[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-99":3,"related-99":55},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":49,"slug":52,"view_count":53,"doi":8,"paper":8,"created_at":54},99,"黑龙江农科院：智慧农业研究持续推进 助力黑土地'智'变","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7667399897556992553\u002F","黑龙江省农业科学院持续推进智慧农业研究,当地'智慧农业1+2+3+10'体系建设32个智慧农场,北林区落地国家级'北斗+智慧农业'试点。绥化'农业托管智慧链平台'入选国家数据局首批50个数字中国建设典型案例,1800多万亩托管土地实现数字精细化管理。",null,"![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F7b55cd347cde46b48d6435e829b72209~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1786061022&x-signature=EQypIptemHqBYKNYdBoS0IRy1ak%3D)![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fab34dbee30ad386bded274dd8618a77f~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1786061022&x-signature=nOnpyOvb%2Bf2cl1NHQ4Z6PWlw42Y%3D)![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fd289dccbde871fd8e7dae6ef9c709fc4~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1786061022&x-signature=sOmDwhFXrIhmoXyI7b0Jt6oqt9s%3D)![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Ffb82098d6fadd6ab64fc42680696c39b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1786061022&x-signature=p7OuLKsw5bfcXOioUBMG8xMhj24%3D)\n□本报记者 宋晨曦\n\n在日前举办的黑龙江省智慧农业培训班上，绥化市、大庆市、黑河市的代表就智慧农场、智慧牧场及智慧渔场建设等分享了经验。培训结束后，记者又到这些地方进行了深入采访，实地了解他们如何做好“智慧+”这篇大文章。\n\n### 田畴有“智” 凭经验转为看数据\n\n在绥化市北林区，稻浪翻滚的田间已不见面朝黄土背朝天的身影，只有无人机低空掠过，其传感器将土壤数据实时回传。\n\n其实早在春耕时节，这里的无人机插秧就给记者留下了深刻印象。当时农技人员介绍，以前插秧全靠农户手眼配合，费工又难控精度，现在无人驾驶插秧机依托北斗导航，能按照预设路径自动行驶作业，株距行距的误差能控制在厘米级，目前全区水田已经实现机械化插秧全覆盖。\n\n绥化市农业农村局局长王宝峰说，全市正按照“1+2+3+10”智慧农业综合管理体系推进建设，目前已建成智慧农（牧、渔）场32个；北林区也已落地国家级“北斗+智慧农业”试点，将支撑玉米大面积单产提升。\n\n更新鲜的是AI激光除草机器人的应用。今年，绥化在全省率先启动这项试验示范，6个县（市、区）超过6000亩的有机鲜食玉米、有机黑豆等作物田里，激光除草机器人代替了传统人工和化学除草。“以往人工除草效率低、成本高，现在用激光除草不伤作物，也没有农药残留。”农民张军说。\n\n在绥化市肇东市五里明农场，旱田智能灌溉基地采用了无膜浅埋滴灌水肥一体化技术，物联网水肥一体机根据田间墒情自动调配水肥。基地负责人算了一笔账：节水30%、节肥23%，抗旱保苗效果明显。这套技术已成为绥化市粮食单产提升的关键路径。\n\n值得一提的是，绥化市“农业托管智慧链平台”入选了国家数据局首批50个数字中国建设典型案例。该平台基于云服务架构，将政府监管、托管组织经营、农户查询、金融机构风控等功能纳入一个系统。记者现场扫码发现，农户可随时查看托管服务进度，收粮企业也能参与农田管理。目前，1800多万亩托管土地，正在通过数字手段实现精细化管理。\n\n### 牧场有“数” 精准把握各个环节\n\n作为全省畜牧大市，大庆市畜牧业产值占农林牧渔总产值的42.3%，占比居全省首位，“智慧”早已深度融入畜牧产业链的各个环节。\n\n在林甸县，全省唯一的县级奶牛数字化管理平台已经建成投用。全县17处规模牧场5.9万头奶牛接入“一牧云”和阿菲金智能系统，每头奶牛佩戴智能耳标，发情、产奶、采食、健康状况实时回传。以往三个人管理的牧场，现在一个人就能完成日常运维，人工成本下降30%，奶牛年均单产从8吨提升到10吨以上。更让大庆人自豪的是，全省19家跻身全国荷斯坦12吨高产俱乐部的牧场中，大庆占了7家。\n\n在杜尔伯特蒙古族自治县，2.8万头高端和牛和良种肉牛领到了“电子护照”。当地搭建的“S+A”数字化管理体系，能实现从配种、育肥到屠宰的全流程追溯。这套平台还打通了与金融、保险机构的数据对接，可通过健康数据提前预判疫病风险，累计已帮助养殖户挽回损失184万元，肉牛交易溢价率可达26%。\n\n智慧养殖不止于数据采集，更在于精准执行。在谷实牧业、双胞胎牧业等龙头企业的养猪场，全自动清洁限饲系统、空气过滤智能环控设备一应俱全，64套料比智能测定站实时分析采食量和增重数据，AI自动匹配营养配方，母猪产仔成活率提升5%以上。\n\n祖代、父母代、商品代三级智能繁育场形成完整籽鹅繁育体系，年孵化能力710万只，“肇源白鹅”获国家地理标志产品认证，“鹅香久”品牌入选“黑土优品”。据悉，肇源已与国内禽肉龙头企业江苏益客集团签约，白鹅产业链将从养殖延伸至屠宰、熟食加工、鹅绒制造，全链条数字管控正加速落地。\n\n### 基地加“芯” 突破寒地“天花板”\n\n来到佳木斯抚远市乌苏现代农业产业园，走进8万平方米的智能温室，眼前景象与窗外的东北原野恍如两个世界。荷兰进口的豪根道智能中控系统实时监测温度、湿度、光照、二氧化碳浓度等数十项数据，自动调控天窗、幕布、灌溉。立体吊架无土栽培系统让空间利用率最大化，水肥一体化循环系统节水率高达95%。\n\n产业园负责人告诉记者，这里产出的每一颗番茄都可保证零农残、零激素、零转基因，物理与生物双重绿色防控体系代替了化学农药。“我们不是靠天吃饭，是靠数据种地。”在自动化分选包装线上，除了人工采摘，分选、质检、称重、贴标、码垛全部由机器完成，日包装产能10吨，产品全部符合全球良好农业规范。这个项目总投资3.53亿元，旨在打造智能化、数字化、集约化、绿色化的智慧设施农业标杆。\n\n在黑河市爱辉区西岗子镇盘肠沟寒地小龙虾养殖基地，物联网智慧管控同样让人眼前一亮。21口池塘全部布设水质传感器、自动增氧设备和远程投料终端，水体溶氧低于5毫克\u002F升自动开启增氧，高于7毫克\u002F升自动关停。这套系统让饵料损耗下降30%，用电节约30%以上，小龙虾生长速度提升5%~10%，100亩水面每年节省生产成本4万余元。