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1](https:\u002F\u002Faircas.cas.cn\u002Fdtxw\u002Fhzjl\u002F202609\u002FW020260904583592815639.png)\n\n大会合影","中国科学院空天信息创新研究院2026年9月4日",84,{"impact":130,"substance":131,"depth":30,"authority":31,"freshness":229,"relevant":33,"comment":230},6,"全国性数字农业学术盛会，汇聚顶尖专家，聚焦天空地一体化农情监测，具有行业引领价值。",[232],{"name":226,"url":223},[234,235,236,237,238],"数字农业","农情监测","遥感","天空地一体化","学术研讨会",{"code":240,"slug":241,"name":242,"path":243,"parent_code":145},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":246},{"candidates":247,"agricultural_evidence":25},[248,252,256],{"code":240,"score":85,"supported":25,"title":249,"abstract":250,"tags":251},[235],[236],[235,236],{"code":141,"score":105,"supported":25,"title":253,"abstract":254,"tags":255},[234],[234],[234],{"code":257,"score":157,"supported":25,"title":258,"abstract":259,"tags":261},"G1",[],[260],"技术培训",[],[263,264],"天空地一体化 学术研讨会 农情监测 数字农业","天空地一体化 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种业振兴 种质资源 安徽","产业政策 种业振兴","产业政策种业振兴种质资源安徽-1778","2026-09-07T00:04:37.022591Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":20,"content":322,"source_name":323,"source_url":20,"published_at":324,"category":325,"cover_url":20,"hotness":130,"is_selected":51,"score":326,"score_detail":327,"sources":329,"tags":334,"primary_topic":338,"secondary_topics":343,"classification":344,"search_phrases":352,"slug":355,"view_count":33,"doi":20,"paper":20,"created_at":356},1780,"农业农村部办公厅关于异噁唑虫酰胺等化学物质特定用途管理认定的通知","http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fdetail\u002F33028","农业农村部办公厅8月29日印发通知：在对氟雷拉纳等13种化学物质特定用途管理认定基础上，对异噁唑虫酰胺等12种化学物质用于农林病虫草害防控等特定用途时的管理作出规定——用于《农药管理条例》第二条规定目的、场所时，按农药有效成分处理；生产经营含有上述物质的产品用于上述目的、场所的，按假农药处理。","[English](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Feng\u002Findex)[办公平台](http:\u002F\u002Foa.icama.org.cn\u002Fseeyon\u002Findex.jsp)[政务邮箱![Image 2: 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web-logo](http:\u002F\u002Fwww.icama.org.cn\u002Fnyupload\u002Fimage\u002F202301\u002Fc4656dee-8927-4c04-9f68-d0aa98bdb1ab.png)](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fdetail\u002F33028#)\n\n搜索\n\n[中文版](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Fzwb\u002Findex)[农药科学与管理](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Fkgls\u002Findex)[English](http:\u002F\u002Fwww.chinapesticide.org.cn\u002Feng\u002Findex)\n\n[首页](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Findex)[药检系统](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FdrugTest)[新闻动态](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fnews)[信息公开](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FinfoPublic)[服务大厅](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FserviceHall)[专题栏目](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Ftopic)[数据中心](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FdataCenter)[数字平台](https:\u002F\u002Fwww.icama.cn\u002F)[农药科学与管理](http:\u002F\u002Fwww.icama.org.cn\u002Fkgls\u002Findex)\n\n[首页](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Findex)[药检系统](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FdrugTest)[新闻动态](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fnews)[信息公开](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FinfoPublic)[服务大厅](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FserviceHall)[专题栏目](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Ftopic)[数据中心](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FdataCenter)[服务平台](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FservicePlatform)[数字平台](https:\u002F\u002Fwww.icama.cn\u002F)[农药科学与管理](http:\u002F\u002Fwww.icama.org.cn\u002Fkgls\u002Findex)\n\n![Image 4](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Fm-nav-icon.png)\n\n搜索\n\n当前位置：[新闻动态](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FnewsType\u002F4)[通知通告](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FnewsType\u002F55)\n\n# 农业农村部办公厅关于异噁唑虫酰胺等化学物质特定用途管理认定的通知\n\n发布时间：2026-09-02 来源：种植业管理司 浏览次数：1215\n\n种植业管理司**_2026-09-02_****_![Image 5](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fdetail\u002Ficon-gkl.png)_ _1215_**\n\n各省、自治区、直辖市农业农村（农牧）厅（局、委），新疆生产建设兵团农业农村局：\n\n在对氟雷拉纳等13种化学物质特定用途管理认定的基础上，通过认真调研和科学评估，按照《农药管理条例》有关规定，经全国农药登记评审委员会审议，我部对异噁唑虫酰胺等12种化学物质（详见附件）用于农林病虫草害防控等特定用途时的管理作出如下规定：\n\n一、异噁唑虫酰胺等12种化学物质用于《农药管理条例》第二条规定目的、场所时，按农药有效成分处理。