[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-540":3,"related-540":69},{"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":33,"secondary_topics":39,"classification":40,"search_phrases":64,"slug":67,"view_count":47,"doi":8,"paper":8,"created_at":68},540,"AdapterMoE: A Two-Stage Hard-Routing Mixture-of-Experts Architecture for Multi-Crop Disease Recognition with Calibrated Rejection and Incremental Learning","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.08808","【预印本,未经同行评审】针对多作物病害识别任务,提出基于确定性硬路由的两阶段 AdapterMoE 架构:RouterHead 依据最大 softmax 概率阈值分类作物并拒判非目标作物,Energy+KNN 双重门控模块捕获分布外输入;在冻结 EfficientNet-B0 主干上添加五个逐作物 Adapter,通过温度缩放校准。在 PlantVillage(5 作物 26 类)上,AdapterMoE 与最优基线 Macro-F1 差距在 0.24 分以内,但训练成本约为全网络基线的 9%,并可通过 add_crop 接口实现逐作物增量更新,避免专家坍缩,适合田间部署成本敏感的精准植保场景。",null,"[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2608.08808)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2608.08808v1)\n\n> Abstract:Timely crop-disease identification is critical to food security. Multi-crop recognition suits Mixture-of-Experts (MoE), but conventional soft-routing MoE learns crop assignment freely end-to-end, letting a few experts dominate (expert collapse) with no semantic correspondence to crops, and facing high retraining costs, unstable rejection of non-target inputs, and a saturated accuracy ceiling. We shift the objective from accuracy toward a trade-off among deployment cost, scaling flexibility, and rejection stability, using deterministic hard routing. We propose AdapterMoE: a RouterHead classifies the crop and rejects non-target crops via a Maximum Softmax Probability threshold, with a dual-gate Energy+KNN out-of-distribution module catching distribution-shifted inputs; five per-crop Adapters atop a frozen EfficientNet-B0 backbone discriminate diseases, each calibrated via Temperature Scaling. Because experts are hard-isolated at the data level, the design avoids expert collapse and exposes an add_crop interface for local, per-crop updates instead of full retraining. On PlantVillage (5 crops, 26 classes), across a fair five-system comparison, AdapterMoE attains accuracy statistically indistinguishable from the best baselines (Macro-F1 within a 0.24-point band) while cutting training cost to about 9% of full-network baselines, expanding to a new crop in\n\nComments:17 pages, 7 figures, 14 tables\nSubjects:Computer Vision and Pattern Recognition (cs.CV)\nCite as:[arXiv:2608.08808](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.08808) [cs.CV]\n(or [arXiv:2608.08808v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.08808v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.08808](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2608.08808)\n\narXiv-issued DOI via DataCite\n\n## Submission history\n\nFrom: PinHsun Huang [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F12891f91\u002F2608.08808)] \n\n**[v1]** Sun, 9 Aug 2026 16:45:21 UTC (106 KB)","arXiv [预印本]","2026-08-09T16:45:21Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,20,12,6,1,"提出农业多作物病害识别的硬路由MoE架构，方法新颖且实验扎实，对智慧农业有实质贡献。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"农业人工智能","精准农业","边缘部署","植物病害识别","Mixture-of-Experts","多作物",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,59],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},3,true,[],[51],"植保",[],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":58},"I2",2,[],[],[27,30],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"I1",[],[],[28],[65,66],"农业人工智能 植物病害识别 精准农业 边缘部署","农业人工智能 