\n\n黑河市农业农村局相关负责人介绍，基地通过菌藻共生生态养殖和智能化育苗攻关，攻克了寒地虾苗越冬难题。今年全市小龙虾养殖面积发展到1091亩，投放虾苗1000万尾，盘肠沟基地年产商品虾20吨，已签订10吨订单销往北京等高端市场。在黑河，冷水鱼精深加工也有了突破——总投资1亿元的浔然谷冷水鱼加工厂正在瑷珲对俄进出口加工基地建设，投产后每年可加工冷水鱼2000吨。\n\n**记者手记**\n\n如今，黑龙江的智慧农业探索正从“盆景”迈向“风景”。记者一路采访深有体会：数字技术早已不是展示台上的抽象概念，而是实实在在落地到了田垄、圈舍和温室中——降了成本、提了产量、增了效益。\n\n当然，推进过程中挑战仍然存在：基层技术人员短缺、部分智能设备后期维护成本偏高、小型农户接受度有限等，仍是智慧农业进一步普及需要突破的瓶颈。但正如一位基层农业干部所说：“智慧农业不是选择题，而是必答题。”在保障国家粮食安全、推进农业现代化的大背景下，黑龙江这片黑土地上的“智慧革命”，才刚刚拉开大幕。\n\n本文图片均由受访者提供","黑龙江新闻网 2026-07-28","2026-07-28T02:27:00Z","报道",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},20,18,16,6,1,"省级官媒报道黑龙江智慧农业实践，涵盖多地案例与数据，内容详实，但时效性稍差，适合精选。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","黑龙江","农科院","托管平台",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[26],[26],[26],[50,51],"托管平台 智慧农业 农科院 黑龙江","托管平台 智慧农业","托管平台智慧农业农科院黑龙江-99",11,"2026-07-31T00:00:54.873505Z",{"total":20,"page":21,"page_size":20,"items":56},[57,117,177,223,263,323],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":8,"content":8,"source_name":62,"source_url":60,"published_at":63,"category":64,"cover_url":8,"hotness":13,"is_selected":14,"score":65,"score_detail":66,"sources":70,"tags":72,"primary_topic":77,"secondary_topics":78,"classification":79,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":116},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z","论文",79,{"impact":19,"substance":67,"depth":18,"authority":68,"freshness":13,"relevant":21,"comment":69},21,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[71],{"name":62,"url":60},[26,73,74,75,76],"农业人工智能","精准农业","机器视觉","作物行检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":80},{"candidates":81,"agricultural_evidence":45},[82,88],{"code":31,"score":83,"supported":45,"title":84,"abstract":85,"tags":87},5,[],[86],"precision agriculture",[26,74],{"code":89,"score":90,"supported":14,"title":91,"abstract":92,"tags":93},"I2",2,[],[],[73,75],[95,96],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539",0,"10.1007\u002Fs11119-026-10473-z",{"doi":99,"openalex_id":101,"authors":102,"venue":62,"cited_by_count":98,"oa_url":8,"card":8,"direction":8,"ingested_from":115},"W7223117828",[103,106,109,112],{"name":104,"orcid":105},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":107,"orcid":108},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":110,"orcid":111},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":113,"orcid":114},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":8,"source_name":123,"source_url":120,"published_at":124,"category":64,"cover_url":8,"hotness":125,"is_selected":14,"score":126,"score_detail":127,"sources":131,"tags":135,"primary_topic":140,"secondary_topics":141,"classification":142,"search_phrases":159,"slug":162,"view_count":98,"doi":163,"paper":164,"created_at":176},5397,"The Relationship Between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23469780","Relationship between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study explores the relationship between nutrient concentrations and the optical characteristics of banana leaves using quantitative image analysis. The study focuses on measurable leaf features, including colour intensity, RGB values, and other image-derived parameters, to identify patterns associated with plant nutrient status. Digital images of banana