\n\n二、生产经营含有上述物质的产品，用于《农药管理条例》第二条规定目的、场所的，依据《农药管理条例》第四十四条规定，按假农药处理。\n\n附件：按农药有效成分处理的异噁唑虫酰胺等12种化学物质名单\n\n农业农村部办公厅\n\n2026年8月29日\n\n附件:\n*   [按农药有效成分处理的异噁唑虫酰胺等12种化学物质名单](http:\u002F\u002Fwww.icama.org.cn\u002Fnyupload\u002Ffile\u002F202609\u002F31e8be10-67c7-42cc-a4f5-1e43bfd05164.docx)\n\n2\n\n#### [微信扫一扫：分享 ![Image 6](blob:http:\u002F\u002Flocalhost\u002F651a5f17cbc5f16bcacc862772114f57) 微信里点“发现”，扫一下 二维码便可将本文分享至朋友圈。](javascript:;)[](https:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=http%3A%2F%2Fwww.icama.org.cn%2Fzwb%2Fdetail%2F33028&title=%E5%86%9C%E4%B8%9A%E5%86%9C%E6%9D%91%E9%83%A8%E5%8A%9E%E5%85%AC%E5%8E%85%E5%85%B3%E4%BA%8E%E5%BC%82%E5%99%81%E5%94%91%E8%99%AB%E9%85%B0%E8%83%BA%E7%AD%89%E5%8C%96%E5%AD%A6%E7%89%A9%E8%B4%A8%E7%89%B9%E5%AE%9A%E7%94%A8%E9%80%94%E7%AE%A1%E7%90%86%E8%AE%A4%E5%AE%9A%E7%9A%84%E9%80%9A%E7%9F%A5&pic=http%3A%2F%2Fwww.icama.org.cn%2Fresources%2Fweb%2Fimages%2Fhome%2Ficon_mail.png&appkey=)[](http:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=http%3A%2F%2Fwww.icama.org.cn%2Fzwb%2Fdetail%2F33028&title=%E5%86%9C%E4%B8%9A%E5%86%9C%E6%9D%91%E9%83%A8%E5%8A%9E%E5%85%AC%E5%8E%85%E5%85%B3%E4%BA%8E%E5%BC%82%E5%99%81%E5%94%91%E8%99%AB%E9%85%B0%E8%83%BA%E7%AD%89%E5%8C%96%E5%AD%A6%E7%89%A9%E8%B4%A8%E7%89%B9%E5%AE%9A%E7%94%A8%E9%80%94%E7%AE%A1%E7%90%86%E8%AE%A4%E5%AE%9A%E7%9A%84%E9%80%9A%E7%9F%A5&source=%E5%86%9C%E4%B8%9A%E5%86%9C%E6%9D%91%E9%83%A8%E5%8A%9E%E5%85%AC%E5%8E%85%E5%85%B3%E4%BA%8E%E5%BC%82%E5%99%81%E5%94%91%E8%99%AB%E9%85%B0%E8%83%BA%E7%AD%89%E5%8C%96%E5%AD%A6%E7%89%A9%E8%B4%A8%E7%89%B9%E5%AE%9A%E7%94%A8%E9%80%94%E7%AE%A1%E7%90%86%E8%AE%A4%E5%AE%9A%E7%9A%84%E9%80%9A%E7%9F%A5&desc=&pics=http%3A%2F%2Fwww.icama.org.cn%2Fresources%2Fweb%2Fimages%2Fhome%2Ficon_mail.png)\n\n### **相关**内容[更多>>](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FnewsType\u002F55)\n\n[![Image 7: 农业农村部办公厅关于异噁唑虫酰胺等化学物质特定用途管理认定的通知](http:\u002F\u002Fwww.icama.org.cn\u002Fnyupload\u002Fimage\u002F202305\u002F5a8e73d1-3c58-448c-8351-7084ee28b36a.png) ## 农业农村部办公厅关于异噁唑虫酰胺等化学物质特定用途管理认定的通知 各省、自治区、直辖市农业农村（农牧）厅（局、委），新疆生产建设兵团农业农村局： 在对氟雷拉纳等13种化学物质特定用途管理认定的基础上，通过认真调研和科学评估，按照《农药管理条例》有关规定，经全国农药登记评审委员会审议，我部对异噁唑虫酰胺等12种化学物质（详见附件）用于农林病虫草害防控等特定用途时的管理作出如下规定： 一、异噁唑虫酰胺等12种化学物质用于《农药管理条例》第二条规定目的、场所时，按 2026-09-02](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fdetail\u002F33028)\n\n[![Image 8: 关于办理指定试验单位有关事项的通知](http:\u002F\u002Fwww.icama.org.cn\u002Fnyupload\u002Fimage\u002F202305\u002Fcc9c93f9-b360-4960-b804-9af524557a69.png) ## 关于办理指定试验单位有关事项的通知 农药检（试验）函〔2026〕55号各有关单位：按照《农药登记试验管理办法》第三十二条“现有农药登记试验单位无法承担的试验项目，由农业农村部指定的单位承担”有关规定，依据部农药管理司意见，为建立公开透明、高效便捷的农药试验单位指定机制，便利农药登记申请人办理指定事项，现将有关事项通知如下。一、基本原则（一）一般要求。指定的试验单位须具备同类试验项目的实施经验，配备试验所需仪器设备、专业人员，拥有独立 2026-08-25](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fdetail\u002F32995)\n\n[![Image 9: 中国政府网](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002F%E4%B8%AD%E5%9B%BD%E6%94%BF%E5%BA%9C%E7%BD%91.png)](http:\u002F\u002Fwww.gov.cn\u002F)[![Image 10: 中华人民共和国农业农村部](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Fpng_mar.png)](http:\u002F\u002Fwww.moa.gov.cn\u002F)\n\n[网站声明](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002Fwebsite_statement)|[关于我们](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FdrugTest)|[联系我们](http:\u002F\u002Fwww.icama.org.cn\u002Fzwb\u002FdrugTest?contact=1)\n\n[![Image 