[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-883":3,"related-883":71},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":65,"slug":68,"view_count":69,"doi":8,"paper":8,"created_at":70},883,"八部门联合印发《关于进一步加强工业等污水污染风险防范 保障农业灌溉用水安全的通知》","https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804295","生态环境部联合工信部、公安部、自然资源部、水利部、农业农村部、最高法、最高检等八部门印发《关于进一步加强工业等污水污染风险防范 保障农业灌溉用水安全的通知》：从优化空间布局、加强污水排放管控、强化农业灌溉用水水质保障、加强环境执法与宣传培训四方面，明确农田周边、喀斯特地貌等高风险地区特征污染物建设项目准入要求，推进工业园区污水集中处理设施升级改造，开展工业\u002F畜禽养殖涉水排查整治；出台农田灌溉用水水质监测技术指南，加大对非法排放工业废水和畜禽粪污现场执法力度，畅通行政执法与刑事司法衔接。",null,"![Image 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[地方+](https:\u002F\u002Fcenews.com.cn\u002Flocality.html?cid=16)\n\n[首页](https:\u002F\u002Fcenews.com.cn\u002Findex.html)[>](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804295)[市场快讯](https:\u002F\u002Fcenews.com.cn\u002Fcolumn.html?cid=212)\n\n##### 八部门印发通知加强工业等污水污染风险防范\n\n时间：2026-08-19 18:00:32 来源：生态环境部\n\n近日，生态环境部联合工业和信息化部、公安部、自然资源部、水利部、农业农村部、最高人民法院、最高人民检察院印发《关于进一步加强工业等污水污染风险防范 保障农业灌溉用水安全的通知》（以下简称《通知》）。\n\n按照《群众身边水体保护治理行动方案》有关规定，《通知》作为其配套制度文件，坚持问题导向、系统治理，针对当前工业企业及园区、畜禽养殖场空间布局不够合理，污水排放管控要求不够完善，农业灌溉用水水质保障机制不够健全等突出问题，从优化空间布局、加强污水排放管控、强化农业灌溉用水水质保障、加强环境执法与宣传培训等四方面明确相关举措，推动构建跨部门、全周期的风险防控体系。\n\n《通知》明确，在国土空间规划管控、生态环境分区管控、建设项目环境影响评价等环节，进一步规范农田周边、喀斯特地貌等风险较高地区，以及排放全盐量、重金属、有毒有害污染物等特征污染物的建设项目准入要求，严防化工园区转移带来的环境风险。\n\n《通知》要求，推进农田周边工业园区污水集中处理设施升级改造，开展工业、畜禽养殖涉水问题排查整治，对工业企业排放特征污染物可能影响下游农业灌溉用水安全的，开展地方水污染物排放标准制修订，加强排放管控要求。\n\n《通知》提出，要进一步压实企业自行监测主体责任，出台农田灌溉用水水质监测技术指南，指导地方因地制宜制定农业灌溉用水水质监测计划，加强农业灌溉用水水质监测协作，明确信息通报、数据共享、应急处置等要求。\n\n《通知》要求，加大现场执法力度，重点整治利用溶洞、渗坑、裂隙等非法排放工业废水、畜禽粪污等行为。加强执法司法联动和信息共享，完善行政执法与刑事司法衔接，坚决打击污染环境犯罪。加强政策解读和宣传培训，提升企业守法自觉和基层履职能力。\n\n《通知》的发布实施将有助于进一步补齐工业等污水污染排放管控短板，更好保障农业灌溉用水安全，加快解决人民群众的“急难愁盼”。下一步，生态环境部将会同有关部门加大宣传培训力度，加强跟踪指导帮扶，推动地方抓好《通知》落实，守牢农业灌溉用水安全底线。\n\n![Image 9: 图片](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0818\u002F17870605371201089_723x2059.jpg)\n\n编辑：刘良伟\n\n本作品如有侵权，请联系我们及时删除。联系电话：010-67175015\n\n*   _0_\n*   ![Image 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22](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0630\u002F17828000522751254_595x170.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=9576)\n## [习近平党建思想](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=9576)\n\n中国环境APP|2026-06-30 浏览量：124\n\n*   [](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804295)分享到 [![Image 23](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0814\u002F17866768045101144_600x171.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=9610)\n## [AI系列短剧——韦一发那些事](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=9610)\n\n中国环境APP|9天前 浏览量：47\n\n*   [](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804295)分享到 [![Image 24](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0312\u002F17732972677851907_600x338.png)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1773128)\n## 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27](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0821\u002F17872804181701591_600x338.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804839)\n## [从感官不适到法定污染，生态环境法典如何为光污染“定规立矩”？](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804839)\n\n中国环境APP|-7小时前 浏览量：0\n\n*   [](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804295)分享到 [![Image 28](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0319\u002F17739194296271339_520x294.png)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1774580)\n## [生态环境法典“掌中宝”上线！可以看全文、查关键词！（欢迎转发）](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1774580)\n\n中国环境APP|2026-03-20 浏览量：15082\n\n*   [](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804295)分享到 [![Image 29](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0820\u002F17872079541251910_600x338.