leaves can provide valuable information about variations in leaf appearance that may reflect differences in nutrient availability. By analysing these characteristics alongside nutrient concentration measurements, the study aims to establish quantitative relationships that can support plant nutrient assessment. This approach may contribute to the development of cost-effective, non-destructive, and image-based techniques for monitoring banana plant health. The findings can be useful for precision agriculture, crop management, and future research on digital image processing for agricultural applications.","香蕉叶片养分浓度与可测量光学特征之间的关系：一项基于定量图像的研究探讨了利用定量图像分析研究香蕉叶片养分浓度与光学特征之间的关系。该研究聚焦于可测量的叶片特征，包括颜色强度、RGB值及其他图像衍生参数，以识别与植物养分状况相关的模式。香蕉叶片的数字图像可提供关于叶片外观变化的有价值信息，这些变化可能反映养分有效性的差异。通过将这些特征与养分浓度测量结果一并分析，该研究旨在建立可支持植物养分评估的定量关系。该方法可能有助于开发低成本、非破坏性且基于图像的香蕉植株健康监测技术。研究结果可用于精准农业、作物管理以及未来农业应用数字图像处理的研究。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",25,66,{"impact":44,"substance":18,"depth":128,"authority":44,"freshness":129,"relevant":21,"comment":130},15,9,"该研究基于图像定量分析香蕉叶片营养状况，方法新颖且面向无损监测，对精准农业有参考价值，但属单作物探索性研究，影响范围有限。",[132,133],{"name":123,"url":120},{"name":123,"url":134},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[26,136,137,138,139],"图像分析","香蕉","无损监测","作物营养",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":143},{"candidates":144,"agricultural_evidence":45},[145,150,155],{"code":31,"score":146,"supported":45,"title":147,"abstract":148,"tags":149},7,[],[74,86],[26],{"code":151,"score":21,"supported":14,"title":152,"abstract":153,"tags":154},"B2",[],[],[137],{"code":89,"score":21,"supported":14,"title":156,"abstract":157,"tags":158},[],[],[136],[160,161],"香蕉叶片 营养浓度 图像分析","香蕉 无损监测 精准农业","香蕉叶片营养浓度图像分析-5397","10.5281\u002Fzenodo.23469780",{"doi":163,"openalex_id":165,"authors":166,"venue":123,"cited_by_count":98,"oa_url":120,"card":169,"direction":175,"ingested_from":115},"W7222941290",[167],{"name":168,"orcid":8},"Anantjit Publication",{"tldr":170,"method":171,"finding":172,"direction":173,"opportunity":174},"用定量图像分析探究香蕉叶片养分浓度与可测光学特征的关系。","采集香蕉叶片数字图像，提取颜色强度、RGB值等参数，与养分浓度做定量关联分析。","叶片图像光学特征与养分浓度存在可量化的关联，可支撑无损养分评估。","农业遥感与作物表型","可扩展到多品种、多生育期与田间光照校正，并构建图像-养分反演模型与便携诊断工具。","数字乡村与农业信息化","2026-10-20T23:30:22.619634Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":8,"content":8,"source_name":182,"source_url":180,"published_at":124,"category":64,"cover_url":8,"hotness":125,"is_selected":14,"score":183,"score_detail":184,"sources":187,"tags":191,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":205,"slug":208,"view_count":98,"doi":209,"paper":210,"created_at":222},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex",37,{"impact":185,"substance":20,"depth":185,"authority":20,"freshness":129,"relevant":21,"comment":186},8,"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[188,189],{"name":182,"url":180},{"name":182,"url":190},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[192,26,193,194],"数字农业","农业信息化","中非",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":198},{"candidates":199,"agricultural_evidence":45},[200],{"code":31,"score":68,"supported":45,"title":201,"abstract":203,"tags":204},[202],"digital