植物病害识别","农业人工智能植物病害识别精准农业边缘部署-540","2026-08-15T00:02:55.709784Z",{"total":21,"page":22,"page_size":21,"items":70},[71,193,246,307,398,467],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":84,"tags":86,"primary_topic":92,"secondary_topics":93,"classification":94,"search_phrases":152,"slug":155,"view_count":156,"doi":157,"paper":158,"created_at":192},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":18,"substance":19,"depth":17,"authority":81,"freshness":82,"relevant":22,"comment":83},13,8,"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[85],{"name":77,"url":74},[87,28,88,89,90,91],"智慧农业","基因编辑","植物病害","生物防治","纳米技术",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":95},{"candidates":96,"agricultural_evidence":48},[97,102,108,115,122,129,136,142,147],{"code":60,"score":82,"supported":48,"title":98,"abstract":99,"tags":101},[],[28,100],"precision agriculture",[87,28],{"code":34,"score":103,"supported":48,"title":104,"abstract":105,"tags":107},7,[],[106,90],"农药",[90],{"code":109,"score":21,"supported":48,"title":110,"abstract":111,"tags":114},"A8",[],[112,113],"粮食安全","food security",[],{"code":116,"score":21,"supported":48,"title":117,"abstract":118,"tags":121},"H2",[],[119,120],"土壤","soil",[],{"code":123,"score":21,"supported":48,"title":124,"abstract":125,"tags":128},"H7",[],[126,127],"气候变化","climate change",[],{"code":130,"score":21,"supported":48,"title":131,"abstract":132,"tags":135},"I3",[],[133,134],"遥感","remote sensing",[],{"code":137,"score":47,"supported":48,"title":138,"abstract":139,"tags":141},"H6",[],[140],"农业生态",[],{"code":54,"score":47,"supported":48,"title":143,"abstract":144,"tags":146},[],[145],"artificial intelligence",[],{"code":148,"score":22,"supported":14,"title":149,"abstract":150,"tags":151},"F2",[],[],[88],[153,154],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480",0,"10.3390\u002Fsu182010661",{"doi":157,"openalex_id":159,"authors":160,"venue":77,"cited_by_count":156,"oa_url":74,"card":185,"direction":189,"ingested_from":191},"W7223044776",[161,164,167,170,173,176,179,182],{"name":162,"orcid":163},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":165,"orcid":166},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":168,"orcid":169},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":171,"orcid":172},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":174,"orcid":175},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":177,"orcid":178},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":180,"orcid":181},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":183,"orcid":184},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":186,"method":187,"finding":188,"direction":189,"opportunity":190},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","智慧农业 \u002F 农业物联网","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","openalex","2026-10-21T23:30:09.242484Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":8,"content":198,"source_name":199,"source_url":8,"published_at":200,"category":201,"cover_url":8,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":208,"primary_topic":212,"secondary_topics":213,"classification":214,"search_phrases":241,"slug":244,"view_count":22,"doi":8,"paper":8,"created_at":245},5108,"广西西林:粤桂协作赋能 深山桔园请来「AI管家」 化肥农药减量20%以上","https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm","广西百色市西林县古障镇仙仁掌村桔园,无人机升空巡测快速识别叶片黄化、缺水、病虫害等异常。依托粤桂协作对口帮扶资源,西林联合深圳市罗湖区、深圳市丰农控股,在233亩核心示范基地搭建「无人机+AI」全域监测平台,气象、墒情、虫情、灾情监测设备全天候运行。核心示范区化肥、农药用量减少20%以上,人工管护成本下降30%,果树亩均产量提升15%,优质果率提高8个百分点;全镇果园管理效率提升60%,每年减少农业灾害损失100万至150万元,全域每年节约农资成本超1426万元。","![Image 3](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002FgmwLogo_share.jpg)\n\n点击右上角![Image 4](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Ficonshare.png)微信好友\n\n![Image 5](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Fpengyouquan.png)朋友圈\n\n![Image 6](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Flayershare.png)\n\n请使用浏览器分享功能进行分享\n\n![Image 7](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Farrow.png)\n\n[![Image 8: 地方频道](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fcontentlogo\u002F12684.gif)](https:\u002F\u002Fdifang.gmw.cn\u002F)\n\n![Image 