Article(id=1325193387309150357, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193209042829661, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2024.01.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659024000000, receivedDateStr=2022-07-29, revisedDate=1703001600000, revisedDateStr=2023-12-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880324437, onlineDateStr=2026-11-05, pubDate=1707926400000, pubDateStr=2024-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880324437, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880324437, creator=13701087609, updateTime=1793880324437, updator=13701087609, issue=Issue{id=1325193209042829661, tenantId=1146029695717560320, journalId=1319690209941618849, year='2024', volume='5', issue='1', pageStart='1', pageEnd='70', issueExtLink='null', onlineDate='null', pubDate='1707926400000', pubDateStr='2024-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880281935, creator='13701087609', updateTime=1793944056467, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325460699962540033, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193209042829661, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325460699962540034, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193209042829661, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=40, endPage=50, ext={EN=ArticleExt(id=1325193387535642774, articleId=1325193387309150357, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research status and trends of intelligent technology in plant protection machinery, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Intelligent plant protection machinery in large fields is an important means to improve pesticide utilization, enhance the quality of agricultural products, and ensure the sustainable development of agriculture. In order to understand the research status of intelligent plant protection technology and clarify the future development direction, this article focused on three main directions of intelligent operation of high-clearance plant protection machinery: prescription application in large-scale farmland, target spraying on a small scale in land parcels, and variable-speed spraying. From the aspects of perception, analysis, decision-making, and control, the article elaborates on the technical principles of prescription map construction, spatial coordinate transformation methods, and prescription recognition that integrate high-precision satellite positioning, the current level of key technologies for target spraying technology routes and weed recognition, and believed that high-precision spraying based on online prescriptions must be the focus of future research. The article carried out a comparative analysis of the advantages and disadvantages of four speed measurement modes for variable-speed spraying and believed that with the widespread application of satellite positioning in agricultural machinery, satellite-based speed measurement will become the main method of high-precision speed measurement due to its greater versatility, convenience, and accuracy. To explore the current development status of variable spraying control, the article summarized two control methods and implementation methods for pressure-controlled variable spraying. From a comprehensive perspective, pipeline cut-off flow control is the main way to achieve variable spraying, and three control algorithms of segmented control, pulse width modulation, and PID control of flow control were analyzed from the perspective of technical principles, implementation processes, and optimized applications. After the discussion, it is believed that PID control based on machine learning will be an important direction to improve traffic control performance. With the continuous development of artificial intelligence technology, plant protection robots that integrate intelligent perception, analysis and decision-making and autonomous operation capabilities will become the mainstream development direction of plant protection machinery in the future.

, authors=Zheng LIU¹, Chengqian JIN^{1, 2, *}, Yugang FENG¹, Tengxiang YANG¹, authorsList=Zheng LIU, Chengqian JIN, Yugang FENG, Tengxiang YANG, authorCompany=null, correspAuthors=Chengqian JIN, 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=1325193387804078231, articleId=1325193387309150357, tenantId=1146029695717560320, journalId=1319690209941618849, language=CN, title=植保机械智能化技术研究现状与趋势, columnId=0, journalTitle=智能化农业装备学报（中英文）, columnName=, runingTitle=null, highlight=null, articleAbstract=

大田智能化植保机械是提高农药利用率、提升粮食品质、保障农业可持续发展的重要手段。为了解植保机械智能化技术的研究现状、明确未来发展方向，围绕高地隙植保机智能化作业的规模农田大尺度下的处方施药、地块级小尺度下的对靶喷药以及随速变量喷药三大主流方向，从感知、分析、决策、控制等方面，阐述了融合高精度卫星定位的处方图构建、空间坐标变换方法和处方识别的技术原理、对靶喷药技术路线及杂草识别关键技术研究水平，认为基于在线处方的高精度喷药是未来研究的重点；针对随速变量喷药的高精度测速这一关键技术，进行了4种测速模式的优缺点对比分析，随着卫星定位在农机上的广泛应用，卫星测速以其更加通用、便捷、精准将成为高精度测速的主要方式。为探究变量喷药控制的发展现状，对压力控制变量喷药和管路截流式流量控制变量喷药两种控制方式及实现方法进行了总结，从结构复杂性、实现的安全性和系统稳定性方面综合来看，管路截流式流量控制是实现变量喷药的主要方式，并从技术原理、实现过程及优化应用情况对流量控制的分段控制、脉宽调制、PID控制3种控制算法进行了论述，认为基于机器学习的PID控制将是改善流量调控性能重要方向。随着人工智能技术的不断发展，融合智能感知、分析决策和自主作业能力的植保机器人将成为植保机械未来主流的发展方向。

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刘政，男，1990年生，江苏宿迁人，博士，助理研究员；研究方向为智能农机技术与装备。E-mail：913573407@qq.com

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刘政，男，1990年生，江苏宿迁人，博士，助理研究员；研究方向为智能农机技术与装备。E-mail：913573407@qq.com

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