[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2563":3,"related-2563":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":75,"slug":78,"view_count":65,"doi":8,"paper":8,"created_at":79},2563,"中国农业科学院 AI赋能农产品贸易跨境有害生物风险管控 植保所登Artificial Intelligence in Agriculture","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F6e1f3ee4b85d402c9b490a571ade9af0.htm","植保所农业入侵生物预防与监控创新团队利用人工智能技术有效破解跨境有害生物多阶段风险评估与管控决策衔接脱节难题，相关研究成果发表在农业人工智能期刊上。研究团队首次解析了柑橘害虫随跨境贸易传入的243条风险路径和98个关键风险节点，将多阶段定量风险评估信息与大语言模型等生成式人工智能相结合，构建了证据可追溯、结论可解释的智能风险研判与辅助决策应用系统。",null,"[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 2](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 3](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fcaas.cn\u002Findex.htm)\n\n*   [组织机构](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Findex.htm)[走进中国农科院](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fzjnky\u002Findex.htm)[寄语](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fyczc\u002Findex.htm)[院章程](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fzgnykxyzc\u002Findex.htm)[院领导](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fyld\u002Findex.htm)[历任领导](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Flrld\u002Findex.htm)[组织架构](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fzzjg\u002Findex.htm)[院机关](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fyjg\u002Findex.htm)[院属单位](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fysdw\u002Findex.htm)[区域创新中心](https:\u002F\u002Fcaas.cn\u002Fzzjg\u002Fqycxzx\u002Findex.htm) \n*   [新闻中心](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Findex.htm)[图片新闻](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Ftpxw\u002Findex.htm)[时政要闻](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fszyw\u002Findex.htm)[农科要闻](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fnkyw\u002Findex.htm)[科研活动](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Findex.htm)[三农一线](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fhzdznew\u002Findex.htm)[媒体报道](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Findex.htm)[视频农科](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fspnk\u002Findex.htm)[政务新媒体矩阵](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fzwxmtjz\u002Findex.htm) \n*   [科学研究](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Findex.htm)[重大成果](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fzdcgzs\u002Findex.htm)[科研进展](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fkyjz\u002Findex.htm)[科技创新](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fkjcx\u002Findex.htm)[科技奖励](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fkjjl\u002Findex.htm)[农业高端智库](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fnygdzk\u002Findex.htm)[中国农科院重大科学发现](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fzgnkyzdkxfx\u002Findex.htm)[中国农科院重大产品创制、重大技术突破和重大智库报告](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fjstpzkbg\u002Findex.htm)[中国农科院重大科技任务布局](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fzgnkyzdkjrwbj\u002Findex.htm)[中国农科院十大科研进展](https:\u002F\u002Fcaas.cn\u002Fkxyj\u002Fzgnkysdkyjz\u002Findex.htm) \n*   [科技支撑](https:\u002F\u002Fcaas.cn\u002Fkjzc\u002Findex.htm)[主推成果](https:\u002F\u002Fcaas.cn\u002Fkjzc\u002Fcgzhfw\u002Findex.htm)[产业专家团](https:\u002F\u002Fcaas.cn\u002Fkjzc\u002Fcyzjt\u002Findex.htm) \n*   [人才教育](https:\u002F\u002Fcaas.cn\u002Frcjy\u002Findex.htm)[人才队伍概况](https:\u002F\u002Fcaas.cn\u002Frcjy\u002Frcgk\u002Findex.htm)[人才工作概述](https:\u002F\u002Fcaas.cn\u002Frcjy\u002Frcgzgs\u002Findex.htm)[专家学者](https:\u002F\u002Fcaas.cn\u002Frcjy\u002Fzjxz\u002Findex.htm)[人才动态](https:\u002F\u002Fcaas.cn\u002Frcjy\u002Frcdtnew\u002Findex.htm)[研究生教育](https:\u002F\u002Fgs.caas.cn\u002F)[中国农科院人才招聘网](https:\u002F\u002Fzhaopin.caas.cn\u002F#\u002F) \n*   [国际合作](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Findex.htm)[总体概况](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Fgjhzgk\u002Findex.htm)[合作伙伴](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Fhzhb\u002Findex.htm)[合作平台](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Fhzpt\u002Findex.htm)[合作机制](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Fhzzz\u002Findex.htm)[国际农业科学计划](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Fgjnykxjh\u002Findex.htm)[中国农科院年报](https:\u002F\u002Fcaas.cn\u002Fgjhz\u002Fzgnkynb\u002Findex.htm) \n*   [平台基地](https:\u002F\u002Fcaas.cn\u002Fptjd\u002Findex.htm)[资源概况](https:\u002F\u002Fcaas.cn\u002Fptjd\u002Fzygk\u002Findex.htm)[平台](https:\u002F\u002Fcaas.cn\u002Fptjd\u002Fptq\u002Findex.htm)[基地](https:\u002F\u002Fcaas.cn\u002Fptjd\u002Fjd\u002Findex.htm) \n*   [信息服务](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Findex.htm)[资源服务](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Fzyfw\u002Findex.htm)[数字农科院](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Fsznky\u002Findex.htm)[农业科研信息化典型案例](https:\u002F\u002Fwww.caas.cn\u002Fzt\u002Fxxhbp_0\u002Fnykyxxhdxa_0\u002Findex.htm) \n*   [党建文化](https:\u002F\u002Fdw.caas.cn\u002F)\n\n![Image 4](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Flogo_s03.png)[](https:\u002F\u002Fcaas.cn\u002Findex.htm)[](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Findex.htm)\n\n[返回门户首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)\n\n官方微信 \n\n![Image 5](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 6](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n*    [院网首页](https:\u002F\u002Fcaas.cn\u002Findex.htm) \n*    [图片新闻](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Ftpxw\u002Findex.htm) \n*    [时政要闻](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fszyw\u002Findex.htm) \n*    [农科要闻](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fnkyw\u002Findex.htm) \n*    [科研活动](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Findex.htm) \n*    [三农一线](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fhzdznew\u002Findex.htm) \n*    [媒体报道](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Findex.htm) \n*    [视频农科](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fspnk\u002Findex.htm) \n*    [政务新媒体矩阵](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fzwxmtjz\u002Findex.htm) \n\n[返回首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)[English](https:\u002F\u002Fwww.caas.cn\u002Fen\u002F)\n\n[首页](https:\u002F\u002Fcaas.cn\u002Findex.htm)-[新闻中心](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Findex.htm)-[媒体报道](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Findex.htm)\n\n分享到\n\n### [中国科学报] AI赋能农产品贸易跨境有害生物风险管控\n\n发布时间：2026-09-11 _|_ 来源： 中国科学报 _|_ 作者：李晨,郭建英\n\n字体[小](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F6e1f3ee4b85d402c9b490a571ade9af0.htm)[中](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F6e1f3ee4b85d402c9b490a571ade9af0.htm)[大](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F6e1f3ee4b85d402c9b490a571ade9af0.htm)\n\n近日，中国农业科学院[植物保护研究所](http:\u002F\u002Fipp.caas.cn\u002F)农业入侵生物预防与监控创新团队利用人工智能技术，有效破解跨境有害生物多阶段风险评估与管控决策衔接脱节难题。相关研究成果发表在《农业人工智能》（_**Artificial Intelligence in Agriculture**_）上。\n\n随着全球贸易持续加速，柑橘等重要果品进口量攀升，植物有害生物随贸易“搭车”入境的风险日益加剧。然而，跨境植物有害生物伴随果品贸易“传入—定殖—扩散”的多阶段风险尚不明确。\n\n研究团队首次解析了柑橘害虫随跨境贸易传入的243条风险路径和98个关键风险节点，精准锁定介壳虫、实蝇等高风险类群及其关键来源地与传入地区，发现我国南方柑橘主产区面临多种害虫共同传入与定殖的复合威胁。\n\n在此基础上，该研究将多阶段定量风险评估信息等数据与大语言模型等生成式人工智能相结合，突破通用大语言模型缺乏领域知识的固有局限，构建了证据可追溯、结论可解释的智能风险研判与辅助决策应用系统，为跨境有害生物风险管控提供科技支撑。\n\n该研究得到国家重点研发计划等项目支持。\n\n相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.07.004\n\n(单位: 中国农业科学院植物保护研究所)\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [[科学网] 弯刺蔷薇缘何成为月季的抗寒育种亲本](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F71f0fd687f5a4cbcb5d8e14dd8ca5ab7.htm)2026-09-11  \n*   [[科学网] 高效引导编辑系统创制双抗黄瓜新种质](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F918aff6c268a4196880a4514b117bf34.htm)2026-09-11  \n*   [[总台中国乡村之声]《乡村FM》东北粮仓的秋粮冲刺](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F2a01860de3d54f759501beb9fb666fee.htm)2026-09-11  \n*   [[经济日报]发展现代设施农业保障多元供给](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fc856990f152e4152a93934f2322c9705.htm)2026-09-11  \n*   [[光明日报]植物免组织培养基因编辑技术前沿进展如何？中外科学家团队携手作答](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F34fa785eabd3421a80d8807908352d0d.htm)2026-09-11  \n*   [低剂量甲维盐可持续抑制棉蓟马生殖](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd590871ec8a24b02a5eeab6d80107ad8.htm)2026-09-11  \n*   [我国首台纯电动小型稻麦收割机投入使用](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F60ad6c661fd5410fbbf8aef86538b770.htm)2026-09-11  \n*   [高油酸抗病“中花29”创长江流域丘陵山地花生高产纪录](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fa7a4c6735ba149f197aa5a5ecc8d5b11.htm)2026-09-11  \n*   [[农民日报]我国科学家构建油菜根肿病防控新策略](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F599fe991382f4e9bb3dd60b1ac10fbc2.htm)2026-09-10  \n*   [[科技日报]油料所最新研究构建油菜根肿病防控新策略](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F8cacd3422bd946ed86809e48a356ec9a.htm)2026-09-10  \n\n*   [](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fspnk\u002Findex.htm)[视频农科](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fspnk\u002Findex.htm) \n*   [](https:\u002F\u002Fcaas.cn\u002Ftzgg\u002Findex.htm)[通知公告](https:\u002F\u002Fcaas.cn\u002Ftzgg\u002Findex.htm) \n*   [](https:\u002F\u002Fcaas.cn\u002Findex.htm?mao=special_attention)[特别关注](https:\u002F\u002Fcaas.cn\u002Findex.htm?mao=special_attention) \n*   [](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fzwxmtjz\u002Findex.htm)[政务新媒体矩阵](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fzwxmtjz\u002Findex.htm) \n\n*   [![Image 7](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2025-12\u002Feebc10f934d74b978fccd8c654f47577.jpg)](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Fzyfw\u002Fcqk\u002Findex.htm)\n*   [![Image 