9](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fmgmw\u002Fmline.png)![Image 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[时评](https:\u002F\u002Fguancha.gmw.cn\u002F)\n*   [理论](https:\u002F\u002Ftheory.gmw.cn\u002F)\n*   [文化](https:\u002F\u002Fculture.gmw.cn\u002F)\n*   [科技](https:\u002F\u002Ftech.gmw.cn\u002F)\n*   [教育](https:\u002F\u002Fedu.gmw.cn\u002F)\n*   [![Image 13](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fcon_headIcon.png)](https:\u002F\u002Fwww.gmw.cn\u002Fmap.htm)\n\n正在阅读：广西西林:粤桂协作赋能，深山桔园请来 “AI 管家”\n\n[首页](https:\u002F\u002Fwww.gmw.cn\u002F)>[![Image 14](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fcontentlogo\u002F12684.gif)](https:\u002F\u002Fdifang.gmw.cn\u002F)[地方频道](https:\u002F\u002Fdifang.gmw.cn\u002F)>[地方频道－广西](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F)>[当地新闻](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002Fnode_12944.htm)>正文\n\n# 广西西林:粤桂协作赋能，深山桔园请来 “AI 管家”\n\n来源：[光明日报客户端](https:\u002F\u002Fapp.gmdaily.cn\u002Fas\u002Fopened\u002Fn\u002F59cdd34806ab4ec7b66540c2cb9dc89e)2026-10-16 12:43\n\n[](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)[X](javascript:;)[](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)\n\n清晨，广西百色市西林县古障镇仙仁掌村桔园，晨露未消，无人机升空巡测，快速识别叶片黄化、缺水、病虫害等果园异常。智慧管护，正成为当地沙糖桔产业的生产常态。\n\n西林是“中国沙糖桔之乡”，沙糖桔种植面积达22.46万亩，是群众增收的支柱产业。但境内山地多、果园地块零散，长期沿用传统管护模式，施肥打药依靠经验，收成受天气影响大。粗放种植造成果品品质不稳定，病虫害、自然灾害带来减产风险，制约产业效益提升。\n\n![Image 15: twitter](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fpng\u002Fsite2\u002F20261016\u002Fe4232a100a384446b18875424e94dd72.png)\n\n依托粤桂协作对口帮扶资源，西林联合深圳市罗湖区、深圳市丰农控股，在古障镇仙仁掌村打造智慧农业试点，搭建“无人机+AI”全域监测平台，探索山地桔园数字化管护，破解“看天吃饭”困境。\n\n在233亩核心示范基地，气象、墒情、虫情、灾情监测设备全天候运行，无人机定期空中巡园，土壤、病虫害、气象等数据实时回传。AI系统自动分析研判，给出农事指导，改变过去果农肉眼巡查果园的传统方式。\n\n项目按照“基地做示范，全镇一张图，全县一个码”三步走推进智慧果园建设。先以上、下俄屯基地打造样板，配套海拔梯度采集点，跑通“数据采集—AI建模—标准输出”流程，优化适配本地沙糖桔的算法模型，解决智慧农业技术“水土不服”问题。第二步完成全镇7.13万亩沙糖桔标准化航测，建成“产业一张图”数字平台，地块信息、产业风险一屏可查，产业管理由凭经验估变为凭数据管。第三步打造“掌上桔事”小程序，把灾害预警、农事指导、AI问诊直达农户手机。目前小程序正在内测，上线后将惠及全县8万多户果农。\n\n![Image 16: twitter](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fpng\u002Fsite2\u002F20261016\u002Ff025b13ba3384034afba14495fc87f4d.png)\n\n“以前种果全靠摸索，天天跑果园巡查，病虫害也不好分辨。现在靠AI系统管护省心很多，期待明年好收成。”首批试点果农、古障镇仙仁掌村上俄屯组长秦锋说。\n\n数字化落地成效显著。核心示范区化肥、农药用量减少20%以上，人工管护成本下降30%，果树亩均产量提升15%，优质果率提高8个百分点；全镇果园管理效率提升60%，每年减少农业灾害损失100万至150万元，全域每年节约农资成本超1426万元。\n\n不少地方智慧农业容易变成观赏“盆景”。西林立足山地特点，聚焦生产实效、农户增收，让数字技术扎根田间。丰农控股广西总经理陈洁介绍，依托AI技术加持，深山沙糖桔产业迎来新机遇。西林计划用三到五年推动沙糖桔产业从“靠人力、凭经验”转向“靠系统、用数据”，打造山地沙糖桔全链路数智化样板。（光明日报全媒体记者 周仕兴 通讯员 杨先能）\n\n[ 责编：王宏泽 ]\n\n阅读剩余全文（）\n\n![Image 17](https:\u002F\u002Fimg.gmw.cn\u002Fcontent_banner\u002Fcontent_840x120_qrcodes.jpg)\n\n## 相关阅读\n\n*   [![Image 18](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)](javascript:;)    \n*   [![Image 19](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)](javascript:;)    \n*   [](javascript:;)[](javascript:;)[](javascript:;)[](javascript:;)\n\n## 您此时的心情\n\n[![Image 20: 光明云投](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fyuntoulogo.png)](https:\u002F\u002Fvote.gmw.cn\u002F)\n\n新闻表情排行[日](https:\u002F\u002Fwww.gmw.cn\u002Fmotionsdaytop.htm)\u002F[周](https:\u002F\u002Fwww.gmw.cn\u002Fmotionsweektop.htm)\n\n*   [![Image 21](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_1.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)开心   0   \n*   [![Image 22](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_2.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)难过   0   \n*   [![Image 23](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_3.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)点赞   0   \n*   [![Image 24](https:\u002F\u002Fimg.gmw.cn\u002Fpic\u002Fconimages\u002Fico_motion_4.png)](https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm)飘过   0   \n\n## 视觉焦点\n\n*   [![Image 25](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20261016\u002F3ab721cf47be4960823a8636e4478b8e.jpg)](https:\u002F\u002Fkepu.