agriculture",[202],[193,192,26],[206,207],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":209,"openalex_id":211,"authors":212,"venue":8,"cited_by_count":98,"oa_url":180,"card":8,"direction":175,"ingested_from":115},"W7222930115",[213,215,217,220],{"name":214,"orcid":8},"Anani Ogou",{"name":216,"orcid":8},"Henry Chotangui",{"name":218,"orcid":219},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":221,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":8,"content":8,"source_name":182,"source_url":226,"published_at":124,"category":64,"cover_url":8,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":231,"tags":233,"primary_topic":236,"secondary_topics":237,"classification":238,"search_phrases":251,"slug":254,"view_count":98,"doi":255,"paper":256,"created_at":262},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex",56,{"impact":18,"substance":185,"depth":13,"authority":44,"freshness":185,"relevant":21,"comment":230},"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[232],{"name":182,"url":226},[26,234,74,235],"农业机器人","智能采收",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":239},{"candidates":240,"agricultural_evidence":45},[241,246],{"code":31,"score":242,"supported":45,"title":243,"abstract":244,"tags":245},13,[86],[86],[26,74],{"code":247,"score":21,"supported":14,"title":248,"abstract":249,"tags":250},"F6",[],[],[234],[252,253],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":255,"openalex_id":257,"authors":258,"venue":8,"cited_by_count":98,"oa_url":226,"card":8,"direction":261,"ingested_from":115},"W7222971426",[259],{"name":260,"orcid":8},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":8,"source_name":269,"source_url":266,"published_at":124,"category":64,"cover_url":8,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":275,"tags":277,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":292,"slug":295,"view_count":98,"doi":296,"paper":297,"created_at":322},5375,"Triboelectric Nanogenerator Based on All‐Cellulose Triboelectric Layer for Droplet Energy Harvesting and Self‐Powered Agriculture Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsmll.76354","Cellulose-based triboelectric nanogenerators (TENGs) attract increasing attention; however, developing high-performance all-cellulose-based triboelectric layer TENGs remains challenging. In this study, a regenerated cellulose nanofiber film (R-CNF) is used as a substrate, and an aminosilane intermediate layer is introduced. By utilizing the silanol groups on the silane chains as compatible reactive sites for fluorination grafting, and simultaneously enhancing the bonding strength between modified layers through N-H···F-C hydrogen-bond interactions between amino and fluorine groups, a fluorinated regenerated cellulose nanofiber film (F-R-CNF) is successfully prepared. The degradation rate of F-R-CNF within 13 days is approximately 0.96%. When assembled into a TENG with an ethyl cellulose nanofiber film(ECNF), the output voltage of the resulting TENG is ten times higher than that of the R-CNF-based TENGs, while maintaining 85% of its output performance at 80% relative humidity. Furthermore, a droplet energy harvesting TENG (DE-TENG) is constructed to harvest energy from falling water droplets, demonstrating the potential of the DE-TENG to power agricultural micro-sensors. This study provides a feasible strategy for designing high-performance all-cellulose-based triboelectric layer triboelectric nanogenerators and reveals the great potential of F-R-CNF for self-powered devices and smart agriculture applications.","纤维素基摩擦纳米发电机（TENGs）日益受到关注，然而开发高性能全纤维素基摩擦电层TENG仍面临挑战。