8](https:\u002F\u002Fcaas.cn\u002Fimages\u002Fcontent\u002F2022-02\u002Faefa6458187845e9a3aeb18472ae9e7f.jpg)](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Fzyfw\u002Fcqk\u002Findex.htm)\n*   [![Image 9](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F5c35b429611f44aa927c9b2698ff91e5.png)](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Fzyfw\u002Fcqk\u002Findex.htm)\n*   [![Image 10](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F2ec7c5b598424a0b801fd6fde5956562.png)](https:\u002F\u002Fcaas.cn\u002Fxxfw\u002Fzyfw\u002Fcqk\u002Findex.htm)\n\n*   [网站地图](https:\u002F\u002Fcaas.cn\u002Fwzdt\u002Findex.htm)\n*   [联系我们](https:\u002F\u002Fcaas.cn\u002Flxwm\u002Findex.htm)\n\n[我要捐赠](https:\u002F\u002Fjjh.caas.cn\u002Fjzzn\u002Fjzfs\u002Findex.htm)\n\n主办：中国农业科学院 承办:中国农业科学院农业信息研究所 地址:北京市海淀区中关村南大街12号 邮编:100081\n\nCopyright@中国农业科学院 京ICP备10039560号-5[_![Image 11](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Ficon\\_ba.png)_ 京公网安备11010802048565号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802048565)","中国农业科学院 2026-09","2026-09-13T01:00:00Z","报道",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},22,16,15,8,1,"国家级科研机构在AI农业期刊发表跨境有害生物风险管控成果，专业性与权威性突出，但正文信息有限，属细分领域科研进展。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","植物保护","智慧植保","跨境有害生物","农产品贸易",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,63,70],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},12,true,[54],"artificial intelligence",[26],[26],{"code":39,"score":58,"supported":52,"title":59,"abstract":61,"tags":62},11,[60],"植保",[60],[],{"code":64,"score":65,"supported":52,"title":66,"abstract":67,"tags":69},"B2",3,[],[68],"柑橘",[],{"code":71,"score":21,"supported":14,"title":72,"abstract":73,"tags":74},"E5",[],[],[30],[76,77],"农业人工智能 跨境有害生物 农产品贸易 智慧植保","农业人工智能 跨境有害生物","农业人工智能跨境有害生物农产品贸易智慧植保-2563","2026-09-16T00:03:46.695280Z",{"total":81,"page":21,"page_size":81,"items":82},6,[83,180,303,359,419,482],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":92,"sources":98,"tags":100,"primary_topic":105,"secondary_topics":106,"classification":108,"search_phrases":157,"slug":160,"view_count":161,"doi":162,"paper":163,"created_at":179},2046,"AI-Powered Crop Protection: Transforming Pest Surveillance and Decision-Making Through Digital Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i94503","Crop protection is increasingly challenged by changing pest population dynamics, pesticide resistance and the need for more sustainable agricultural production. Artificial intelligence (AI) and digital agriculture offer complementary tools for improving pest surveillance, forecasting and decision-making through automated detection, continuous monitoring and more precise interventions. This review synthesises the applications of machine learning, deep learning, computer vision, Internet of Things-enabled sensing, smart traps, remote sensing, unmanned aerial vehicles, robotics, predictive analytics and intelligent decision support systems in pest management. These approaches support pest identification, population monitoring, outbreak prediction, risk assessment and site-specific management, thereby strengthening the information base required for integrated pest management. The review also examines emerging technologies, including generative AI, foundation models, digital twins, explainable AI, edge intelligence and autonomous agricultural platforms, in relation to future crop protection systems. Despite rapid technological progress, practical deployment remains constrained by limited high-quality datasets, variable field conditions, model generalisation, interoperability, digital infrastructure, cost and farmer adoption. Effective implementation therefore requires transparent, robust and scalable systems that are validated under diverse agricultural conditions and aligned with ecological pest management principles. Overall, integrating AI-enabled surveillance and decision support with integrated pest management can support more timely, adaptive and resource-efficient crop protection while reducing unnecessary interventions.","作物保护正日益受到害虫种群动态变化、农药抗性以及更可持续农业生产需求的挑战。人工智能（AI）与数字农业通过自动检测、持续监测和更精准的干预措施，为改善害虫监测、预测和决策提供了互补性工具。本文综述了机器学习、深度学习、计算机视觉、物联网传感、智能诱捕器、遥感、无人机、机器人技术、预测分析和智能决策支持系统在害虫管理中的应用。这些方法支持害虫识别、种群监测、暴发预测、风险评估和位点特异性管理，从而强化了有害生物综合治理所需的信息基础。本文还探讨了新兴技术，包括生成式人工智能、基础模型、数字孪生、可解释人工智能、边缘智能和自主农业平台，及其在未来作物保护系统中的前景。尽管技术进展迅速，实际部署仍受限于高质量数据集不足、田间条件多变、模型泛化能力、互操作性、数字基础设施、成本以及农民采纳程度等因素。因此，有效实施需要透明、稳健且可扩展的系统，这些系统应在多样化农业条件下得到验证，并与生态害虫管理原则相一致。