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39050695.htm)  \n来中国科技馆，和科学家击掌！\n\n*   [![Image 26](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20261016\u002F115e67cd19084c568c2edd94f9e6391c.jpg)](https:\u002F\u002Ftheory.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39046807.htm)  \n理响中国·幸福有“理”\n\n## 最热文章\n\n*   [一见·习近平主席三封广交会贺信的“开放”底色](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39050949.htm) 1\n*   [和总书记面对面丨每一代人都要走好自己的长征路](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-10\u002F16\u002Fcontent_39050945.htm) 2\n*   [微视频丨聆听总书记深情话语 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27](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260918\u002Fdcd47048fbc148c18e29bbf71cbb75c6.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170734.htm) [赓续长征精神 奋进复兴征程](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170734.htm)  [第十一个全民国家安全教育日](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168693.htm) [![Image 28](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260415\u002F005056b837832b49959d02.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168693.htm)  \n*   [![Image 29](https:\u002F\u002Fimgdifang.gmw.cn\u002Fattachement\u002Fjpg\u002Fsite2\u002F20260918\u002F0aba89129d0f45b8a77ac2b07d7dbfeb.jpg)](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170718.htm) [2026年亚运会](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_170718.htm)  [树立和践行正确政绩观](https:\u002F\u002Ftopics.gmw.cn\u002Fnode_168213.htm) [![Image 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09:51\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F15\u002Fcontent_39047718.htm)\n\n[我科学家首次观测到临界拓扑态](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F15\u002Fcontent_39047718.htm)\n\n常压下，水到0℃会结冰，这就是人们熟悉的相变，0℃即是相变的临界点。两篇论文的共同通讯作者、宁波东方理工大学助理教授余雪佳，把这个难题比作“在沸水中完好保存一件冰雕”。\n\n2026-10-15 09:49\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F15\u002Fcontent_39047675.htm)\n\n[这个打破常识的发现因何而来](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F15\u002Fcontent_39047675.htm)\n\n长久以来，古生物学界与教科书普遍认为琥珀皆源自种子植物（裸子或被子植物），最古老的确切记录定格在美国约3.2亿年前的石炭纪晚期。\n\n2026-10-15 09:48\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F15\u002Fcontent_39047658.htm)\n\n[科技创新下一程，不是“接力赛”！](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F15\u002Fcontent_39047658.htm)\n\n在芯片制造、航空发动机等高端硬核制造领域，技术迭代具有长周期、累积性、阶梯式特征，遵循循序渐进、层层突破的线性攻关规律。新征程上，持续深耕创新生态建设、迭代升级现代化创新体系，必能培育形成扎根中国、联通世界、内生增长、开放共生的创新新格局。\n\n2026-10-15 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09:34\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045218.htm)\n\n[我国优化调整矿产资源目录](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045218.htm)\n\n自然资源部会同国家发展改革委12日发布《矿产资源目录》。自然资源部矿产资源保护监督司负责人表示，矿产资源目录是矿产资源管理制度的重要基础，是制定矿产资源规划、实施矿业权管理、规范矿产资源勘查开采行为、征缴矿产资源税费、制定产业政策等工作的依据。\n\n2026-10-14 09:40\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045301.htm)\n\n[海拔3650米，高原水稻突破种植上限（秋收进行时·不一样的水稻品种）](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045301.htm)\n\n与共康村相邻的巴达村，是第一批参与试种的村，也是西藏首个高海拔水稻实测亩产破千斤的村子，村民把“海拔3175米种水稻”写进村史。一粒水稻种子，不只改写了亩产数字，更改变了“西藏水稻只能在察隅、墨脱海拔1500米以下种植”的历史，为高原水稻纳入地方主粮增添可能。\n\n2026-10-14 09:41\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045297.htm)\n\n[面对AI，如何成为会提问的人](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045297.htm)\n\n如今，AI正改变许多行业的底层逻辑，固守单一思维模式将面临严峻挑战，文理兼备、跨界融合才是稀缺能力——既有理科生的技术思维和逻辑能力，又有文科生的人文洞察和情感素养。未来更有竞争力的，可能不是最典型的文科生或理科生，而是既有专业深度，又能跨学科解决问题的人。\n\n2026-10-14 09:48\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045293.htm)\n\n[深海科技，下水见真章](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F14\u002Fcontent_39045293.htm)\n\n不久前，在海南三亚崖州湾科技城，一场名为“青年π”的海洋主题对话临海开讲。高频下潜试出的不只有装备可靠性，也不断刷新人们对深海的认识。同一园区，由哈尔滨工程大学南海研究院孵化的海南水之声海洋科技有限公司，正给海洋装备搭水下“Wi－Fi”。\n\n2026-10-14 09:49\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F13\u002Fcontent_39042838.htm)\n\n[重点人群尽量在流感季来临前接种疫苗](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F13\u002Fcontent_39042838.htm)\n\n目前各地正在开展流感疫苗接种。