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804663)\n## [学习生态环境法典 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32](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0823\u002F17874477026321462_600x338.png)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1805153)\n## [美国友人“赛考斯”受邀来华参观交流](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1805153)\n\n新华社|13小时前 浏览量：44\n\n##### 热点\n\n*   [i>1 7月，镜头对准了“蒸”战一线的生态环保人 沸点|](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804598)\n*   [i>2 不养猫不养狗 年轻人为何开始流行“养砖头”？ 沸点|](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804823)\n*   [i>3 热浪驱动臭氧污染机制被揭示 沸点|](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804702)\n*   [i>4 热带气旋“向极迁移”加剧风暴潮风险 沸点|](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804780)\n*   [i>5 蝉鸣也是“隐形噪音”？专家：警惕声污染伤耳 沸点|](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804642)\n\n[![Image 33](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0703\u002F17830591145181216_600x338.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1794317)[![Image 34](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2025\u002F1117\u002F17633440748251153_600x339.png)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1748633)[![Image 35](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2025\u002F1223\u002F17664783024401681_600x339.png)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1752262)[![Image 36](http:\u002F\u002Fres.cenews.com.cn\u002Fdata2\u002F1\u002Fimages\u002F2022\u002F0308\u002F16467049077991839_616x230.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=1523)[![Image 37](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0610\u002F17810582464551996_598x338.jpg)](https:\u002F\u002Fwww.cenews.com.cn\u002Ftraining.html)[![Image 38](http:\u002F\u002Fres.cenews.com.cn\u002Fdata2\u002F1\u002Fimages\u002F2022\u002F0311\u002F16469641977271560_911x261.jpg)](https:\u002F\u002Fwww.cenews.com.cn\u002Fspecial.html?cid=1566)[![Image 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44](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2025\u002F0401\u002F17434890692031809_600x338.jpg)](https:\u002F\u002Fwww.cenews.com.cn\u002Fspecial.html?cid=8965&articleId=1195832)[![Image 45](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0706\u002F17833048511191409_600x254.png)](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=8356)[![Image 46](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2025\u002F0424\u002F17454618341781981_600x339.jpg)](https:\u002F\u002Fwww.ie-expo.cn\u002F#\u002F)[![Image 47](http:\u002F\u002Fres.cenews.com.cn\u002Fdata2\u002F1\u002Fimages\u002F2023\u002F0908\u002F16941616070211453_1322x378.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1082373)[![Image 48](http:\u002F\u002Fres.cenews.com.cn\u002Fdata2\u002F1\u002Fimages\u002F2023\u002F0721\u002F16899311689971782_900x383.jpg)](https:\u002F\u002Fwww.cenews.com.cn\u002Feconomics.html?cid=1378&articleId=1070768)[![Image 49](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2025\u002F0528\u002F17484026470901312_600x337.jpg)](https:\u002F\u002Fcenews.com.cn\u002Fspecial.html?cid=1549)\n\n##### [爆料](https:\u002F\u002Fcenews.com.cn\u002Fcolumn.html?cid=26)\n\n[广西柳州市友军机械、亿昌机械有限公司废气排放污染 2023-08-17](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1076657)[江西省景德镇市乐平市南内河污染严重 2023-08-04](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1056460)[辽宁省营口市一烧烤店油烟污染周边环境 2023-08-04](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1057454)[广西河池市凤山县地质公园保护区内炸山开路、采矿、采石 2023-08-04](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1062657)[广东省东莞市虎门镇房顶疑似堆放工业垃圾 