总体而言，将人工智能驱动的监测与决策支持同有害生物综合治理相结合，可支持更及时、更具适应性和资源效率更高的作物保护，同时减少不必要的干预。","Journal of Scientific Research and Reports","2026-09-09T00:00:00Z","论文",{"impact":93,"substance":94,"depth":95,"authority":96,"freshness":20,"relevant":21,"comment":97},18,20,17,13,"系统综述AI与数字农业在病虫害监测预警与决策支持中的应用与瓶颈，信息密度高、结论可靠，对智慧植保方向有参考价值，但属综述类论文而非突破性成果，适合进入精选。",[99],{"name":89,"url":86},[101,102,26,103,104],"数字农业","智慧农业","病虫害监测","精准植保",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[107],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":109},{"candidates":110,"agricultural_evidence":52},[111,119,126,137,144,151],{"code":112,"score":113,"supported":52,"title":114,"abstract":116,"tags":118},"I1",19,[115],"digital agriculture",[101,117,115],"数字孪生",[101,102],{"code":39,"score":95,"supported":52,"title":120,"abstract":122,"tags":125},[121],"crop protection",[123,124,121],"农药","pesticide",[],{"code":32,"score":13,"supported":52,"title":127,"abstract":128,"tags":136},[],[129,130,131,132,133,54,134,135],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision","decision support",[26],{"code":138,"score":81,"supported":52,"title":139,"abstract":140,"tags":143},"I3",[],[141,142],"遥感","remote sensing",[],{"code":145,"score":81,"supported":52,"title":146,"abstract":147,"tags":150},"I5",[],[148,149],"物联网","internet of things",[],{"code":152,"score":65,"supported":52,"title":153,"abstract":154,"tags":156},"H8",[],[155],"可持续农业",[],[158,159],"农业人工智能 病虫害监测 数字农业 智慧农业","农业人工智能 病虫害监测","农业人工智能病虫害监测数字农业智慧农业-2046",2,"10.9734\u002Fjsrr\u002F2026\u002Fv32i94503",{"doi":162,"openalex_id":164,"authors":165,"venue":89,"cited_by_count":170,"oa_url":86,"card":171,"direction":177,"ingested_from":178},"W7212081296",[166,168],{"name":167,"orcid":8},"R. Saritha",{"name":169,"orcid":8},"M. Swathi",0,{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"综述AI与数字农业在害虫监测、预测及决策支持中的应用与挑战。","综述机器学习、深度学习、计算机视觉、物联网、遥感、无人机与决策支持系统。","AI可提升害虫识别、暴发预测和精准管理，但受数据、泛化、成本与采纳制约。","农业人工智能与决策模型","可探索生成式AI、数字孪生与边缘智能在田间害虫管理中的可解释、可扩展落地验证。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.692422Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":8,"source_name":186,"source_url":183,"published_at":187,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":191,"tags":193,"primary_topic":197,"secondary_topics":203,"classification":206,"search_phrases":258,"slug":261,"view_count":65,"doi":262,"paper":263,"created_at":302},1325,"Changing pest dynamics and pesticide use in cotton: Challenges and sustainable management","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16195","Cotton (Gossypium spp.) is one of the world’s most important fibre crops, yet its sustainability is increasingly challenged by evolving pest complexes, intensified pesticide use, climate change and climate variability. The evidence indicates current knowledge on pest dynamics, pesticide dependence and ecological interactions in cotton agroecosystems, highlighting the shift from bollworm-dominated pest complexes to the increasing prevalence of secondary pests following the widespread adoption of Bacillus thuringiensis (Bt) cotton. Although Bt (Cry toxin)-based transgenic cotton technology initially reduced insecticide use against the bollworm complex, primarily lepidopteran species, the emergence of resistance among bollworms and the upsurge of certain sucking pests in recent decades have reinstated insecticide dependence for pest management. This has led to ecological imbalance, non-target toxicity, the decline of pollinator populations and natural enemy communities, biodiversity loss, environmental contamination, accumulation of residues in products and yield challenges, in addition to increased production costs. The review emphasises the critical role of integrated pest management (IPM) strategies that integrate biological control, habitat diversification, resistant cultivars and threshold-based pesticide applications to restore ecological stability. Alternative approaches such as botanical pesticides, nanoformulations and plant volatile-mediated pest regulation are highlighted as promising tools for sustainable pest control. Advances in precision agriculture, including artificial intelligence (AI), machine learning and unmanned aerial vehicle (UAV)-based pesticide delivery systems, have the potential to improve pest surveillance and reduce chemical inputs. Supply-chain procurement, certification, traceability and consumer demand can encourage lower-pesticide production, although these mechanisms require fair price incentives to avoid transferring compliance costs to smallholders. Sustainable cotton therefore requires region-specific, climate-resilient, evidence-based IPM supported by extension, market incentives, resistance monitoring and coordinated policy.","棉花（Gossypium spp.）是全球最重要的纤维作物之一，然而其可持续性正日益受到害虫群落演变、农药使用强度增加、气候变化及气候变率等因素的挑战。现有证据表明，当前对棉花农业生态系统中害虫动态、农药依赖及生态互作的认识，凸显了自苏云金芽孢杆菌（Bacillus thuringiensis, Bt）棉花广泛推广以来，害虫群落从以棉铃虫为主向次生害虫日益增多的转变。尽管基于Bt（Cry毒素）的转基因棉花技术最初减少了对棉铃虫复合体（主要为鳞翅目害虫）的杀虫剂使用，但近几十年来棉铃虫抗药性的出现及某些刺吸式害虫的激增，重新导致害虫管理对杀虫剂的依赖。这引发了生态失衡、非靶标毒性、传粉昆虫种群及天敌群落减少、生物多样性丧失、环境污染、产品中残留物积累及产量挑战，同时增加了生产成本。本综述强调了综合害虫管理（IPM）策略的关键作用，该策略整合生物防治、生境多样化、抗性品种及基于阈值的农药施用，以恢复生态稳定性。植物源农药、纳米制剂及植物挥发物介导的害虫调控等替代方法，被强调为可持续害虫防治的有前景工具。