今年开始，国家对农村地区“一老一小”重点人群实施免费接种等相关支持性政策。为什么要接种流感疫苗？去年接种过，今年是否还需要接种？到哪里接种？\n\n2026-10-13 10:03\n\n[](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F13\u002Fcontent_39042835.htm)\n\n[让山河可触，尊严可感](https:\u002F\u002Ftech.gmw.cn\u002F2026-10\u002F13\u002Fcontent_39042835.htm)\n\n我国城市盲道总里程以万公里计，公共场所无障碍设施持续铺开。数字世界的“盲道”畅通起来。精神文化的“视界”也在扩大。中国传媒大学师生发起的“光明影院”项目，近十年间每年制作上百部无障碍电影，把电影送到视障朋友耳边，今年还走进联合国教科文组织总部举办专场放映。\n\n2026-10-13 10:01\n\n[](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-10\u002F13\u002Fcontent_39042222.htm)\n\n[截至今年上半年我国生成式人工智能用户规模超7亿人](https:\u002F\u002Fpolitics.gmw.cn\u002F2026-10\u002F13\u002Fcontent_39042222.htm)\n\n记者近期从中国互联网络信息中心获悉：截至今年上半年，我国生成式人工智能用户规模超7亿人，普及率超50%。\n\n2026-10-13 09:58\n\n[加载更多](javascript:void(0);)\n\n光明网版权所有\n\n*   [光明日报社概况](https:\u002F\u002Fabout.gmw.cn\u002Fnode_21441.htm)\n*   [关于光明网](https:\u002F\u002Fabout.gmw.cn\u002Faboutus.htm)\n*   [报网动态](https:\u002F\u002Fabout.gmw.cn\u002Fnode_8926.htm)\n*   [联系我们](https:\u002F\u002Fabout.gmw.cn\u002Fnode_46280.htm)\n*   [法律声明](https:\u002F\u002Fabout.gmw.cn\u002Fnode_46279.htm)\n*   [光明网邮箱](https:\u002F\u002Fmail.gmw.cn\u002F)\n*   [网站地图](https:\u002F\u002Fwww.gmw.cn\u002Fmap.htm)\n\n![Image 33](https:\u002F\u002Fchannel-analysis.gmw.cn\u002F1.gif?s=1792195992041&cl=12944&pd=2026-10-16&ci=39050109&r=https:\u002F\u002Fdifang.gmw.cn\u002Fgx\u002F2026-10\u002F16\u002Fcontent_39050109.htm&at=%E7%8E%8B%E5%AE%8F%E6%B3%BD&tt=%E5%B9%BF%E8%A5%BF%E8%A5%BF%E6%9E%97:%E7%B2%A4%E6%A1%82%E5%8D%8F%E4%BD%9C%E8%B5%8B%E8%83%BD%EF%BC%8C%E6%B7%B1%E5%B1%B1%E6%A1%94%E5%9B%AD%E8%AF%B7%E6%9D%A5%20%E2%80%9CAI%20%E7%AE%A1%E5%AE%B6%E2%80%9D%20_%E5%85%89%E6%98%8E%E7%BD%91)\n\n[](https:\u002F\u002Fwww.facebook.com\u002Fguangmingonline)[](https:\u002F\u002Fwww.facebook.com\u002Fguangmingonline)\n\n[](https:\u002F\u002Ftwitter.com\u002FGuangming_Daily)[](https:\u002F\u002Ftwitter.com\u002FGuangming_Daily)\n\n[](javascript:;)","光明日报全媒体 光明网地方频道 2026 年 10 月 16 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西林","广西西林沙糖桔AI管家-5108","2026-10-17T00:13:01.843117Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":8,"content":251,"source_name":252,"source_url":8,"published_at":253,"category":201,"cover_url":8,"hotness":254,"is_selected":14,"score":255,"score_detail":256,"sources":261,"tags":272,"primary_topic":276,"secondary_topics":277,"classification":278,"search_phrases":302,"slug":305,"view_count":22,"doi":8,"paper":8,"created_at":306},4255,"AI赋能农业如何走出建得起养不起怪圈","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F30\u002F991030157.html","农民日报评论：我国农业科技进步贡献率超过64%、农业生产信息化率超过30%、30余万架植保无人机年作业面积突破4.6亿亩、116个国家智慧农业创新应用项目分品种落地、16个人工智能+行动典型场景。2026年智慧农业市场规模将突破1340亿元。AI农业存在高前期投入、长回报周期、强公共属性，建议健全多元主体参与机制，以财政撬动替代财政兜底。","一、健全多元主体参与机制，以“财政撬动”替代“财政兜底”，是引导社会资本参与涉农数智化建设的核心路径。由于涉农数智化建设具有多元受益属性，必须在强化政府引领作用的基础上构建“政府主导+社会协同”机制，通过投入资金多源化有效降低政府财政负担。一方面，激发内生动力，有效组织农业生产者仍是首要任务。组织化程度直接决定着涉农数智化建设成效——通过提升新型农业经营主体的协同能力，不仅能优化生产效率，更有助于搭建科学的利益分配与风险共担长效机制，从而充分激发农业生产实际出资主体对涉农数智化建设的长远规划意识与持续投资意愿，构建起稳定的建设主体架构。另一方面，要把着力点从“买设备”转向“买服务”。当前，“农业即服务”模式快速兴起，农户无需一次性投入巨资购置智能装备，而是通过按亩付费、以租代售、分时租赁获得植保飞防、变量施肥、智能巡检等服务。地方政府应顺势而为，把有限的补贴资金从“补建设”转向“补作业、补成效”，用作业量和服务农户数说话，让财政资金四两拨千斤。与此同时，要用好信贷、担保、保险等金融工具，并抓住公共数据资源开发利用的机遇，探索涉农数据确权、估值与质押融资，把田间数据从“沉睡资源”转化为可变现资产，为轻资产的涉农AI服务商打开融资通道。在试点选择上，应优先在农业基础好、市场区位优的县市先行先试，重点在智能农机协作平台、农业低空经济、AI智能体托管种植、智慧农（牧、渔）场等市场化程度高的项目上，探索创新收益分配与风险分担模式，切实增强运营商、互联网企业及金融机构等社会资本的投资意愿。\n\n二、推广标准化生产是AI赋能农业的基础，更是降低财政投入强度的“省钱之道”。各级政府若期望AI赋能农业经济发展，必须提前在辖区内开展农业标准化体系建设。首先，只有标准化才能为涉农AI模型训练提供高质量数据。大模型竞争归根结底是数据竞争，国务院《加快农业农村现代化“十五五”规划》明确要求“建设大规模、多模态、高质量数据集，完善数据标准和规范”。通过政策引导使农业生产各环节执行统一操作规范，才能系统性生成结构化、可机读的数据；否则，分散经营造成的数据碎片化会成倍推高模型训练成本，财政投入也将事倍功半。其次，标准化可有效降低智能终端设备的落地门槛。统一技术接口与应用场景，才能让无人机、机器人、传感器、大模型真正“即插即用”，避免各搞一套“土标准”带来的重复建设和适配成本。2026年6月，农业农村部归口管理的8项农业人工智能行业标准获立项，覆盖智能装备、作物智能监测、农业农村大模型等领域，地方应主动对标国家标准、行业标准，让一次投入、多方复用成为现实。