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54](http:\u002F\u002Fcmsapi.cenews.com.cn\u002Fapi\u002FuserFace?uid=7121)\n环监007\n\n[“八一”节无人机助农作业](https:\u002F\u002Fcenews.com.cn\u002Fatlas.html?aid=1800673)\n\n[[更多>>](https:\u002F\u002Fcenews.com.cn\u002Fphotograph.html?cid=6)](javascript:void(0))\n\n##### [随手拍](https:\u002F\u002Fcenews.com.cn\u002Fcolumn.html?cid=240)\n\n*   [_1_ 2026国际数字能源展以“人工智能+”开启能源智变新范式 ![Image 55](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0819\u002F17871338868851767_500x330.jpg) 作者: 环境综合](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804502)\n*   [_2_ 国家级自驾游路线上线！喊你来交作业啦 ![Image 56](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0820\u002F17872197727711310_600x338.jpg) 作者: 中环报记者霍桃](https:\u002F\u002Fcenews.com.cn\u002Fnews.html?aid=1804740)\n*   [_3_ 枯木红枫绽新叶 ![Image 57](https:\u002F\u002Fres.cenews.com.cn\u002Fdata3\u002F1\u002Fimages\u002F2026\u002F0819\u002F17871249510131006_1080x1440.jpg) 作者: 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2026-08-22","2026-08-22T00:00:00Z","政策",10,true,90,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},28,22,18,14,8,1,"八部门联合发文，聚焦工业污水风险防范与农业灌溉用水安全，政策层级高、影响面广，信息增量明确，时效性良好。",[25],{"name":10,"url":6},[27,28,29,30,31],"畜禽粪污","工业污水","农业灌溉用水","八部门通知","行政执法",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,52,58],{"code":33,"score":47,"supported":14,"title":48,"abstract":50,"tags":51},11,[49],"灌溉",[49],[],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"C6",4,[],[27],[27],{"code":59,"score":60,"supported":14,"title":61,"abstract":62,"tags":64},"C",3,[],[63],"畜禽",[],[66,67],"农业灌溉用水 八部门通知 工业污水 畜禽粪污","农业灌溉用水 八部门通知","农业灌溉用水八部门通知工业污水畜禽粪污-883",5,"2026-08-24T00:06:08.765365Z",{"total":72,"page":22,"page_size":72,"items":73},6,[74,123,200,270,383,475],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":8,"content":79,"source_name":80,"source_url":8,"published_at":81,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":82,"score_detail":83,"sources":87,"tags":89,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":117,"slug":120,"view_count":121,"doi":8,"paper":8,"created_at":122},3921,"八部门联合发文筑牢农业灌溉用水安全防线","https:\u002F\u002Fwww.xyshjj.cn\u002Fcontent\u002F202610\u002F01\u002Fc27348831.html","生态环境部联合农业农村部等八部门印发《关于进一步加强工业等污水污染风险防范保障农业灌溉用水安全的通知》。《通知》坚持问题导向、系统治理，针对当前工业企业及园区、畜禽养殖场空间布局不够合理，污水排放管控要求不够完善，农业灌溉用水水质保障机制不够健全等突出问题，从抓好空间管控、强化污水排放管控、加强水质监测、强化执法监管等四个方面明确相关举措，计划今年底前出台农业灌溉用水水质监测技术指南，并构建\"空天地\"一体化监测网络。","近日，生态环境部联合农业农村部、工业和信息化部、公安部、自然资源部、水利部、最高人民法院、最高人民检察院印发《关于进一步加强工业等污水污染风险防范 保障农业灌溉用水安全的通知》（以下简称《通知》）。\n\n水是农业命脉，农业灌溉用水安全直接关系粮食安全。八部门联手出台文件，旨在落实源头预防、过程把控、末端监管，推动构建跨部门、全周期的风险防控体系，切实保障农业灌溉用水安全。\n\n《通知》坚持问题导向、系统治理，针对当前工业企业及园区、畜禽养殖场空间布局不够合理，污水排放管控要求不够完善，农业灌溉用水水质保障机制不够健全等突出问题，从四个方面明确相关举措。\n\n一是抓好空间管控。依托土地审批、项目环评等手段，严格管控农田周边、喀斯特地貌等高风险区域的项目准入，从项目源头减少污染物进入农田，做到防患于未然。\n\n二是强化污水排放管控。对农田周边涉水污染源开展排查，建立问题清单，拉条挂账，制定整治方案并依法依规推进有序整改。推进农田周边工业园区污水集中处理设施提标改造。开展地方水污染物排放标准的制修订，完善特征污染物管控要求，重点强化全盐量、重金属、有毒有害污染物等排放管控，并把相关要求依法纳入排污许可证管理。\n\n三是加强水质监测。计划今年底前出台农业灌溉用水水质监测技术指南，指导地方结合本地实际制定农业灌溉用水监测计划。对于工业园区周边的灌溉用水，灌溉期原则上至少开展2次监测，包括临播种前、作物生长关键期，如幼苗期、水稻分蘖期等。把监测点位布设到田间地头，灌溉用水达不达标，用监测数据来说话。\n\n四是强化执法监管。加快构建“空天地”一体化监测网络，加强工业园区、集聚区以及畜禽养殖企业排污口、雨水口的监管。坚决打击污染环境犯罪，依法查处违规设置排污口、私设暗管排污等违法行为，重点整治利用溶洞、渗坑、裂隙等非法排放工业废水、畜禽粪污等行为。