本研究以再生纤维素纳米纤维膜（R-CNF）为基底，引入氨基硅烷中间层，利用硅烷链上的硅醇基团作为氟化接枝的相容反应位点，同时通过氨基与含氟基团之间的N-H···F-C氢键相互作用增强改性层间的结合强度，成功制备了氟化再生纤维素纳米纤维膜（F-R-CNF）。F-R-CNF在13天内的降解率约为0.96%。将其与乙基纤维素纳米纤维膜（ECNF）组装成TENG后，所得TENG的输出电压比基于R-CNF的TENG高十倍，同时在80%相对湿度下仍保持85%的输出性能。此外，构建了液滴能量收集TENG（DE-TENG）以收集下落水滴的能量，展示了DE-TENG为农业微传感器供电的潜力。本研究为设计高性能全纤维素基摩擦电层摩擦纳米发电机提供了可行策略，并揭示了F-R-CNF在自供电器件和智慧农业应用中的巨大潜力。","Small",81,{"impact":18,"substance":272,"depth":273,"authority":68,"freshness":185,"relevant":21,"comment":274},22,19,"全纤维素摩擦纳米发电机实现液滴能量收集并驱动农业微传感器，方法新颖、数据扎实，属智慧农业自供电传感的实质进展，值得入选。",[276],{"name":269,"url":266},[26,278,279,280,281],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":285},{"candidates":286,"agricultural_evidence":45},[287],{"code":31,"score":146,"supported":45,"title":288,"abstract":289,"tags":291},[],[26,290],"smart agriculture",[26],[293,294],"全纤维素 摩擦纳米发电机 液滴能量收集","F-R-CNF 自供电 农业监测","全纤维素摩擦纳米发电机液滴能量收集-5375","10.1002\u002Fsmll.76354",{"doi":296,"openalex_id":298,"authors":299,"venue":269,"cited_by_count":98,"oa_url":8,"card":317,"direction":261,"ingested_from":115},"W7222917387",[300,302,305,307,309,312,314],{"name":301,"orcid":8},"Hengwei Xiong",{"name":303,"orcid":304},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":306,"orcid":8},"Weijie Li",{"name":308,"orcid":8},"Kangwen Shi",{"name":310,"orcid":311},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":313,"orcid":8},"Wenpeng Fan",{"name":315,"orcid":316},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":318,"method":319,"finding":320,"direction":261,"opportunity":321},"制备全纤维素摩擦电层TENG，收集水滴能量为农业微传感器供电。","再生纤维素纳米纤维膜经氨基硅烷中间层氟化接枝，与乙基纤维素膜组装TENG。","输出达R-CNF基TENG的10倍，80%湿度下保持85%性能，13天降解约0.96%。","可探索全纤维素TENG在田间高湿环境下的长期稳定性及与无线传感节点的集成供电。","2026-10-20T23:30:08.252085Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":8,"source_name":123,"source_url":326,"published_at":124,"category":64,"cover_url":8,"hotness":13,"is_selected":14,"score":65,"score_detail":329,"sources":331,"tags":333,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":350,"slug":353,"view_count":98,"doi":354,"paper":355,"created_at":365},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。",{"impact":18,"substance":272,"depth":18,"authority":44,"freshness":129,"relevant":21,"comment":330},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[332],{"name":123,"url":326},[26,73,334,74,335],"数字孪生","植物表型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":339},{"candidates":340,"agricultural_evidence":45},[341,346],{"code":31,"score":17,"supported":45,"title":342,"abstract":344,"tags":345},[343],"digital twin",[74,334,86],[334,26,74],{"code":89,"score":21,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[73],[351,352],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":354,"openalex_id":356,"authors":357,"venue":123,"cited_by_count":98,"oa_url":326,"card":360,"direction":173,"ingested_from":115},"W7222885652",[358],{"name":359,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":361,"method":362,"finding":363,"direction":173,"opportunity":364},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z"]