最后，标准化是分级奖补的依据。《数字乡村高质量发展行动计划（2026—2030年）》首次提出建立智慧农（牧、渔）场数字化场景认证体系，地方可据此把“感知—决策—执行—数据闭环”的能力分级量化，按级奖补、精准滴灌，推动农业生产模式从“个体经验”向“数据驱动”转型，以边际成本递减效应加速农业现代化进程。\n\n三、打造差异化品牌是利用智能营销体系实现农产品溢价的前提。良好品牌能为智能营销体系提供信任背书与内容抓手，同时为AI驱动的各类新业态提供坚实的价值支撑。例如，高市场认可度的农副产品在利用AI进行精准推送或开展虚拟主播营销时，更易获得消费者信任与青睐，从而显著提升营销转化效率。需要看到的是，品牌建设已不再是“吆喝”层面的事。农业农村部印发的《全国农产品产地市场体系发展“十五五”规划》明确提出“推进‘人工智能+’农产品流通发展”，加强农产品消费大数据分析与生产端反馈应用，打造面向市场、数据驱动、高效协同的智能供应链。这意味着，AI可以把消费端的偏好实时反馈给生产端，实现“以销定产”，既减少滞销带来的资源浪费和财政兜底压力，又让好产品卖出好价钱。2025年全国农村网络零售额已突破3万亿元，较“十三五”末增长67.6%，线上渠道为品牌溢价提供了广阔舞台。因此，尚有余力的地方政府应前瞻性布局，积极引领农企、合作社等经营主体，将农副产品品牌建设与AI技术深度融合，探索“AI+品牌溯源”“AI+内容生成”等应用，并积极参与区块链溯源认证和智慧农（牧、渔）场场景认证，用可验证的数据为品牌背书。上述举措不仅能实现本地优质农副产品的“跨区量价齐升”，更能系统性培育出具有市场号召力的农业品牌，并最终凭借品牌价值与流通效率的双重提升，加速实现农产品溢价。\n\n需要指出的是，破解财政瓶颈，归根结底要处理好“政府有为”与“市场有效”的关系。政府把钱花在标准、数据集、基础设施和联农带农机制上，把经营性环节交给市场，把运营成本摊薄到服务之中，才能真正走出“财政背包袱、设备睡大觉”的怪圈。同时也要守住一条底线：在引导社会资本和数据要素下乡的过程中，应通过明晰的数据权属和利益联结机制，确保农民不仅是数据的提供者，更是增值收益的分享者，防止“算法”成为新的中间商。唯其如此，智能化发展红利才能更多更公平惠及广大农民群众，为农业强国建设和中国式现代化提供坚实支撑。","中国农网 农民日报 2026-09-30","2026-09-30T00:00:00Z",55,82,{"impact":257,"substance":18,"depth":17,"authority":258,"freshness":259,"relevant":22,"comment":260},24,14,4,"央媒深度分析AI赋能农业的财政可持续路径，提出财政撬动、标准化、品牌溢价三大抓手，政策与数据细节扎实，但发布已逾一周，时效性偏弱。",[262,263,266,269],{"name":252,"url":249},{"name":264,"url":265},"新疆维吾尔自治区农业农村厅 2026-10-12","https:\u002F\u002Fnynct.xinjiang.gov.cn\u002Fxjnynct\u002Fc113576\u002F202610\u002F24505848b5c346999ef97ecec7734c21.shtml",{"name":267,"url":268},"农民日报电子版 2026 年 10 月 13 日","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20261013\u002F20261013_6\u002Fnmrb_20261013_13424_6_2109757807733608534.html",{"name":270,"url":271},"农民日报 转载定西市畜牧兽医局 2026 年 10 月 13 日","http:\u002F\u002Fxmj.dingxi.gov.cn\u002Fart\u002F2026\u002F10\u002F13\u002Fart_8322_1916899.html",[209,87,27,273,274,275],"农产品品牌","财政撬动","农业标准化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":279},{"candidates":280,"agricultural_evidence":48},[281,285,289,294,298],{"code":60,"score":259,"supported":48,"title":282,"abstract":283,"tags":284},[],[87],[87],{"code":34,"score":47,"supported":48,"title":286,"abstract":287,"tags":288},[],[51],[],{"code":290,"score":22,"supported":14,"title":291,"abstract":292,"tags":293},"E5",[],[],[273],{"code":54,"score":22,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[27],{"code":237,"score":22,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[209],[303,304],"农业人工智能 行业标准 立项","智慧农场 数字化场景认证","农业人工智能行业标准立项-4255","2026-10-06T00:04:59.914874Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":8,"content":8,"source_name":312,"source_url":8,"published_at":313,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":384,"slug":387,"view_count":55,"doi":8,"paper":388,"created_at":397},3651,"[预印本] 蒙特卡洛事前评估AI驱动智慧农业平台绿色效益——以海南热带农业为例","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2609.06737v1","以整合大语言模型问答、多模态病虫害诊断、物联网土壤湿度传感、NASA GIBS卫星遥感与闭环田间记录系统的热带农业AI决策平台为对象，构建覆盖农药化肥生产、田间N2O、灌溉电力与稻田CH4的'摇篮到农场大门'农业碳核算模型。在三个海南代表性场景（芒果园、冬季蔬菜、稻\u002F南繁育种田，按面积40%：30%：30%加权）下，通过蒙特卡洛模拟参数不确定性传播：在完全采纳试点情景下，化学农药使用减少23.5%（90%置信区间15.0%–33.2%），化肥施用减少21.0%（13.8%–28.9%），灌溉水减少16.5%（10.9%–23.5%），碳强度降低21.5%（16.1%–27.2%）。达成化肥减量≥15%与碳强度明确下降的概率分别为90.6%与98.1%，而总节水≥20%的概率仅约20%，表明应采用场景化表述。","arXiv 2609.06737v1 (2026-09)","2026-09-15T00:00:00Z",76,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":316},"预印本以蒙特卡洛量化AI平台在海南三类场景的减药减肥与碳减排概率，方法新颖、数据具体，但尚未经同行评审，属细分领域进展。",[318],{"name":312,"url":310},[87,27,320,321,322],"农药减量","农业碳核算","海南热带农业",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":326},{"candidates":327,"agricultural_evidence":48},[328,333,337,343,349,354,358,364,370,374,380],{"code":60,"score":329,"supported":48,"title":330,"abstract":331,"tags":332},9,[87],[],[87],{"code":34,"score":259,"supported":48,"title":334,"abstract":335,"tags":336},[],[106],[320],{"code":338,"score":47,"supported":48,"title":339,"abstract":340,"tags":342},"A