11: 事业单位](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Ficon_sydy.png)](javascript:void(0))\n*   主办单位：农业农村部农药检定所\n*   网站管理：农业农村部农药检定所药情信息处 [备案序号：京ICP备18011029号-1](javascript:void(0))\n*    地址：北京市朝阳区麦子店22号  邮编：100125 电话：010-59194018\n*   [京公网安备 11010502043033号![Image 12: 京公网安备 11010502043033号](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Ficon_beiab.png)](javascript:void(0))\n*   网站保留所有权, 未经允许不得复制, 镜像\n\n*   ![Image 13: 中国农药查询(安卓版)](http:\u002F\u002Fwww.icama.org.cn\u002Fnyupload\u002Fimage\u002F202212\u002Fb82d3d6d-5398-42e8-a70d-16fb23414579.png)中国农药查询(安卓版)\n*   ![Image 14: 微语农药(公众号)](http:\u002F\u002Fwww.icama.org.cn\u002Fnyupload\u002Fimage\u002F202212\u002F0828b460-066d-4348-808b-53859ccf02b8.jpg)微语农药(公众号)\n\n![Image 15: 回到顶部](http:\u002F\u002Fwww.icama.org.cn\u002Fresources\u002Fweb\u002Fimages\u002Fhome\u002Ftop.png)回到顶部","中国农药信息网2026年9月2日","2026-09-02T00:00:00Z","政策",82,{"impact":130,"substance":29,"depth":31,"authority":31,"freshness":57,"relevant":33,"comment":328},"全国性农药管理政策，明确12种化学物质按农药有效成分处理，影响农药生产经营，具有较高行业指导价值。",[330,331],{"name":323,"url":320},{"name":332,"url":333},"盐城市农业农村局 2026-09-15","https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002FZZYGLS\u002F202609\u002Ft20260901_6487217.htm",[335,336,337],"农药管理","化学物质认定","政策法规",{"code":339,"slug":340,"name":341,"path":342,"parent_code":294},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":345},{"candidates":346,"agricultural_evidence":25},[347],{"code":339,"score":63,"supported":25,"title":348,"abstract":349,"tags":351},[],[350],"农药",[335],[353,354],"化学物质认定 农药管理 政策法规","化学物质认定 农药管理","化学物质认定农药管理政策法规-1780","2026-09-07T00:04:37.164620Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":20,"content":20,"source_name":362,"source_url":20,"published_at":183,"category":127,"cover_url":20,"hotness":24,"is_selected":51,"score":363,"score_detail":364,"sources":367,"tags":369,"primary_topic":375,"secondary_topics":380,"classification":386,"search_phrases":405,"slug":408,"view_count":118,"doi":20,"paper":409,"created_at":417},1818,"From Orbit to Leaf: A Critical Systematic Review of Artificial Intelligence for Multi-Scale Crop Monitoring and Soybean Leaf Disease Diagnosis（IJAIML 2026, 6(9s), 471-498）","https:\u002F\u002Fwww.svedbergopen.com\u002Findex.php\u002Fijaiml\u002Farticle\u002Fview\u002F1516","依据PRISMA 2020筛选4286篇记录，保留2019—2026年84项主要研究（41项轨道、17项航空、26项近距），以大豆叶病识别为近距测试案例。引入AGRI-CRITIC八维评估标准并综合为Field Readiness Index（FRI）。中位报告准确率93.9%但中位FRI仅37.5\u002F100，仅22.6%研究超过田间就绪阈值60，且报告准确率与评估集大小呈1.9%\u002F十倍样本负相关——小样本乐观的标志。识别七类反复失败模式，发布FIELD-READY十项报告协议与三阶段研究议程。","International Journal of Artificial Intelligence and Machine Learning 2026, 6(9s)",81,{"impact":30,"substance":365,"depth":131,"authority":105,"freshness":186,"relevant":33,"comment":366},24,"系统综述揭示AI作物监测报告准确率与田间实用性差距，提出评估标准与报告协议，对智慧农业落地有重要参考价值。",[368],{"name":362,"url":360},[370,371,372,373,374],"农业人工智能","系统综述","作物监测","大豆叶病","田间就绪",{"code":376,"slug":377,"name":378,"path":379,"parent_code":287},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[381],{"code":382,"slug":383,"name":384,"path":385,"parent_code":145},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":50,"locked":51,"version":52,"method":53,"evidence":387},{"candidates":388,"agricultural_evidence":25},[389,396,401],{"code":376,"score":390,"supported":25,"title":391,"abstract":393,"tags":395},11,[392],"soybean",[394],"大豆",[],{"code":382,"score":32,"supported":25,"title":397,"abstract":399,"tags":400},[398],"artificial intelligence",[],[370],{"code":240,"score":33,"supported":51,"title":402,"abstract":403,"tags":404},[],[],[372],[406,407],"农业人工智能 作物监测 大豆叶病 田间就绪","农业人工智能 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农业科技 饲草产业","产业政策 