, journalTitle=麦类作物学报, columnName=生理、生态与栽培, runingTitle=null, highlight=null, articleAbstract=

为了解利用深度学习技术实现小麦穗数高效精准测定的可行性，选取黄淮冬麦区10个小麦主栽品种（泛麦8号、周麦36、中麦895、马兰1号、新麦26、豫麦49、济麦22、中麦578、郑麦1860和中麦255），设置120 万、240万和360万株·hm^-2三个种植密度，采用YOLO v5、YOLO v6、YOLO v8和YOLO v10四种深度学习算法，构建通过视频流实时识别计数小麦穗数的模型，并以小麦品种中麦578的生产田群体（300万株·hm^-2）进行预测精度验证。结果表明，不同模型损失函数起点和下降幅度均存在差异，各模型对麦穗的检测性能均随迭代次数的增加而提升。在训练时间和检测效率方面，YOLO v6和YOLO v10模型速度较快，但检测性能和准确率均低于YOLO v5和YOLO v8。YOLO v5和YOLO v8模型耗时相对较长，但YOLO v8模型的召回率、F1分数、平均精度及准确率表现最佳，分别为90.90%、93.00%、97.20%和88.00%。YOLO v8模型识别穗数与真实计数的相关性随种植密度增加而降低，在三个种植密度下r²分别为0.92、0.81和0.79。经生产田群体验证，综合考虑r²、RMSE和MAE值，YOLO v8模型相对其他模型在小麦灌浆各阶段的穗数识别中表现最稳定，识别穗数精度最高；在视频流中YOLO v8模型检测穗数的r²最高，达到0.90。这说明YOLO v8模型在小麦群体穗数识别方面具有较高的可靠性，受种植密度、品种和穗数发育阶段影响较小，在复杂田间环境下同样表现出较好的鲁棒性和高效性，适用于产量预测、育种研究、栽培管理等场景。

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