1",[],[341],"稻田",[],{"code":344,"score":47,"supported":48,"title":345,"abstract":346,"tags":348},"B1",[],[347],"蔬菜",[],{"code":223,"score":47,"supported":48,"title":350,"abstract":351,"tags":353},[],[352],"芒果",[],{"code":116,"score":47,"supported":48,"title":355,"abstract":356,"tags":357},[],[119],[],{"code":359,"score":47,"supported":48,"title":360,"abstract":361,"tags":363},"H3",[],[362],"灌溉",[],{"code":365,"score":47,"supported":48,"title":366,"abstract":367,"tags":369},"H4",[],[368],"农业碳",[],{"code":130,"score":47,"supported":48,"title":371,"abstract":372,"tags":373},[],[133],[],{"code":375,"score":47,"supported":48,"title":376,"abstract":377,"tags":379},"I5",[],[378],"物联网",[],{"code":54,"score":22,"supported":14,"title":381,"abstract":382,"tags":383},[],[],[27],[385,386],"海南 热带农业 AI平台","蒙特卡洛 农业碳核算","海南热带农业AI平台-3651",{"doi":8,"openalex_id":8,"authors":389,"venue":8,"cited_by_count":156,"oa_url":8,"card":390,"direction":394,"ingested_from":396},[],{"tldr":391,"method":392,"finding":393,"direction":394,"opportunity":395},"用蒙特卡洛模拟评估AI智慧农业平台在海南热带农业中的绿色减排效益。","构建摇篮到农场大门碳核算模型，结合蒙特卡洛模拟参数不确定性。","完全采纳下碳强度降21.5%，化肥减量达标概率90.6%，但总节水≥20%概率仅20%。","农业绿色发展与碳","可延伸研究AI平台在不同作物场景下的节水短板及多目标优化策略。","agent","2026-09-28T00:03:02.301756Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":8,"source_name":404,"source_url":401,"published_at":405,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":406,"sources":408,"tags":410,"primary_topic":415,"secondary_topics":416,"classification":417,"search_phrases":439,"slug":442,"view_count":47,"doi":443,"paper":444,"created_at":466},3460,"Knowledge graph-based expert system for tea pesticide residue compliance verification and detection method selection optimization","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1865601","Introduction Strict Maximum Residue Limits (MRLs) and complex residue definitions in pesticide standards (e.g., GB 2763) pose significant challenges for compliance verification and detection method selection in tea safety management. Manual inspection is prone to systematic errors, particularly false negatives caused by neglecting metabolites or isomers. Methods This study proposes a Knowledge Graph-based Expert System to address these issues. We constructed a domain-specific knowledge graph integrating 110 tea-registered pesticide active substances (comprising 847 entities and 2,156 relationships) and 37 detection standards using a human-in-the-loop verification process. A semantic classification model categorizing residue definitions into six patterns (Type A–F) was developed to enable automated, logic-driven compliance verification. A weighted set-cover algorithm was implemented to optimize the selection of detection methods among existing standardized methods. Validation included a small exploratory pilot user study (n = 5) and a retrospective analysis of 500 historical laboratory samples. Results In the retrospective analysis, the system’s automated verdicts agreed with the laboratory’s human Final Verdict in all 500 cases (95% CI: 99.3%–100.0%), demonstrating faithful reproduction of institutional interpretation of GB 2763. Notably, the system independently flagged three metabolite-summation (Type B) cases that first-level manual review had passed, illustrating its value as a redundant safeguard for multi-component residue definitions. The pilot user study illustrated qualitative trends consistent with cognitive load theory, whereas the system produced deterministic verdicts at a mean of 0.63 s per sample. Discussion This system provides an effective decision-support tool for laboratory quality control and regulatory compliance.","引言 农药标准（如GB 2763）中严格的最高残留限量（MRLs）和复杂的残留物定义，给茶叶安全管理中的合规性验证和检测方法选择带来了重大挑战。人工审查容易产生系统性错误，尤其是因忽视代谢物或异构体而导致的假阴性。方法 本研究提出了一种基于知识图谱的专家系统来解决这些问题。我们采用人在回路验证流程，构建了整合110种茶叶登记农药活性物质（包含847个实体和2，156个关系）及37项检测标准的领域专用知识图谱。