农业科技","产业政策农业科技饲草产业-1810","2026-09-07T00:04:40.188487Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":20,"content":20,"source_name":125,"source_url":20,"published_at":126,"category":127,"cover_url":20,"hotness":24,"is_selected":51,"score":466,"score_detail":467,"sources":469,"tags":471,"primary_topic":475,"secondary_topics":476,"classification":477,"search_phrases":484,"slug":487,"view_count":118,"doi":20,"paper":488,"created_at":495},1813,"Digital Technology Driving Agricultural Economic Resilience: Mechanism Analysis, Empirical Test, and Policy Implications（《智慧农业（中英文）》2026, 8(3): 203-214）","https:\u002F\u002Fagris.fao.org\u002Fsearch\u002Far\u002Frecords\u002F6a96d27a8a202ccda1b6499b","基于2012、2015、2017、2018、2020年中国省级非连续面板数据，运用投入产出模型测算\"信息传输、软件和信息技术服务业\"被农业部门完全消耗系数界定数字技术投入总值。基准回归显示ICT投入系数在1%水平显著为正。维度分解表明数字技术显著增强恢复力与发展力，但对抵抗力维度影响有限且呈现边际负向效应。机制分析表明市场化、经济效率、运输密度为主要传导路径。异质性分析显示数字技术在西部呈现\"数字补偿\"优势。",79,{"impact":29,"substance":131,"depth":30,"authority":132,"freshness":57,"relevant":33,"comment":468},"基于省级面板数据与投入产出模型，实证分析数字技术对农业经济韧性的多维影响，机制与异质性分析扎实，对政策制定有参考价值。",[470],{"name":125,"url":464},[137,472,473,474],"农业经济","数字技术","ICT投入",{"code":141,"slug":142,"name":143,"path":144,"parent_code":145},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":478},{"candidates":479,"agricultural_evidence":25},[480],{"code":141,"score":24,"supported":25,"title":481,"abstract":482,"tags":483},[137],[],[473,137],[485,486],"农业经济 数字技术 智慧农业 ICT投入","农业经济 数字技术","农业经济数字技术智慧农业ICT投入-1813",{"doi":20,"openalex_id":20,"authors":489,"venue":20,"cited_by_count":168,"oa_url":20,"card":490,"direction":415,"ingested_from":175},[],{"tldr":491,"method":492,"finding":493,"direction":415,"opportunity":494},"研究数字技术投入对中国农业经济韧性的影响及其机制。","投入产出模型测算ICT完全消耗系数，面板回归及机制分析。","数字技术显著增强恢复力与发展力，对抵抗力影响有限且边际负向。","可探索数字技术对农业抵抗力维度的非线性影响，或针对西部地区的补偿机制设计政策。","2026-09-07T00:04:41.311629Z",{"id":497,"title":498,"url":499,"summary":500,"summary_zh":20,"content":501,"source_name":502,"source_url":20,"published_at":183,"category":23,"cover_url":20,"hotness":24,"is_selected":51,"score":466,"score_detail":503,"sources":505,"tags":507,"primary_topic":510,"secondary_topics":516,"classification":517,"search_phrases":533,"slug":536,"view_count":157,"doi":20,"paper":20,"created_at":537},1802,"上海育成南方稻区唯一节水抗旱稻\"标准品种\"——\"中旱3号\"累计推广534.96万亩","https:\u002F\u002Fwww.fxbwd.com\u002F128234.html","上海市农业科学院选育的\"中旱3号\"是中国南方稻区第一个通过国家审定的旱稻品种，被指定为国家旱稻品种区域试验南方稻区唯一的对照品种；\"沪旱3号\"是中国南方稻区第一个通过国家审定的粳型旱稻品种。目前累计推广面积已达534.96余万亩，增产稻谷3亿公斤，节约水资源16亿吨，实现经济效益5.89亿元。在一些种植区域，节水可达50%。","![Image 1](https:\u002F\u002Fwww.fxbwd.com\u002Fstatic\u002Ffx\u002Fimages\u002Ffanhui.png)\n\n![Image 2](https:\u002F\u002Fwww.fxbwd.com\u002Fstatic\u002Ffx\u002Fimages\u002Flogosmall.png)\n\n![Image 3](https:\u002F\u002Fwww.fxbwd.com\u002Fstatic\u002Ffx\u002Fimages\u002Fsearch.png)\n\n*   [首页](https:\u002F\u002Fwww.fxbwd.com\u002F)\n*   [资讯](https:\u002F\u002Fwww.fxbwd.com\u002Fzixun\u002F \"资讯\")\n*   [政策](https:\u002F\u002Fwww.fxbwd.com\u002Fzhengce\u002F \"政策\")\n*   [农机](https:\u002F\u002Fwww.fxbwd.com\u002Fnongji\u002F \"农机\")\n*   [化肥](https:\u002F\u002Fwww.fxbwd.com\u002Fhuafei\u002F \"化肥\")\n*   [农药](https:\u002F\u002Fwww.fxbwd.com\u002Fnongyao\u002F \"农药\")\n*   [花生价格](https:\u002F\u002Fwww.fxbwd.com\u002Fhuashengjiage\u002F \"花生价格\")\n*   [玉米价格](https:\u002F\u002Fwww.fxbwd.com\u002Fyumijiage\u002F \"玉米价格\")\n*   [猪价格](https:\u002F\u002Fwww.fxbwd.com\u002Fzhujiage\u002F \"猪价格\")\n*   [棉花价格](https:\u002F\u002Fwww.fxbwd.com\u002Fmianhuajiage\u002F \"棉花价格\")\n*   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09月07日报导。\n\n上海市农业科学院16日宣布，上海农业科学家已育成南方稻区惟一的旱稻区域试验对照品种和南方稻区第一个通过国家审定的粳型旱稻品种，并建成国内最完善的水稻抗旱性鉴定和评价体系及种质资源平台。上海已成为全国水稻节水抗旱研究的主要基地。\n\n据上海市农科院副院长顾晓君介绍，此间科学家选育出的“中旱3号”是中国南方稻区第一个通过国家审定的旱稻品种，被指定为国家旱稻品种区域试验南方稻区惟一的对照品种，也就是说“今后任何新选育的旱稻品种的抗旱性评价都要以‘中旱3号’为标准”。