精准农业的进展，包括人工智能（AI）、机器学习及基于无人机（UAV）的农药施用系统，有望改善害虫监测并减少化学投入。供应链采购、认证、可追溯性及消费者需求可促进低农药生产，但这些机制需辅以公平价格激励，以避免将合规成本转嫁给小农户。因此，可持续棉花生产需要区域特异性、气候韧性、基于证据的IPM，并辅以推广服务、市场激励、抗性监测及协调政策支持。","Plant Science Today","2026-08-31T00:00:00Z",72,{"impact":93,"substance":17,"depth":93,"authority":51,"freshness":161,"relevant":21,"comment":190},"综述性论文，系统分析棉花害虫动态与农药依赖问题，提出IPM与精准农业等可持续路径，对农业绿色发展有参考价值。",[192],{"name":186,"url":183},[102,26,194,195,196],"棉花","农药减量","病虫害防治",{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[204,205],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":207},{"candidates":208,"agricultural_evidence":52},[209,214,219,223,232,239,246,252],{"code":39,"score":93,"supported":52,"title":210,"abstract":211,"tags":213},[124],[123,212,124],"生物防治",[195],{"code":198,"score":19,"supported":52,"title":215,"abstract":217,"tags":218},[216],"cotton",[194,216],[194],{"code":32,"score":13,"supported":52,"title":220,"abstract":221,"tags":222},[],[130,132,54],[26],{"code":224,"score":225,"supported":52,"title":226,"abstract":227,"tags":231},"H6",9,[],[228,229,230],"生物多样性","农业生态","biodiversity",[],{"code":112,"score":233,"supported":52,"title":234,"abstract":235,"tags":238},7,[],[236,237],"精准农业","precision agriculture",[102],{"code":240,"score":81,"supported":52,"title":241,"abstract":242,"tags":245},"H7",[],[243,244],"气候变化","climate change",[],{"code":247,"score":65,"supported":52,"title":248,"abstract":249,"tags":251},"A4",[],[250],"花生",[],{"code":253,"score":65,"supported":52,"title":254,"abstract":255,"tags":257},"G2",[],[256],"小农户",[],[259,260],"农业人工智能 病虫害防治 农药减量 智慧农业","农业人工智能 病虫害防治","农业人工智能病虫害防治农药减量智慧农业-1325","10.14719\u002Fpst.16195",{"doi":262,"openalex_id":264,"authors":265,"venue":186,"cited_by_count":170,"oa_url":183,"card":296,"direction":175,"ingested_from":178},"W7204806269",[266,269,272,275,278,281,284,287,290,293],{"name":267,"orcid":268},"V Veeragowtham","https:\u002F\u002Forcid.org\u002F0009-0002-9822-4901",{"name":270,"orcid":271},"A. Suganthi","https:\u002F\u002Forcid.org\u002F0000-0001-7013-4459",{"name":273,"orcid":274},"M. Murugan","https:\u002F\u002Forcid.org\u002F0000-0002-7485-1153",{"name":276,"orcid":277},"P. Renukadevi","https:\u002F\u002Forcid.org\u002F0000-0001-9665-1681",{"name":279,"orcid":280},"S. Vellaikumar","https:\u002F\u002Forcid.org\u002F0000-0001-9277-457X",{"name":282,"orcid":283},"Ali Iqra","https:\u002F\u002Forcid.org\u002F0009-0008-6095-9471",{"name":285,"orcid":286},"J Kousika","https:\u002F\u002Forcid.org\u002F0000-0002-2455-5268",{"name":288,"orcid":289},"Sheela Venugopal","https:\u002F\u002Forcid.org\u002F0000-0002-4689-4105",{"name":291,"orcid":292},"Prakash Kolanchi","https:\u002F\u002Forcid.org\u002F0000-0003-1244-199X",{"name":294,"orcid":295},"Murugesh Dharani","https:\u002F\u002Forcid.org\u002F0009-0004-6705-1344",{"tldr":297,"method":298,"finding":299,"direction":300,"opportunity":301},"综述棉花害虫动态与农药使用变化，提出可持续管理策略。","综述文献，分析Bt棉影响、害虫抗性及IPM、精准农业等技术。","Bt棉导致次生害虫上升和抗性，需综合IPM与精准农业减少农药。","农业绿色发展与碳","可研究AI与无人机在棉花害虫监测与精准施药中的实际应用效果及小农采纳机制。","2026-09-01T23:30:34.470501Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":8,"content":308,"source_name":309,"source_url":8,"published_at":310,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":311,"score_detail":312,"sources":314,"tags":316,"primary_topic":320,"secondary_topics":321,"classification":322,"search_phrases":354,"slug":357,"view_count":65,"doi":8,"paper":8,"created_at":358},2471,"广东省农科院植保所与北京爱格瑞等企业开展科企合作交流(9-8)","https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1338205.html","9 月 8 日中国农业大学植保学院成果转化工作站主任兼北京爱格瑞科技有限公司董事长李世盛、广东电信农业农村行业资深总监黄凯豪一行 4 人到广东省农科院植保所开展科企合作交流。植保所党委书记吴寿迁介绍团队建设、科技创新、成果转化等基本情况;李世盛以'爱格瑞科技绿色智慧植保综合解决方案'为题详细介绍了公司在绿色智慧植保领域的技术布局与应用成效;各方围绕水稻和荔枝病虫害智能体研发及科企合作模式进行了深入研讨。后续将依托技术指导、项目合作等方式,深化人工智能技术与植保科技的深度融合。","[![Image 2](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002FIPv6.png)](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1338205.html)\n\n[简体](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1338205.html) | [繁体](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1338205.html) | [EN](http:\u002F\u002Fwww.gdaas.cn\u002Fenglish\u002Findex.html)\n\n![Image 3](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fphone.png)手机版 \n\n![Image 4: 手机版二维码](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fmoblie_ewm.png)\n\n![Image 5](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fwetchat.png)微信 \n\n![Image 6: 手机版二维码](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fwx_ewm.png)\n\n![Image 7](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Flogo.png)\n\n![Image 8](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fsearch_top.png)\n\n![Image 9](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fsearch.png)\n\n*   [首页](https:\u002F\u002Fwww.gdaas.cn\u002F)\n|*   [本院概况](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Findex.html)\n    *   [本院简介](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Fbyjj\u002Findex.html)\n    *   [发展历程](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Flsyg\u002Findex.html)\n    *   [文化标识](http:\u002F\u002Fwww.gdaas.cn\u002Fnkygk\u002Fbyjj\u002Fwhbz\u002Findex.html)\n    *   [现任领导](http:\u002F\u002Fwww.gdaas.cn\u002Fzzjg\u002Fxrld\u002Findex.html)\n    *   [院属单位](http:\u002F\u002Fwww.gdaas.cn\u002Fzzjg\u002Fysdw\u002Findex.html)\n    *   [管理部门](http:\u002F\u002Fwww.gdaas.cn\u002Fzzjg\u002Fglbm\u002Findex.html)\n\n|*   [人才队伍](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Findex.html)\n    *   [知名专家](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fzmzj\u002Findex.html)\n    *   [学科团队](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fxktd\u002Findex.html)\n    *   [博士后工作站](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fbshgzz\u002Findex.html)\n    *   [研究生培养](http:\u002F\u002Fwww.gdaas.cn\u002Frcdwn\u002Fyjspy\u002Findex.html)\n\n|*   [科学研究](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Findex.html)\n    *   [科研进展](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fkyjz\u002Findex.html)\n    *   [学术活动](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fxshd\u002Findex.html)\n    *   [科技成果](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fkjcg\u002Findex.html)\n    *   [科研条件](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fkytj\u002Findex.html)\n    *   [国际合作](http:\u002F\u002Fwww.gdaas.cn\u002Fkxyjn\u002Fgjhz\u002Findex.html)\n\n|*   [服务三农](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Findex.html)\n    *   [科技服务](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Findex.html)\n    *   [院地合作](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fydhz\u002Findex.html)\n    *   [院企合作](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fyqhz\u002Findex.html)\n    *   [主导品种](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fzdpz\u002Findex.html)\n    *   [主推技术](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fztjs\u002Findex.html)\n    *   [成果转化（孵化器）](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fcgzhfhq\u002Findex.html)\n\n|*   [党建群团](http:\u002F\u002Fwww.gdaas.cn\u002Fdjqt\u002Findex.html)\n\n![Image 10](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Fpos.png)[首页](http:\u002F\u002Fwww.gdaas.cn\u002F)>[服务三农](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002F)>[科技服务](http:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002F)\n\n### 植保所与北京爱格瑞科技有限公司等企业开展科企合作交流\n\n时间：2026-09-10 09:54 来源：植保所【字体：大 中 小】\n\n 分享到： \n\n9月8日，中国农业大学植保学院成果转化工作站主任兼北京爱格瑞科技有限公司董事长李世盛、广东电信农业农村行业资深总监黄凯豪等一行4人到植保所开展科企合作交流。植保所党委书记吴寿迁，副所长李振宇、陈深及相关科技人员参加座谈。\n\n座谈会上，吴寿迁从团队建设、科技创新、成果转化等方面介绍了植保所的基本情况；李世盛以“爱格瑞科技绿色智慧植保综合解决方案”为题，详细介绍了公司在绿色智慧植保领域的技术布局与应用成效；黄凯豪介绍了电信在智慧植保推广方面的优势与成效。各方围绕水稻和荔枝病虫害智能体研发及科企合作模式进行了深入研讨。与会人员结合各自研究领域和企业需求，就病虫害智能识别、绿色防控技术集成、成果转化落地等方面进行了深度交流。\n\n吴寿迁表示，后续将依托技术指导、项目合作等方式，深化人工智能技术与植保科技的深度融合，为农业高质量发展注入智慧植保的专业力量。\n\n![Image 11: 植保所与北京爱格瑞科技有限公司等企业开展科企合作交流.jpg](http:\u002F\u002Fwww.gdaas.cn\u002Fimg\u002F0\u002F887\u002F887071\u002F1338205.png)\n\n院属单位\n\n*   [水稻研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdrice\u002F)\n*   [果树研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdfruit\u002F)\n*   [蔬菜研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdvri\u002F)\n*   [作物研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fcrigdaas\u002F)\n*   [植物保护研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdppri\u002F)\n*   [农业质量标准与监测技术研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fnkyjczx\u002F)\n*   [设施农业研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdaasifa\u002F)\n*   [动物科学研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdias\u002F)\n*   [蚕业与农产品加工研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fsrigaas\u002F)\n*   [农业资源与环境研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdiare\u002F)\n*   [动物卫生研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fvmi\u002F)\n*   [农业经济与信息研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fiard\u002F)\n*   [茶叶研究所](https:\u002F\u002Fwww.gdaas.cn\u002Ftea\u002F)\n*   [环境园艺研究所](https:\u002F\u002Fwww.gdaas.cn\u002Fgdflower\u002F)\n*   [农业生物基因研究中心](https:\u002F\u002Fwww.gdaas.cn\u002Fagrogene\u002F)\n*   [《广东农业科学》](https:\u002F\u002Fgdnykx.gdaas.cn\u002F)\n\n政府机构\n\n*   [中国政府网](http:\u002F\u002Fwww.gov.cn\u002F)\n*   [科技部](http:\u002F\u002Fwww.most.gov.cn\u002Findex.htm)\n*   [农业农村部](http:\u002F\u002Fwww.moa.gov.cn\u002F)\n*   [中国科学院](http:\u002F\u002Fwww.cas.cn\u002F)\n*   [中国工程院](http:\u002F\u002Fwww.cae.cn\u002F)\n*   [国家自然科学基金委员会](https:\u002F\u002Fwww.nsfc.gov.cn\u002F)\n*   [广东省人民政府](http:\u002F\u002Fwww.gd.gov.cn\u002F)\n*   [省发展改革委](http:\u002F\u002Fdrc.gd.gov.cn\u002F)\n*   [省教育厅](https:\u002F\u002Fedu.gd.gov.cn\u002F)\n*   [省科技厅](http:\u002F\u002Fgdstc.gd.gov.cn\u002F)\n*   [省财政厅](http:\u002F\u002Fczt.gd.gov.cn\u002F)\n*   [省人力资源社会保障厅](http:\u002F\u002Fhrss.gd.gov.cn\u002F)\n*   [省生态环境厅](https:\u002F\u002Fgdee.gd.gov.cn\u002F)\n*   [省水利厅](http:\u002F\u002Fslt.gd.gov.cn\u002F)\n*   [省农业农村厅](http:\u002F\u002Fdara.gd.gov.cn\u002F)\n*   [省政府外事办公室](http:\u002F\u002Fwww.gdfao.gov.cn\u002F)\n*   [省国资委](http:\u002F\u002Fgzw.gd.gov.cn\u002F)\n*   [省市场监督管理局](http:\u002F\u002Famr.gd.gov.cn\u002F)\n*   [省林业局](http:\u002F\u002Flyj.gd.gov.cn\u002F)\n*   [广东省直机关党建](http:\u002F\u002Fwww.gdszjgdj.org\u002F)\n*   [广州市科学技术局](https:\u002F\u002Fkjj.gz.gov.cn\u002F)\n*   [广州市农业农村局](https:\u002F\u002Fnyncj.gz.gov.cn\u002F)\n\n科研机构\n\n*   [中国农业科学院](https:\u002F\u002Fwww.caas.cn\u002F)\n*   [中国热带农业科学院](http:\u002F\u002Fwww.catas.cn\u002F)\n*   [北京市农林科学院](http:\u002F\u002Fwww.baafs.net.cn\u002F)\n*   [河北省农林科学院](http:\u002F\u002Fwww.hebnky.com\u002F)\n*   [山西省农业科学院](http:\u002F\u002Fwww.sxau.edu.cn\u002F)\n*   [内蒙古农牧业科学院](http:\u002F\u002Fwww.imaaahs.ac.cn\u002F)\n*   [辽宁省农业科学院](https:\u002F\u002Fwww.laas.cn\u002F)\n*   [吉林省农业科学院](http:\u002F\u002Fwww.jaas.com.cn\u002F)\n*   [黑龙江省农业科学院](https:\u002F\u002Fwww.haas.cn\u002F)\n*   [上海市农业科学院](http:\u002F\u002Fwww.saas.sh.cn\u002F)\n*   [江苏省农业科学院](http:\u002F\u002Fwww.jaas.ac.cn\u002F)\n*   [浙江省农业科学院](http:\u002F\u002Fwww.zaas.ac.cn\u002F)\n*   [安徽省农业科学院](http:\u002F\u002Fwww.ahas.org.cn\u002F)\n*   [福建省农业科学院](http:\u002F\u002Fwww.faas.cn\u002F)\n*   [江西省农业科学院](https:\u002F\u002Fwww.jxaas.cn\u002F)\n*   [山东省农业科学院](http:\u002F\u002Fwww.saas.ac.cn\u002F)\n*   [河南省农业科学院](http:\u002F\u002Fwww.hnagri.org.cn\u002F)\n*   [湖北省农业科学院](http:\u002F\u002Fwww.hbaas.ac.cn\u002F)\n*   [湖南省农业科学院](http:\u002F\u002Fwww.hunaas.cn\u002F)\n*   [广西农业科学院](http:\u002F\u002Fwww.gxaas.net\u002F)\n*   [海南省农业科学院](http:\u002F\u002Fwww.hnaas.org.cn\u002F)\n*   [重庆市农业科学院](http:\u002F\u002Fwww.cqaas.cn\u002F)\n*   [四川省农业科学院](http:\u002F\u002Fwww.chinawestagr.com\u002F)\n*   [贵州省农业科学院](http:\u002F\u002Faas.guizhou.gov.cn\u002F)\n*   [云南省农业科学院](http:\u002F\u002Fwww.yaas.org.cn\u002F)\n*   [西藏自治区农牧科学院](https:\u002F\u002Fwww.xzaas.cn\u002F)\n*   [甘肃省农业科学院](https:\u002F\u002Fwww.gsagr.cn\u002F)\n*   [青海省畜牧兽医科学院](https:\u002F\u002Fmky.qhu.edu.cn\u002F)\n*   [宁夏农林科学院](http:\u002F\u002Fwww.nxaas.com.cn\u002F)\n*   [新疆农业科学院](http:\u002F\u002Fwww.xaas.ac.cn\u002F)\n*   [中国农业大学](http:\u002F\u002Fwww.cau.edu.cn\u002F)\n*   [华南农业大学](http:\u002F\u002Fwww.scau.edu.cn\u002F)\n*   [华中农业大学](http:\u002F\u002Fwww.hzau.edu.cn\u002F)\n*   [东北农业大学](https:\u002F\u002Fwww.neau.edu.cn\u002F)\n*   [南京农业大学](http:\u002F\u002Fwww.njau.edu.cn\u002F)\n*   [中山大学](http:\u002F\u002Fwww.sysu.edu.cn\u002F)\n*   [广东省科学院](http:\u002F\u002Fwww.gdas.gd.cn\u002F)\n*   [中国农业新闻网](http:\u002F\u002Fwww.farmer.com.cn\u002F)\n\n[](https:\u002F\u002Fwww.gdaas.cn\u002Ffwsnn\u002Fkjfw\u002Fcontent\u002Fpost_1338205.html)\n\n版权所有：广东省农业科学院\n\n地址：广州市天河区金颖路29号 邮编：510640\n\n[![Image 12: 粤公网安备44010602013764号](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_ga.png)粤公网安备44010602013764号](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=44010602013764)[粤ICP备16101361号](https:\u002F\u002Fbeian.miit.gov.cn\u002F)\n\n[![Image 13](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_wzdt.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986930.html)\n\n[网站地图](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986930.html)\n\n[![Image 14](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_lxwm.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986932.html)\n\n[联系我们](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_986932.html)\n\n[![Image 15](https:\u002F\u002Fwww.gdaas.cn\u002Fpub\u002Ficon_wzjc.png)](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)\n\n[网站纠错](https:\u002F\u002Fwww.gdaas.cn\u002Fsyqt\u002Fcontent\u002Fpost_1029428.html)","广东省农科院植保所 2026-09-09","2026-09-08T16:00:00Z",43,{"impact":20,"substance":13,"depth":20,"authority":58,"freshness":81,"relevant":21,"comment":313},"省级植保所与高校、电信及企业围绕病虫害智能体研发的科企合作座谈，方向契合智慧农业但尚处交流阶段，缺乏实质成果与数据，可作为行业动态收录。",[315],{"name":309,"url":306},[26,317,318,319,28],"绿色防控","病虫害识别","科企合作",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":323},{"candidates":324,"agricultural_evidence":52},[325,329,335,340,346,350],{"code":39,"score":51,"supported":52,"title":326,"abstract":327,"tags":328},[60],[60],[317],{"code":330,"score":65,"supported":52,"title":331,"abstract":332,"tags":334},"A1",[],[333],"水稻",[],{"code":64,"score":65,"supported":52,"title":336,"abstract":337,"tags":339},[],[338],"荔枝",[],{"code":341,"score":65,"supported":52,"title":342,"abstract":343,"tags":345},"G1",[],[344],"成果转化",[],{"code":32,"score":161,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[26,318],{"code":152,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[317],[355,356],"农业人工智能 病虫害识别 智慧植保 科企合作","农业人工智能 