开发了将残留物定义归类为六种模式（A型–F型）的语义分类模型，以实现自动化、逻辑驱动的合规性验证。实现了加权集合覆盖算法，以在现有标准化方法中优化检测方法的选择。验证包括一项小型探索性试点用户研究（n = 5）和对500份历史实验室样本的回顾性分析。结果 在回顾性分析中，系统的自动判定在所有500个案例中均与实验室人工最终判定一致（95% CI：99.3%–100.0%），表明系统忠实再现了GB 2763的机构解释。值得注意的是，系统独立标记出3例一级人工审查已通过的代谢物加和（B型）案例，说明其作为多组分残留物定义冗余保障的价值。试点用户研究显示了与认知负荷理论一致的定性趋势，而系统以平均每样本0.63秒的速度产生确定性判定。讨论 该系统为实验室质量控制和法规合规提供了有效的决策支持工具。","Frontiers in Sustainable Food Systems","2026-09-24T00:00:00Z",{"impact":204,"substance":18,"depth":17,"authority":81,"freshness":329,"relevant":22,"comment":407},"知识图谱专家系统用于茶叶农残合规判定与检测方法优选，500例回溯验证一致率100%，方法新颖、数据扎实，对农产品质量安全监管有实用价值。",[409],{"name":404,"url":401},[87,27,411,412,413,414],"知识图谱","农药残留","茶叶安全","检测标准",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":418},{"candidates":419,"agricultural_evidence":48},[420,425,431,435],{"code":34,"score":258,"supported":48,"title":421,"abstract":423,"tags":424},[422],"pesticide",[106,422],[],{"code":426,"score":47,"supported":48,"title":427,"abstract":428,"tags":430},"B3",[],[429],"茶叶",[],{"code":54,"score":55,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[27,411],{"code":60,"score":22,"supported":14,"title":436,"abstract":437,"tags":438},[],[],[87],[440,441],"农药残留 知识图谱","GB 2763 茶叶 检测方法","农药残留知识图谱-3460","10.3389\u002Ffsufs.2026.1865601",{"doi":443,"openalex_id":445,"authors":446,"venue":404,"cited_by_count":156,"oa_url":401,"card":460,"direction":464,"ingested_from":191},"W7214240413",[447,449,451,453,455,457],{"name":448,"orcid":8},"Yanyan Zhang",{"name":450,"orcid":8},"Jianrong Wen",{"name":452,"orcid":8},"Xuexiang Su",{"name":454,"orcid":8},"Tingxin Chen",{"name":456,"orcid":8},"Min Xie",{"name":458,"orcid":459},"Jindong Chen","https:\u002F\u002Forcid.org\u002F0000-0002-5560-1431",{"tldr":461,"method":462,"finding":463,"direction":464,"opportunity":465},"构建知识图谱专家系统，自动核验茶叶农药残留合规并优化检测方法选择。","整合110种农药与37项标准构建知识图谱，用语义分类和加权集合覆盖算法。","500例回溯判定与人工一致，并额外发现3例被人工漏检的代谢物加和案例。","农业人工智能与决策模型","可扩展至其他作物与多残留定义，并探索知识图谱与实验室LIMS实时联动。","2026-09-25T23:30:06.985753Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":8,"content":8,"source_name":472,"source_url":8,"published_at":473,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":474,"sources":476,"tags":478,"primary_topic":482,"secondary_topics":483,"classification":484,"search_phrases":506,"slug":509,"view_count":47,"doi":8,"paper":510,"created_at":517},2734,"面向智慧农业的农业移动平台综述——设计要求、平台类型、应用与未来展望","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260914082445314.htm","综述从设计与操作要求、平台分类、动力总成与移动技术以及农业应用四个互补视角考察农业移动平台。轮式、履带式、腿式及轮腿式、轨道引导或约束运动平台在移动特性和适用操作环境方面进行比较。代表性应用包括作物监测和表型分析、精准作物管理、除草和收获,以及运输和多用途作业。未来发展应强调面向任务的重新配置平台、标准化机械和电气接口、任务级能源管理。","Agriculture (MDPI) 2026年9月14日","2026-09-14T00:00:00Z",{"impact":17,"substance":19,"depth":17,"authority":258,"freshness":82,"relevant":22,"comment":475},"核心期刊综述系统梳理农业移动平台的设计要求、平台类型与应用场景，并提出模块化重构、标准化接口与任务级能源管理等方向，对智慧农业装备研发具有参考价值。",[477],{"name":472,"url":470},[87,479,28,480,481],"农业机器人","自主导航","农业装备",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":485},{"candidates":486,"agricultural_evidence":48},[487,491,496,501],{"code":60,"score":13,"supported":48,"title":488,"abstract":489,"tags":490},[87],[],[87,28],{"code":34,"score":47,"supported":48,"title":492,"abstract":493,"tags":495},[],[494],"除草",[],{"code":497,"score":55,"supported":14,"title":498,"abstract":499,"tags":500},"F5",[],[],[481,480],{"code":502,"score":22,"supported":14,"title":503,"abstract":504,"tags":505},"F6",[],[],[479],[507,508],"农业机器人 农业装备 智慧农业 精准农业","农业机器人 农业装备","农业机器人农业装备智慧农业精准农业-2734",{"doi":8,"openalex_id":8,"authors":511,"venue":8,"cited_by_count":156,"oa_url":8,"card":512,"direction":189,"ingested_from":396},[],{"tldr":513,"method":514,"finding":515,"direction":189,"opportunity":516},"综述农业移动平台的设计要求、平台类型、动力与移动技术及农业应用，并展望未来方向。","文献综述，比较轮式、履带式、腿式等平台及动力总成与移动技术。","未来应发展面向任务可重构平台、标准化机电接口与任务级能源管理。","可重构平台与标准化接口如何适配多任务农田作业，是值得深入的研究空白。","2026-09-17T00:04:40.709195Z"]