上海选育的“沪旱3号”是中国南方稻区的第一个通过国家审定的粳型旱稻品种；“沪旱7号”也已通过上海的审定。\n\n干旱已成为中国粮食减产的主要原因。水稻生产用水占中国全部农业用水量的七成，消耗了中国总用水量的一半左右。发掘节水抗旱种质资源，培育和推广节水抗旱稻，是保障中国粮食安全、缓解水资源危机的主要出路。\n\n上海旱稻研究项目已育成的3个通过审定的节水抗旱稻品种，已在全国大面积推广种植，主要分布在广西、湖北、浙江、江西、江苏、山东、河北等省的干旱地区。目前累计推广面积已达534．96余万亩，增产稻谷3亿公斤，节约水资源16亿吨，实现经济效益5．89亿元。与抗旱稻相配套的节水栽培技术也在趋于成熟，在一些种植区域，节水可达50％，而且还可在坡耕地实现水土保持，减少近七成的泥沙流失。\n\n据了解，上海还在国内首次建立具有国际先进水平的水稻抗旱性鉴定与评价体系，形成“土壤水分梯度鉴定法”，实现在田间条件下进行全生育期的抗旱性准确鉴定。目前这一鉴定平台已成农业部指定的平台，向国内外开放使用。同时这一技术体系还筛选出86份节水抗旱种质资源，获得一批综合性状较为优良的种质，向国内其它研究单位提供各类节水抗旱资源千余份次，广泛应用于各地旱稻育种和研究之中。\n\n在国家“863”计划、上海科技攻关计划及美国洛克菲勒基金会的长期资助下，上海旱稻研究项目已取得突破性成果。上海成为中国旱稻研究的主要中心。旱稻的推广已形成节水、节能、减污和水土保持四大经济社会效应。\n\n本站推荐: 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\"分享到印象笔记\")[](https:\u002F\u002Fwww.fxbwd.com\u002F128234.html#)","FX农业网2026年9月5日",{"impact":130,"substance":29,"depth":31,"authority":24,"freshness":186,"relevant":33,"comment":504},"报道了上海育成节水抗旱稻标准品种并累计推广超500万亩，具有全国性产业影响，数据详实，来源为行业媒体，时效性尚可。",[506],{"name":502,"url":499},[279,508,509],"品种推广","节水抗旱稻",{"code":511,"slug":512,"name":513,"path":514,"parent_code":515},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":518},{"candidates":519,"agricultural_evidence":25},[520,525,529],{"code":511,"score":157,"supported":25,"title":521,"abstract":522,"tags":524},[],[523],"水资源",[],{"code":290,"score":118,"supported":51,"title":526,"abstract":527,"tags":528},[],[],[508,279],{"code":284,"score":33,"supported":51,"title":530,"abstract":531,"tags":532},[],[],[509],[534,535],"节水抗旱稻 品种推广 种业振兴","节水抗旱稻 品种推广","节水抗旱稻品种推广种业振兴-1802","2026-09-07T00:04:39.570853Z",{"id":539,"title":540,"url":541,"summary":542,"summary_zh":543,"content":20,"source_name":544,"source_url":541,"published_at":545,"category":127,"cover_url":20,"hotness":24,"is_selected":25,"score":466,"score_detail":546,"sources":548,"tags":550,"primary_topic":554,"secondary_topics":555,"classification":556,"search_phrases":581,"slug":584,"view_count":63,"doi":585,"paper":586,"created_at":607},1764,"Human-Centric, AI-Enabled Concurrent Engineering: Towards a cross-domain framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jii.2026.101192","The convergence of Artificial Intelligence (AI) and Concurrent Engineering (CE) has begun to reshape how industrial systems are designed, validated, and operated. We argue that the next, and arguably more consequential, step is to embed this convergence inside a coherent Industrial Information Integration (III) substrate that is explicitly human-centric and that generalises beyond the classical manufacturing core. This paper develops such a framework. We propose the H-AICE architecture (Human-centric AI-enabled Concurrent Engineering), which couples (i) a five-dimensional taxonomy of AI interventions in CE, (ii) a time-dependent overall-risk model Roverall(t) that integrates internal and external risk along the design lifecycle, and (iii) an integration layer based on shared ontologies, knowledge graphs, and digital twins. The framework is then instantiated and critically discussed in six application scenarios that exhibit very different data regimes, regulatory contexts, and human stakes: precision agriculture (Agriculture 5.0), biotechnology and life sciences, climate-aware industrial sustainability, sustainable forestry (Forestry 5.0), medical device development, and livestock breeding. We show that the same III primitives—data quality, semantic interoperability, explainable models, and human-in-the-loop oversight—recur across all six scenarios, and that the principal failure modes are organisational rather than algorithmic. We conclude with a research agenda for human-centric, AI-enabled CE that emphasises explainability, governance, and sector-specific certification, and that positions III as a load-bearing rather than a cosmetic discipline.","人工智能（AI）与并行工程（CE）的融合已开始重塑工业系统的设计、验证与运行方式。