病虫害识别","农业人工智能病虫害识别智慧植保科企合作-2471","2026-09-15T00:04:23.184605Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":8,"source_name":365,"source_url":362,"published_at":366,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":371,"tags":373,"primary_topic":376,"secondary_topics":381,"classification":383,"search_phrases":399,"slug":402,"view_count":170,"doi":403,"paper":404,"created_at":418},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":20,"substance":369,"depth":93,"authority":51,"freshness":13,"relevant":21,"comment":370},21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[372],{"name":365,"url":362},[102,26,256,374,375],"植物病害识别","可解释性",{"code":253,"slug":377,"name":378,"path":379,"parent_code":380},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[382],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":384},{"candidates":385,"agricultural_evidence":52},[386,391,395],{"code":253,"score":51,"supported":52,"title":387,"abstract":389,"tags":390},[388],"smallholder",[388],[256],{"code":32,"score":13,"supported":52,"title":392,"abstract":393,"tags":394},[134],[],[26,374],{"code":112,"score":21,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[102],[400,401],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":403,"openalex_id":405,"authors":406,"venue":365,"cited_by_count":170,"oa_url":362,"card":413,"direction":175,"ingested_from":178},"W7223137323",[407,409,411],{"name":408,"orcid":8},"Akshay Ramgoolam",{"name":410,"orcid":8},"Priya Bundhoo Appadoo",{"name":412,"orcid":8},"Jean-Noël LEspérance",{"tldr":414,"method":415,"finding":416,"direction":175,"opportunity":417},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":8,"source_name":365,"source_url":422,"published_at":366,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":367,"score_detail":425,"sources":427,"tags":429,"primary_topic":433,"secondary_topics":439,"classification":441,"search_phrases":464,"slug":467,"view_count":170,"doi":468,"paper":469,"created_at":481},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":20,"substance":17,"depth":93,"authority":51,"freshness":225,"relevant":21,"comment":426},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[428],{"name":365,"url":422},[102,26,430,431,432],"灌溉决策","安全保证","试验设计",{"code":434,"slug":435,"name":436,"path":437,"parent_code":438},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[440],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":442},{"candidates":443,"agricultural_evidence":52},[444,449,453,460],{"code":434,"score":19,"supported":52,"title":445,"abstract":446,"tags":448},[435],[447,435],"灌溉",[430],{"code":32,"score":51,"supported":52,"title":450,"abstract":451,"tags":452},[54],[135],[26],{"code":454,"score":81,"supported":52,"title":455,"abstract":456,"tags":459},"H2",[],[457,458],"土壤","soil",[],{"code":112,"score":21,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[102],[465,466],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":468,"openalex_id":470,"authors":471,"venue":365,"cited_by_count":170,"oa_url":422,"card":476,"direction":175,"ingested_from":178},"W7223131205",[472,474],{"name":473,"orcid":8},"Nassima Benkhelifa",{"name":475,"orcid":8},"Yacine Aït Ahmed",{"tldr":477,"method":478,"finding":479,"direction":175,"opportunity":480},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":483,"title":484,"url":485,"summary":486,"summary_zh":487,"content":8,"source_name":365,"source_url":485,"published_at":366,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":488,"score_detail":489,"sources":491,"tags":493,"primary_topic":496,"secondary_topics":497,"classification":499,"search_phrases":514,"slug":517,"view_count":170,"doi":518,"paper":519,"created_at":533},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",70,{"impact":20,"substance":17,"depth":93,"authority":51,"freshness":13,"relevant":21,"comment":490},"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[492],{"name":365,"url":485},[102,26,256,494,495],"作物病害识别","模型审计",{"code":253,"slug":377,"name":378,"path":379,"parent_code":380},[498],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":500},{"candidates":501,"agricultural_evidence":52},[502,506,510],{"code":253,"score":19,"supported":52,"title":503,"abstract":504,"tags":505},[388],[256,388],[256],{"code":32,"score":13,"supported":52,"title":507,"abstract":508,"tags":509},[134],[],[494,26],{"code":112,"score":21,"supported":14,"title":511,"abstract":512,"tags":513},[],[],[102],[515,516],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":518,"openalex_id":520,"authors":521,"venue":365,"cited_by_count":170,"oa_url":485,"card":528,"direction":175,"ingested_from":178},"W7223088030",[522,524,526],{"name":523,"orcid":8},"Abdi-Basid Idriss Farah",{"name":525,"orcid":8},"Amina Abdillahi Guedi",{"name":527,"orcid":8},"Moussa Omar Youssouf",{"tldr":529,"method":530,"finding":531,"direction":175,"opportunity":532},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z"]