（记者 杨玉）","中国县域经济报·2026年10月01日","2026-10-01T04:07:53Z",92,{"impact":84,"substance":85,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":86},27,23,"八部门联合发文，条款具体、时效性强，直接关系粮食安全与灌溉水质，值得进入每日精选。",[88],{"name":80,"url":77},[90,29,91,92,93],"粮食安全","水质监测","污水排放管控","跨部门监管",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":97},{"candidates":98,"agricultural_evidence":14},[99,103,107,112],{"code":33,"score":47,"supported":14,"title":100,"abstract":101,"tags":102},[49],[49],[],{"code":59,"score":60,"supported":14,"title":104,"abstract":105,"tags":106},[],[63],[],{"code":108,"score":22,"supported":41,"title":109,"abstract":110,"tags":111},"A8",[],[],[90],{"code":113,"score":22,"supported":41,"title":114,"abstract":115,"tags":116},"D4",[],[],[91],[118,119],"八部门 农业灌溉用水安全 通知","农业灌溉用水 污水排放管控 跨部门监管 水质监测","八部门农业灌溉用水安全通知-3921",2,"2026-10-02T11:53:08.199707Z",{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":8,"source_name":129,"source_url":126,"published_at":130,"category":131,"cover_url":8,"hotness":13,"is_selected":41,"score":132,"score_detail":133,"sources":137,"tags":139,"primary_topic":145,"secondary_topics":146,"classification":153,"search_phrases":179,"slug":182,"view_count":183,"doi":184,"paper":185,"created_at":199},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",69,{"impact":21,"substance":18,"depth":19,"authority":134,"freshness":135,"relevant":22,"comment":136},12,9,"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。",[138],{"name":129,"url":126},[140,141,142,143,144],"智慧农业","农业人工智能","灌溉决策","安全保证","试验设计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[147],{"code":148,"slug":149,"name":150,"path":151,"parent_code":152},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":41,"version":42,"method":43,"evidence":154},{"candidates":155,"agricultural_evidence":14},[156,161,167,174],{"code":33,"score":157,"supported":14,"title":158,"abstract":159,"tags":160},15,[34],[49,34],[142],{"code":148,"score":134,"supported":14,"title":162,"abstract":164,"tags":166},[163],"artificial intelligence",[165],"decision support",[141],{"code":168,"score":72,"supported":14,"title":169,"abstract":170,"tags":173},"H2",[],[171,172],"土壤","soil",[],{"code":175,"score":22,"supported":41,"title":176,"abstract":177,"tags":178},"I1",[],[],[140],[180,181],"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",0,"10.5281\u002Fzenodo.23543665",{"doi":184,"openalex_id":186,"authors":187,"venue":129,"cited_by_count":183,"oa_url":126,"card":192,"direction":196,"ingested_from":198},"W7223131205",[188,190],{"name":189,"orcid":8},"Nassima Benkhelifa",{"name":191,"orcid":8},"Yacine Aït Ahmed",{"tldr":193,"method":194,"finding":195,"direction":196,"opportunity":197},"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。","农业人工智能与决策模型","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。","openalex","2026-10-22T11:21:10.283561Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":8,"source_name":206,"source_url":203,"published_at":207,"category":131,"cover_url":8,"hotness":13,"is_selected":41,"score":208,"score_detail":209,"sources":213,"tags":215,"primary_topic":221,"secondary_topics":222,"classification":223,"search_phrases":248,"slug":251,"view_count":183,"doi":252,"paper":253,"created_at":269},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment","2026-10-20T00:00:00Z",65,{"impact":21,"substance":210,"depth":211,"authority":134,"freshness":135,"relevant":22,"comment":212},20,16,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[214],{"name":206,"url":203},[216,217,218,219,220],"NDVI","埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":224},{"candidates":225,"agricultural_evidence":14},[226,232,238,243],{"code":33,"score":72,"supported":14,"title":227,"abstract":228,"tags":231},[],[229,230],"蒸散发","evapotranspiration",[],{"code":233,"score":60,"supported":14,"title":234,"abstract":235,"tags":237},"H7",[],[236],"气候变化",[],{"code":239,"score":22,"supported":41,"title":240,"abstract":241,"tags":242},"H6",[],[],[218],{"code":244,"score":22,"supported":41,"title":245,"abstract":246,"tags":247},"I3",[],[],[216],[249,250],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":252,"openalex_id":254,"authors":255,"venue":206,"cited_by_count":183,"oa_url":262,"card":263