我们认为，下一步——或许也是更具深远意义的一步——是将这种融合嵌入一个连贯的工业信息集成（III）基底之中，该基底明确以人为中心，并超越传统制造业核心进行泛化。本文构建了这样一个框架。我们提出H-AICE架构（以人为中心的AI赋能并行工程），该架构结合了（i）AI在CE中干预措施的五维分类法，（ii）一个随时间变化的总体风险模型Roverall(t)，该模型沿设计生命周期整合内部与外部风险，以及（iii）基于共享本体、知识图谱和数字孪生的集成层。随后，该框架在六个应用场景中得到实例化并加以批判性讨论，这些场景展现出截然不同的数据体制、监管环境及人类利害关系：精准农业（农业5.0）、生物技术与生命科学、气候感知型工业可持续性、可持续林业（林业5.0）、医疗器械开发以及畜牧育种。我们表明，相同的III原语——数据质量、语义互操作性、可解释模型及人在回路监督——在全部六个场景中反复出现，且主要失效模式属于组织层面而非算法层面。最后，我们提出一个以人为中心的AI赋能CE研究议程，该议程强调可解释性、治理机制及行业特定认证，并将III定位为一门承重学科而非装饰性学科。","Journal of Industrial Information Integration","2026-09-03T00:00:00Z",{"impact":131,"substance":29,"depth":30,"authority":132,"freshness":57,"relevant":33,"comment":547},"提出跨领域人本AI并发工程框架，覆盖农业5.0等场景，具前瞻性与方法论价值。",[549],{"name":544,"url":541},[551,137,370,552,553],"数字乡村","知识图谱","农业5.0",{"code":382,"slug":383,"name":384,"path":385,"parent_code":145},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":557},{"candidates":558,"agricultural_evidence":25},[559,566,572,576],{"code":141,"score":24,"supported":25,"title":560,"abstract":561,"tags":565},[],[562,563,564],"精准农业","数字孪生","precision agriculture",[137],{"code":437,"score":229,"supported":25,"title":567,"abstract":568,"tags":571},[],[569,570],"畜牧","livestock",[],{"code":382,"score":57,"supported":25,"title":573,"abstract":574,"tags":575},[],[398],[370,552],{"code":577,"score":33,"supported":51,"title":578,"abstract":579,"tags":580},"I6",[],[],[551],[582,583],"农业人工智能 数字乡村 智慧农业 知识图谱","农业人工智能 数字乡村","农业人工智能数字乡村智慧农业知识图谱-1764","10.1016\u002Fj.jii.2026.101192",{"doi":585,"openalex_id":587,"authors":588,"venue":544,"cited_by_count":168,"oa_url":598,"card":599,"direction":605,"ingested_from":606},"W7167326837",[589,592,595],{"name":590,"orcid":591},"Andreas Holzinger","https:\u002F\u002Forcid.org\u002F0000-0002-6786-5194",{"name":593,"orcid":594},"Heimo Müller","https:\u002F\u002Forcid.org\u002F0000-0002-9691-4872",{"name":596,"orcid":597},"Josip Stjepandić","https:\u002F\u002Forcid.org\u002F0000-0003-0877-3799","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2452414X26001342\u002Fpdf",{"tldr":600,"method":601,"finding":602,"direction":603,"opportunity":604},"提出人本AI并发工程框架，跨领域应用于农业等六场景。","提出H-AICE架构，结合AI干预分类、风险模型、本体与数字孪生。","跨场景共性为数据质量、语义互操作、可解释模型和人机协同，主要失败模式是组织性的。","其他","农业5.0场景中，可研究人机协同决策与组织治理对AI采纳的影响，以及可解释模型在精准农业中的实际应用。","智慧农业 \u002F 农业物联网","openalex","2026-09-06T23:30:12.843367Z",{"id":609,"title":610,"url":611,"summary":612,"summary_zh":613,"content":20,"source_name":614,"source_url":611,"published_at":126,"category":127,"cover_url":20,"hotness":24,"is_selected":25,"score":466,"score_detail":615,"sources":617,"tags":619,"primary_topic":622,"secondary_topics":623,"classification":624,"search_phrases":636,"slug":639,"view_count":273,"doi":640,"paper":641,"created_at":661},1760,"RAVA: a robust recurrent active vision agent for plant disease diagnosis under severe environmental noise","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67160-2","Abstract Deep learning models have achieved near-perfect accuracy in plant disease classification within controlled laboratory settings; however, their deployment in real-world agricultural environments is severely hindered by the “deployment gap”—a critical vulnerability to environmental corruptions such as sensor noise, motion blur, and occlusion. To bridge this gap, we propose the Recurrent Active Vision Agent (RAVA), formulating the disease detection task as a Partially Observable Markov Decision Process (POMDP). Unlike passive Convolutional Neural Networks (CNNs) that process images globally, RAVA mimics the active inspection behavior of human agronomists. Our architecture integrates a lightweight ResNet-18 backbone with a Recurrent Neural Network (RNN) and a Spatial Transformer Network (STN). Driven by Proximal