Article(id=1325193534004933482, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193166743282319, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2025.04.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733932800000, receivedDateStr=2024-12-12, revisedDate=1736956800000, revisedDateStr=2025-01-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880359411, onlineDateStr=2026-11-05, pubDate=1763136000000, pubDateStr=2025-11-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880359411, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880359411, creator=13701087609, updateTime=1793880359411, updator=13701087609, issue=Issue{id=1325193166743282319, tenantId=1146029695717560320, journalId=1319690209941618849, year='2025', volume='6', issue='4', pageStart='1', pageEnd='110', issueExtLink='null', onlineDate='null', pubDate='1763136000000', pubDateStr='2025-11-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880271850, creator='13701087609', updateTime=1793943421371, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325458035077952468, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193166743282319, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325458035077952469, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193166743282319, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=17, endPage=23, ext={EN=ArticleExt(id=1325193537641395051, articleId=1325193534004933482, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research and experiment on walnut kernel grading based on improved YOLOv7, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=
In China, walnut kernel grading predominantly relies on mechanical and manual methods, which suffer from high economic costs, low efficiency, and poor accuracy, severely limiting the standardization and economic value of walnut kernel products. To achieve automated and intelligent walnut kernel grading, this study proposes a deep learning-based intelligent grading method. An image acquisition device was utilized to capture walnut kernel images across six grades under three distinct background colors: pure white, light green, and conveyor belt. The Python OpenCV library was employed for data augmentation to expand the dataset, resulting in a VOC format dataset of 12 246 images, with training, testing, and validation sets allocated in an 8：1：1 ratio. To select the most suitable model for walnut kernel grading, two models were constructed based on the YOLOv5 and YOLOv7 object detection networks, respectively. Both models were trained using pre-trained weights, achieving average grading accuracies of 87.83% and 91.16% for YOLOv5 and YOLOv7, respectively. During validation, YOLOv7 exhibited mispredictions, prompting the integration of two attention mechanisms, ECANet and CBAM, to refine the model. The improved models demonstrated enhanced performance, with the YOLOv7+CBAM model achieving the highest average accuracy (94.5%) and F1-score (90.2%). Compared to the original YOLOv7, the upgraded model increased average accuracy and F1-score by 3.34% and 5.9%, respectively, while adding only 2ms to inference time. To validate practical feasibility, a recognition system platform was developed for walnut kernel classification testing. Four experimental groups were designed, each containing 120 walnut kernels (20 per grade). The YOLOv7+CBAM model achieved an average recognition accuracy of 91.63%, demonstrating robust grading capabilities for walnut kernel appearance quality. This study provides a reference for intelligent walnut kernel grading systems.
, authors=Yingjie YU, Kunshu WU, Pengxin RAN, Jianian LI, authorsList=Yingjie YU, Kunshu WU, Pengxin RAN, Jianian LI, authorCompany=null, correspAuthors=Jianian LI, authorNote=
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中国核桃仁分级大多依靠机械和人工，经济成本高、效率低、精度差，严重影响核桃仁产品的标准化和经济价值。为实现核桃仁分级的自动化与智能化，本研究提出基于深度学习的核桃仁智能分级方法。利用图像采集装置采集纯白、淡绿与传送带3种不同颜色背景下的6个等级的核桃仁图像，使用Python的OpenCV库对图像进行数据增强扩充数据集，建立VOC格式数据集12 246张，其中训练集、测试集与验证集的比例设置为8：1：1。为选取更适合完成核桃仁分级的模型，分别基于目标检测网络YOLOv5和YOLOv7构建核桃仁分级模型，采用加载预训练权重的方式训练模型，得到YOLOv5和YOLOv7模型核桃仁分级的平均准确率分别为87.83%和91.16%。在验证过程中YOLOv7预测到了错误的对象，引入两种注意力机制ECANet与CBAM对YOLOv7进行改进，改进后的两种模型的训练效果均有所提升，其中YOLOv7+CBAM模型效果更好，平均准确率为94.5%、F1评分为90.2%。改进后的YOLOv7核桃仁分类平均准确率和F1评分比YOLOv7高出3.34%和5.9%，并且推理时间比YOLOv7仅增加2 ms。为验证模型的可行性，搭建识别系统平台进行核桃仁等级识别分类测试，设置6种等级的核桃仁各20个，共120个核桃仁为一组，共4组试验。得到YOLOv7+CBAM模型的核桃仁平均识别正确率为91.63%。改进后的YOLOv7+CBAM模型可以实现对核桃仁外观品质的良好分级。本研究可为核桃仁智能化分级提供参考。
, authors=于英杰, 吴坤澍, 冉朋鑫, 李加念
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于英杰，女，1980年生，吉林舒兰人，博士，副教授；研究方向为高原特色农业技术与装备研究。E-mail：646677093@qq.com
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于英杰，女，1980年生，吉林舒兰人，博士，副教授；研究方向为高原特色农业技术与装备研究。E-mail：646677093@qq.com
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