,"direction":267,"ingested_from":198},"W7223064056",[256,259],{"name":257,"orcid":258},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":260,"orcid":261},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":264,"method":265,"finding":266,"direction":267,"opportunity":268},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","农业遥感与作物表型","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":8,"source_name":276,"source_url":273,"published_at":130,"category":131,"cover_url":8,"hotness":13,"is_selected":41,"score":277,"score_detail":278,"sources":280,"tags":282,"primary_topic":286,"secondary_topics":287,"classification":294,"search_phrases":353,"slug":356,"view_count":183,"doi":357,"paper":358,"created_at":382},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":19,"substance":18,"depth":19,"authority":134,"freshness":135,"relevant":22,"comment":279},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[281],{"name":276,"url":273},[140,141,283,284,285],"遥感","作物水分胁迫","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[288,289],{"code":148,"slug":149,"name":150,"path":151,"parent_code":152},{"code":290,"slug":291,"name":292,"path":293,"parent_code":152},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":41,"version":42,"method":43,"evidence":295},{"candidates":296,"agricultural_evidence":14},[297,301,307,314,321,326,333,340,347],{"code":33,"score":157,"supported":14,"title":298,"abstract":299,"tags":300},[34],[49,34],[285],{"code":148,"score":13,"supported":14,"title":302,"abstract":303,"tags":306},[],[304,305,163],"机器学习","machine learning",[141],{"code":290,"score":135,"supported":14,"title":308,"abstract":309,"tags":313},[],[310,311,312],"物联网","internet of things","IoT",[],{"code":175,"score":315,"supported":14,"title":316,"abstract":317,"tags":320},7,[],[318,319],"数字孪生","digital twin",[140],{"code":244,"score":315,"supported":14,"title":322,"abstract":323,"tags":325},[],[283,324],"remote sensing",[283],{"code":327,"score":72,"supported":14,"title":328,"abstract":329,"tags":332},"A2",[],[330,331],"小麦","wheat",[],{"code":334,"score":72,"supported":14,"title":335,"abstract":336,"tags":339},"A3",[],[337,338],"玉米","maize",[],{"code":341,"score":72,"supported":14,"title":342,"abstract":343,"tags":346},"A5",[],[344,345],"马铃薯","potato",[],{"code":348,"score":60,"supported":14,"title":349,"abstract":350,"tags":352},"B2",[],[351],"葡萄",[],[354,355],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":357,"openalex_id":359,"authors":360,"venue":276,"cited_by_count":183,"oa_url":273,"card":376,"direction":381,"ingested_from":198},"W7223088921",[361,364,367,370,373],{"name":362,"orcid":363},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":365,"orcid":366},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":368,"orcid":369},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":371,"orcid":372},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":374,"orcid":375},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":377,"method":378,"finding":379,"direction":267,"opportunity":380},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":8,"source_name":389,"source_url":386,"published_at":130,"category":131,"cover_url":8,"hotness":13,"is_selected":41,"score":390,"score_detail":391,"sources":394,"tags":396,"primary_topic":399,"secondary_topics":400,"classification":403,"search_phrases":451,"slug":454,"view_count":183,"doi":455,"paper":456,"created_at":474},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":19,"substance":18,"depth":19,"authority":392,"freshness":135,"relevant":22,"comment":393},13,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[395],{"name":389,"url":386},[140,141,310,397,398],"精准农业","气候韧性",{"code":148,"slug":149,"name":150,"path":151,"parent_code":152},[401,402],{"code":290,"slug":291,"name":292,"path":293,"parent_code":152},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":404},{"candidates":405,"agricultural_evidence":14},[406,414,419,425,430,435,439,445],{"code":148,"score":19,"supported":14,"title":407,"abstract":408,"tags":413},[163],[409,304,410,305,411,412],"深度学习","计算机视觉","deep