Policy Optimization (PPO), the agent learns a sequential policy to intelligently navigate and zoom in on informative “glimpses,” effectively bypassing background clutter. To stabilize the reinforcement learning process and enforce noise-invariant feature representations, we introduce a hybrid objective incorporating Supervised Contrastive Learning (SupCon). Comprehensive experiments on a combined PlantVillage and PlantDoc dataset demonstrate RAVA’s overwhelming superiority under extreme conditions. In a “Severe Degradation” stress test, standard ResNet-50 accuracy collapses to 26.3%, whereas our active agent maintains a robust 77.0%. Under extreme noise and occlusion, RAVA preserves 54.6% accuracy compared to the baseline’s 17.2%. Notably, this resilience is achieved with merely ∼12M parameters—significantly fewer than large-scale Vision Transformers—proving that active visual attention, coupled with contrastive learning, offers a computationally efficient and highly robust pathway for field-ready precision agriculture.","深度学习模型在受控实验室环境中进行植物病害分类时已能达到近乎完美的准确率，然而，它们在真实农业环境中的部署却因“部署鸿沟”而严重受阻——即对传感器噪声、运动模糊和遮挡等环境干扰存在关键脆弱性。为弥合这一鸿沟，我们提出了循环主动视觉智能体（RAVA），将病害检测任务构建为部分可观测马尔可夫决策过程（POMDP）。与被动地全局处理图像的卷积神经网络（CNNs）不同，RAVA模拟了人类农学家的主动检查行为。我们的架构将轻量级ResNet-18骨干网络与循环神经网络（RNN）及空间变换网络（STN）相集成。在近端策略优化（PPO）的驱动下，该智能体学习一种序列策略，以智能地导航并放大信息丰富的“瞥视”区域，从而有效避开背景杂乱干扰。为稳定强化学习过程并强制实现噪声不变的特征表示，我们引入了一种融合监督对比学习（SupCon）的混合目标函数。在结合PlantVillage和PlantDoc数据集的综合实验中，RAVA在极端条件下展现出压倒性优势。在“严重退化”压力测试中，标准ResNet-50的准确率骤降至26.3%，而我们的主动智能体则保持了稳健的77.0%。在极端噪声和遮挡条件下，RAVA保持了54.6%的准确率，而基线模型仅为17.2%。值得注意的是，这种鲁棒性仅以约1200万参数即可实现——远少于大规模视觉Transformer——这证明了主动视觉注意力结合对比学习，为面向田间部署的精准农业提供了一条计算高效且高度稳健的路径。","Scientific Reports",{"impact":30,"substance":29,"depth":131,"authority":105,"freshness":186,"relevant":33,"comment":616},"提出RAVA主动视觉模型，显著提升真实环境下植物病害诊断鲁棒性，方法新颖且实验扎实。",[618],{"name":614,"url":611},[137,370,620,621],"深度学习","植物病害",{"code":382,"slug":383,"name":384,"path":385,"parent_code":145},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":625},{"candidates":626,"agricultural_evidence":25},[627,632],{"code":382,"score":273,"supported":25,"title":628,"abstract":629,"tags":631},[],[620,630],"deep learning",[370,620],{"code":141,"score":186,"supported":25,"title":633,"abstract":634,"tags":635},[],[562,564],[137],[637,638],"农业人工智能 智慧农业 植物病害 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业植物病害深度学习-1760","10.1038\u002Fs41598-026-67160-2",{"doi":640,"openalex_id":642,"authors":643,"venue":614,"cited_by_count":168,"oa_url":655,"card":656,"direction":605,"ingested_from":606},"W7208713425",[644,646,648,651,653],{"name":645,"orcid":20},"Nima Saeedi",{"name":647,"orcid":20},"Sana Gholinavaz",{"name":649,"orcid":650},"Sina Samadi Gharehveran","https:\u002F\u002Forcid.org\u002F0000-0001-5442-2485",{"name":652,"orcid":20},"Kimia Shirini",{"name":654,"orcid":20},"Adel Taheri Hajivand","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67160-2_reference.pdf",{"tldr":657,"method":658,"finding":659,"direction":415,"opportunity":660},"提出RAVA，一种循环主动视觉智能体，通过主动观察应对环境噪声，提升植物病害诊断鲁棒性。","结合ResNet-18、RNN、STN，用PPO训练主动策略，并加入监督对比学习","在严重退化下，RAVA准确率77.0%，远超ResNet-50的26.3%，且参数仅约12M。","可探索将主动视觉与对比学习结合，应用于其他农业任务，如害虫检测或果实计数，在复杂田间环境下提升鲁棒性。","2026-09-06T23:30:08.817010Z",{"id":663,"title":664,"url":665,"summary":666,"summary_zh":20,"content":20,"source_name":667,"source_url":20,"published_at":324,"category":127,"cover_url":20,"hotness":24,"is_selected":51,"score":668,"score_detail":669,"sources":672,"tags":674,"primary_topic":676,"secondary_topics":677,"classification":678,"search_phrases":689,"slug":691,"view_count":118,"doi":20,"paper":692,"created_at":699},1814,"Agentic, Autonomous and Explainable AI in Smart Agriculture: A Systematic Literature Review（MDPI Agronomy 2026, 16(17), 1690）","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F17\u002F1690","依据PRISMA 2020，从Web of Science Core Collection、Scopus和IEEE Xplore检索2016—2026年相关同行评议论文与会议论文共2255篇，筛选322项研究纳入分析。