Article(id=1324333574840787076, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2026.01.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751990400000, receivedDateStr=2025-07-09, revisedDate=1758902400000, revisedDateStr=2025-09-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1793675329170, onlineDateStr=2026-11-03, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793675329170, onlineIssueDateStr=2026-11-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793675329170, creator=13701087609, updateTime=1793675329170, updator=13701087609, issue=Issue{id=1324333573712511176, tenantId=1146029695717560320, journalId=1319690209941618849, year='2026', volume='7', issue='1', pageStart='1', pageEnd='196', 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=1793675328901, creator='13701087609', updateTime=1793872389599, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325160106287039215, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325160106287039216, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=8, endPage=18, ext={EN=ArticleExt(id=1324333575004364933, articleId=1324333574840787076, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research on navigation path recognition method for wheat combine harvester, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=
To address the challenges in balancing accuracy and real-time performance for navigation path recognition methods of wheat harvesters, this study introduces a lightweight design based on the DeepLabv3+ model. Specifically, the original backbone network is replaced with MobileNetV3-Large, and the ReLU activation function is substituted with Leaky_ReLU. To further reduce computational load, the three dilated convolutions with different dilation rates within the atrous spatial pyramid pooling (ASPP) module are replaced with depthwise separable convolution (DSC) employing identical dilation rates. To ensure the generalizability of the wheat harvester navigation path recognition research, images of wheat harvest areas were captured under six typical environmental conditions: strong light, low light, back lighting, front lighting, shadows, and field edges. The road information within the collected images was annotated by the Labelme tool, thereby constructing a wheat harvest region data set. In the path extraction stage, key points were acquired from the segmentation mask maps using the horizontal scanning method, and the navigation path was fitted utilizing a piece wise cubic B-spline algorithm. Experimental results demonstrate that the improved DeepLabv3+ model achieved a segmentation accuracy of 98.04% and intersection over union (IoU) ratio of 95.20%, respectively, with a video image processing frame rate of 7.5 frames per second. The average pixel error and average distance error for navigation path recognition were 7.4 pixels and 37 mm, respectively. The wheat harvester operated at a travel speed of approximately 1.5 m/s, and the path recognition time per single frame was only 0.15 seconds. This performance effectively meets the real-time and accuracy requirements for wheat harvester operation. This research provides a theoretical foundation and technical support for enhancing the autonomous navigation capabilities of wheat harvesters.
, authors=Jianian LI, Kunshu WU, Kunyi LI, Shaomin CHEN, authorsList=Jianian LI, Kunshu WU, Kunyi LI, Shaomin CHEN, authorCompany=null, correspAuthors=Shaomin CHEN, authorNote=
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针对小麦收获机导航路径识别方法难以兼顾准确性和实时性等问题，本研究在DeepLabv3+模型的基础上进行轻量化设计，采用MobileNetV3-Large替换原主干网络，并使用Leaky_ReLU替代ReLU激活函数；为了进一步减少网络计算量，将金字塔池化模块（atrous spatial pyramid pooling, ASPP）中的3个不同扩张率的空洞卷积替换为具有相同扩张率的深度可分离卷积（depthwise separable convolution，DSC）。为使小麦收获机导航路径识别研究具有较好的普遍性，采集了强光、弱光、逆光、顺光、阴影、地块边缘6种典型环境下的小麦收获区图像，使用Labelme工具对采集图像中的道路信息进行标注，构建小麦收获区域数据集。在路径提取阶段，通过水平扫描法从分割掩码图中获取关键点，并利用多段三次B样条算法拟合导航路径。试验结果表明，改进后的DeepLabv3+模型分割精度和交并比分别为98.04%和95.20%，视频图像处理帧率为7.5帧/s。6种小麦典型环境下，导航路径识别的平均像素误差和平均距离误差分别为7.4像素和37 mm，小麦收获机行驶速度约为1.5 m/s，单帧图像路径识别时间约为0.15 s，能够有效地满足小麦收获机实时性和准确性的需求。本研究可为提升小麦收获机自主导航能力提供理论基础和技术支撑。
, authors=李加念, 吴坤澍, 李坤依, 陈绍民
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李加念，男，1983年生，湖南道县人，博士，教授；研究方向为农业智能化检测与控制、水肥一体化智能装备与技术。E-mail：ljn825@163.com
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李加念，男，1983年生，湖南道县人，博士，教授；研究方向为农业智能化检测与控制、水肥一体化智能装备与技术。E-mail：ljn825@163.com
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