learning","computer vision",[141],{"code":175,"score":211,"supported":14,"title":415,"abstract":417,"tags":418},[416],"precision agriculture",[397,416],[140,397],{"code":290,"score":13,"supported":14,"title":420,"abstract":421,"tags":424},[],[310,422,312,423],"边缘计算","edge computing",[310],{"code":33,"score":135,"supported":14,"title":426,"abstract":427,"tags":429},[],[49,428,34],"水资源",[],{"code":233,"score":315,"supported":14,"title":431,"abstract":432,"tags":434},[],[236,433],"climate change",[398],{"code":168,"score":72,"supported":14,"title":436,"abstract":437,"tags":438},[],[171,172],[],{"code":440,"score":60,"supported":14,"title":441,"abstract":442,"tags":444},"A7",[],[443],"间作",[],{"code":446,"score":60,"supported":14,"title":447,"abstract":448,"tags":450},"F3",[],[449],"养分管理",[],[452,453],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":455,"openalex_id":457,"authors":458,"venue":389,"cited_by_count":183,"oa_url":386,"card":469,"direction":381,"ingested_from":198},"W7223121456",[459,462,464,466],{"name":460,"orcid":461},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":463,"orcid":8},"Oluwadare OYEBODE",{"name":465,"orcid":8},"Iretiayo Adelaiye",{"name":467,"orcid":468},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":470,"method":471,"finding":472,"direction":196,"opportunity":473},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":476,"title":477,"url":478,"summary":479,"summary_zh":480,"content":8,"source_name":129,"source_url":478,"published_at":130,"category":131,"cover_url":8,"hotness":13,"is_selected":41,"score":481,"score_detail":482,"sources":484,"tags":486,"primary_topic":490,"secondary_topics":491,"classification":497,"search_phrases":524,"slug":527,"view_count":183,"doi":528,"paper":529,"created_at":539},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":19,"substance":210,"depth":211,"authority":134,"freshness":135,"relevant":22,"comment":483},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[485],{"name":129,"url":478},[141,487,283,488,489],"农业物联网","智慧灌溉","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[492,493],{"code":290,"slug":291,"name":292,"path":293,"parent_code":152},{"code":244,"slug":494,"name":495,"path":496,"parent_code":152},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":41,"version":42,"method":43,"evidence":498},{"candidates":499,"agricultural_evidence":14},[500,505,509,513,517],{"code":33,"score":501,"supported":14,"title":502,"abstract":503,"tags":504},19,[34],[49,428,34],[488,489],{"code":290,"score":19,"supported":14,"title":506,"abstract":507,"tags":508},[312],[310,311,312],[487],{"code":244,"score":157,"supported":14,"title":510,"abstract":511,"tags":512},[324],[283,324],[283],{"code":148,"score":134,"supported":14,"title":514,"abstract":515,"tags":516},[163],[163],[141],{"code":518,"score":72,"supported":14,"title":519,"abstract":520,"tags":523},"H8",[],[521,522],"可持续农业","sustainable agriculture",[],[525,526],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":528,"openalex_id":530,"authors":531,"venue":129,"cited_by_count":183,"oa_url":478,"card":534,"direction":381,"ingested_from":198},"W7223090389",[532],{"name":533,"orcid":8},"Patil",{"tldr":535,"method":536,"finding":537,"direction":381,"opportunity":538},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]