XAI占主导（69.6%），自主（14.0%）与代理式（12.1%）设计较少，仅4.3%结合自主与可解释性。病害检测为最主要应用（33.5%）。78.9%系统仍处于感知\u002F决策支持层面，17.4%经过田间验证，4.3%经过农艺学解释验证。证据揭示了可解释性—自主性鸿沟及田间验证与可重复性差距。","MDPI Agronomy 2026, 16(17)",77,{"impact":30,"substance":29,"depth":670,"authority":85,"freshness":57,"relevant":33,"comment":671},19,"系统综述揭示可解释性与自主性鸿沟，对智慧农业AI发展方向有参考价值。",[673],{"name":667,"url":665},[137,370,371,675],"可解释AI",{"code":141,"slug":142,"name":143,"path":144,"parent_code":145},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":679},{"candidates":680,"agricultural_evidence":25},[681,685],{"code":141,"score":32,"supported":25,"title":682,"abstract":683,"tags":684},[153],[],[137],{"code":382,"score":118,"supported":51,"title":686,"abstract":687,"tags":688},[],[],[370,675],[690,638],"农业人工智能 智慧农业 系统综述 可解释AI","农业人工智能智慧农业系统综述可解释AI-1814",{"doi":20,"openalex_id":20,"authors":693,"venue":20,"cited_by_count":168,"oa_url":20,"card":694,"direction":415,"ingested_from":175},[],{"tldr":695,"method":696,"finding":697,"direction":415,"opportunity":698},"系统综述2016-2026年智慧农业中代理式、自主与可解释AI研究，揭示可解释性主导、自主性不足及验","PRISMA 2020系统综述，检索三大数据库，筛选322项研究进行统计分析。","XAI占69.6%，自主与代理式少，仅4.3%结合自主与可解释性；78.9%系统停留在感知\u002F决策层面","可解释性与自主性结合不足，且田间验证少，可探索高自主性且可解释的农业AI系统及其实地验证方法。","2026-09-07T00:04:41.386089Z",{"id":701,"title":702,"url":703,"summary":704,"summary_zh":20,"content":20,"source_name":125,"source_url":20,"published_at":705,"category":127,"cover_url":20,"hotness":24,"is_selected":51,"score":668,"score_detail":706,"sources":708,"tags":710,"primary_topic":715,"secondary_topics":716,"classification":717,"search_phrases":747,"slug":750,"view_count":118,"doi":20,"paper":751,"created_at":758},1812,"Adoption and Challenges of Digital and Intelligent Technologies in Agriculture: Evidence from Two Representative Household Surveys（《智慧农业（中英文）》2026, 8(3): 51-66）","https:\u002F\u002Fagris.fao.org\u002Fsearch\u002Fen\u002Frecords\u002F6a96d27a8a202ccda1b64993","基于中国农业农村发展调查与2023年主粮产区农户调查两次代表性微观数据，对数字智能技术（DITs）在中国农业中的采纳与挑战进行评估。结果显示：智能手机在主要农业决策者中渗透率达76%，但WeChat在农业生产销售中应用有限，约三分之二受访者很少或从不关注农业相关公众号；约78%受访者很少或偶尔通过智能手机获取农业政策信息；2023年农村家庭网购率约52%，但仅0.32%通过线上销售农产品；嵌入式技术（无人机\u002F北斗）采纳更高，9省数据显示2023年8%农户使用无人机植保。","2026-09-01T00:00:00Z",{"impact":131,"substance":29,"depth":30,"authority":132,"freshness":157,"relevant":33,"comment":707},"基于全国性微观调查数据，系统评估数字智能技术在农业中的采纳现状与挑战，数据详实，结论可靠，对智慧农业政策制定具有参考价值。",[709],{"name":125,"url":703},[711,137,712,713,714],"农村电商","无人机植保","数字技术采纳","农户调查",{"code":339,"slug":340,"name":341,"path":342,"parent_code":294},[],{"status":50,"locked":51,"version":52,"method":53,"evidence":718},{"candidates":719,"agricultural_evidence":25},[720,724,729,734,738,742],{"code":141,"score":32,"supported":25,"title":721,"abstract":722,"tags":723},[137],[],[137],{"code":339,"score":63,"supported":25,"title":725,"abstract":726,"tags":728},[],[727],"植保",[712],{"code":95,"score":157,"supported":25,"title":730,"abstract":731,"tags":733},[],[732],"农村发展",[],{"code":62,"score":157,"supported":25,"title":735,"abstract":736,"tags":737},[],[160],[],{"code":211,"score":33,"supported":51,"title":739,"abstract":740,"tags":741},[],[],[711],{"code":743,"score":33,"supported":51,"title":744,"abstract":745,"tags":746},"G2",[],[],[714],[748,749],"数字技术采纳 无人机植保 农户调查 农村电商","数字技术采纳 无人机植保","数字技术采纳无人机植保农户调查农村电商-1812",{"doi":20,"openalex_id":20,"authors":752,"venue":20,"cited_by_count":168,"oa_url":20,"card":753,"direction":173,"ingested_from":175},[],{"tldr":754,"method":755,"finding":756,"direction":173,"opportunity":757},"基于两次全国调查，评估中国农户对数字智能技术的采纳现状与挑战。","利用两次代表性农户微观调查数据，进行描述性统计分析。","智能手机渗透率高但农业应用有限，线上销售率极低，无人机采纳率8%。","可探究农户数字技术采纳的障碍因素，如信息获取渠道、数字素养及市场接入等。","2026-09-07T00:04:41.256301Z"]